From 8454a103acbc58c7da405b1b5c8473623eb05613 Mon Sep 17 00:00:00 2001 From: Alexandra Settle Date: Tue, 8 Aug 2017 11:42:07 +0100 Subject: [PATCH] [arch-guide-archive] Removing old arch guide from master This still exists in the before-migration tag. Removing from master as no longer required. 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old arch design, will be archived - ["arch-design-to-archive"]="skip" ) diff --git a/doc/arch-design-to-archive/setup.cfg b/doc/arch-design-to-archive/setup.cfg deleted file mode 100644 index bbbc2e035e..0000000000 --- a/doc/arch-design-to-archive/setup.cfg +++ /dev/null @@ -1,27 +0,0 @@ -[metadata] -name = architecturedesignguide -summary = OpenStack Architecture Design Guide -author = OpenStack -author-email = openstack-docs@lists.openstack.org -home-page = https://docs.openstack.org/ -classifier = -Environment :: OpenStack -Intended Audience :: Information Technology -Intended Audience :: Cloud Architects -License :: OSI Approved :: Apache Software License -Operating System :: POSIX :: Linux -Topic :: Documentation - -[global] -setup-hooks = - pbr.hooks.setup_hook - -[files] - -[build_sphinx] -warning-is-error = 1 -build-dir = build -source-dir = source - -[wheel] -universal = 1 diff --git a/doc/arch-design-to-archive/setup.py b/doc/arch-design-to-archive/setup.py deleted file mode 100644 index 736375744d..0000000000 --- a/doc/arch-design-to-archive/setup.py +++ /dev/null @@ -1,30 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) 2013 Hewlett-Packard Development Company, L.P. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or -# implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# THIS FILE IS MANAGED BY THE GLOBAL REQUIREMENTS REPO - DO NOT EDIT -import setuptools - -# In python < 2.7.4, a lazy loading of package `pbr` will break -# setuptools if some other modules registered functions in `atexit`. -# solution from: http://bugs.python.org/issue15881#msg170215 -try: - import multiprocessing # noqa -except ImportError: - pass - -setuptools.setup( - setup_requires=['pbr'], - pbr=True) diff --git a/doc/arch-design-to-archive/source/common b/doc/arch-design-to-archive/source/common deleted file mode 120000 index dc879abe93..0000000000 --- a/doc/arch-design-to-archive/source/common +++ /dev/null @@ -1 +0,0 @@ -../../common \ No newline at end of file diff --git a/doc/arch-design-to-archive/source/compute-focus-architecture.rst b/doc/arch-design-to-archive/source/compute-focus-architecture.rst deleted file mode 100644 index 1c11bc485c..0000000000 --- a/doc/arch-design-to-archive/source/compute-focus-architecture.rst +++ /dev/null @@ -1,212 +0,0 @@ -============ -Architecture -============ -The hardware selection covers three areas: - -* Compute - -* Network - -* Storage - -Compute-focused OpenStack clouds have high demands on processor and -memory resources, and requires hardware that can handle these demands. -Consider the following factors when selecting compute (server) hardware: - -* Server density - -* Resource capacity - -* Expandability - -* Cost - -Weigh these considerations against each other to determine the best -design for the desired purpose. For example, increasing server density -means sacrificing resource capacity or expandability. - -A compute-focused cloud should have an emphasis on server hardware that -can offer more CPU sockets, more CPU cores, and more RAM. Network -connectivity and storage capacity are less critical. - -When designing a compute-focused OpenStack architecture, you must -consider whether you intend to scale up or scale out. Selecting a -smaller number of larger hosts, or a larger number of smaller hosts, -depends on a combination of factors: cost, power, cooling, physical rack -and floor space, support-warranty, and manageability. - -Considerations for selecting hardware: - -* Most blade servers can support dual-socket multi-core CPUs. To avoid - this CPU limit, select ``full width`` or ``full height`` blades. Be - aware, however, that this also decreases server density. For example, - high density blade servers such as HP BladeSystem or Dell PowerEdge - M1000e support up to 16 servers in only ten rack units. Using - half-height blades is twice as dense as using full-height blades, - which results in only eight servers per ten rack units. - -* 1U rack-mounted servers that occupy only a single rack unit may offer - greater server density than a blade server solution. It is possible - to place forty 1U servers in a rack, providing space for the top of - rack (ToR) switches, compared to 32 full width blade servers. - -* 2U rack-mounted servers provide quad-socket, multi-core CPU support, - but with a corresponding decrease in server density (half the density - that 1U rack-mounted servers offer). - -* Larger rack-mounted servers, such as 4U servers, often provide even - greater CPU capacity, commonly supporting four or even eight CPU - sockets. These servers have greater expandability, but such servers - have much lower server density and are often more expensive. - -* ``Sled servers`` are rack-mounted servers that support multiple - independent servers in a single 2U or 3U enclosure. These deliver - higher density as compared to typical 1U or 2U rack-mounted servers. - For example, many sled servers offer four independent dual-socket - nodes in 2U for a total of eight CPU sockets in 2U. - -Consider these when choosing server hardware for a compute-focused -OpenStack design architecture: - -* Instance density - -* Host density - -* Power and cooling density - -Selecting networking hardware -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Some of the key considerations for networking hardware selection -include: - -* Port count - -* Port density - -* Port speed - -* Redundancy - -* Power requirements - -We recommend designing the network architecture using a scalable network -model that makes it easy to add capacity and bandwidth. A good example -of such a model is the leaf-spline model. In this type of network -design, it is possible to easily add additional bandwidth as well as -scale out to additional racks of gear. It is important to select network -hardware that supports the required port count, port speed, and port -density while also allowing for future growth as workload demands -increase. It is also important to evaluate where in the network -architecture it is valuable to provide redundancy. - -Operating system and hypervisor -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The selection of operating system (OS) and hypervisor has a significant -impact on the end point design. - -OS and hypervisor selection impact the following areas: - -* Cost - -* Supportability - -* Management tools - -* Scale and performance - -* Security - -* Supported features - -* Interoperability - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -The selection of OpenStack components is important. There are certain -components that are required, for example the compute and image -services, but others, such as the Orchestration service, may not be -present. - -For a compute-focused OpenStack design architecture, the following -components may be present: - -* Identity (keystone) - -* Dashboard (horizon) - -* Compute (nova) - -* Object Storage (swift) - -* Image (glance) - -* Networking (neutron) - -* Orchestration (heat) - - .. note:: - - A compute-focused design is less likely to include OpenStack Block - Storage. However, there may be some situations where the need for - performance requires a block storage component to improve data I-O. - -The exclusion of certain OpenStack components might also limit the -functionality of other components. If a design includes the -Orchestration service but excludes the Telemetry service, then the -design cannot take advantage of Orchestration's auto scaling -functionality as this relies on information from Telemetry. - -Networking software -~~~~~~~~~~~~~~~~~~~ - -OpenStack Networking provides a wide variety of networking services for -instances. There are many additional networking software packages that -might be useful to manage the OpenStack components themselves. The -`OpenStack High Availability Guide `_ -describes some of these software packages in more detail. - -For a compute-focused OpenStack cloud, the OpenStack infrastructure -components must be highly available. If the design does not include -hardware load balancing, you must add networking software packages, for -example, HAProxy. - -Management software -~~~~~~~~~~~~~~~~~~~ - -The selected supplemental software solution impacts and affects the -overall OpenStack cloud design. This includes software for providing -clustering, logging, monitoring and alerting. - -The availability of design requirements is the main determiner for the -inclusion of clustering software, such as Corosync or Pacemaker. - -Operational considerations determine the requirements for logging, -monitoring, and alerting. Each of these sub-categories include various -options. - -Some other potential design impacts include: - -OS-hypervisor combination - Ensure that the selected logging, monitoring, or alerting tools - support the proposed OS-hypervisor combination. - -Network hardware - The logging, monitoring, and alerting software must support the - network hardware selection. - -Database software -~~~~~~~~~~~~~~~~~ - -A large majority of OpenStack components require access to back-end -database services to store state and configuration information. Select -an appropriate back-end database that satisfies the availability and -fault tolerance requirements of the OpenStack services. OpenStack -services support connecting to any database that the SQLAlchemy Python -drivers support, however most common database deployments make use of -MySQL or some variation of it. We recommend that you make the database -that provides back-end services within a general-purpose cloud highly -available. Some of the more common software solutions include Galera, -MariaDB, and MySQL with multi-master replication. diff --git a/doc/arch-design-to-archive/source/compute-focus-operational-considerations.rst b/doc/arch-design-to-archive/source/compute-focus-operational-considerations.rst deleted file mode 100644 index 055a469353..0000000000 --- a/doc/arch-design-to-archive/source/compute-focus-operational-considerations.rst +++ /dev/null @@ -1,68 +0,0 @@ -========================== -Operational considerations -========================== - -There are a number of operational considerations that affect the design -of compute-focused OpenStack clouds, including: - -* Enforcing strict API availability requirements - -* Understanding and dealing with failure scenarios - -* Managing host maintenance schedules - -Service-level agreements (SLAs) are contractual obligations that ensure -the availability of a service. When designing an OpenStack cloud, -factoring in promises of availability implies a certain level of -redundancy and resiliency. - -Monitoring -~~~~~~~~~~ - -OpenStack clouds require appropriate monitoring platforms to catch and -manage errors. - -.. note:: - - We recommend leveraging existing monitoring systems to see if they - are able to effectively monitor an OpenStack environment. - -Specific meters that are critically important to capture include: - -* Image disk utilization - -* Response time to the Compute API - -Capacity planning -~~~~~~~~~~~~~~~~~ - -Adding extra capacity to an OpenStack cloud is a horizontally scaling -process. - -We recommend similar (or the same) CPUs when adding extra nodes to the -environment. This reduces the chance of breaking live-migration features -if they are present. Scaling out hypervisor hosts also has a direct -effect on network and other data center resources. We recommend you -factor in this increase when reaching rack capacity or when requiring -extra network switches. - -Changing the internal components of a Compute host to account for -increases in demand is a process known as vertical scaling. Swapping a -CPU for one with more cores, or increasing the memory in a server, can -help add extra capacity for running applications. - -Another option is to assess the average workloads and increase the -number of instances that can run within the compute environment by -adjusting the overcommit ratio. - -.. note:: - - It is important to remember that changing the CPU overcommit ratio - can have a detrimental effect and cause a potential increase in a - noisy neighbor. - -The added risk of increasing the overcommit ratio is that more instances -fail when a compute host fails. We do not recommend that you increase -the CPU overcommit ratio in compute-focused OpenStack design -architecture, as it can increase the potential for noisy neighbor -issues. diff --git a/doc/arch-design-to-archive/source/compute-focus-prescriptive-examples.rst b/doc/arch-design-to-archive/source/compute-focus-prescriptive-examples.rst deleted file mode 100644 index c8595c8f51..0000000000 --- a/doc/arch-design-to-archive/source/compute-focus-prescriptive-examples.rst +++ /dev/null @@ -1,126 +0,0 @@ -===================== -Prescriptive examples -===================== - -The Conseil Européen pour la Recherche Nucléaire (CERN), also known as -the European Organization for Nuclear Research, provides particle -accelerators and other infrastructure for high-energy physics research. - -As of 2011 CERN operated these two compute centers in Europe with plans -to add a third. - -+-----------------------+------------------------+ -| Data center | Approximate capacity | -+=======================+========================+ -| Geneva, Switzerland | - 3.5 Mega Watts | -| | | -| | - 91000 cores | -| | | -| | - 120 PB HDD | -| | | -| | - 100 PB Tape | -| | | -| | - 310 TB Memory | -+-----------------------+------------------------+ -| Budapest, Hungary | - 2.5 Mega Watts | -| | | -| | - 20000 cores | -| | | -| | - 6 PB HDD | -+-----------------------+------------------------+ - -To support a growing number of compute-heavy users of experiments -related to the Large Hadron Collider (LHC), CERN ultimately elected to -deploy an OpenStack cloud using Scientific Linux and RDO. This effort -aimed to simplify the management of the center's compute resources with -a view to doubling compute capacity through the addition of a data -center in 2013 while maintaining the same levels of compute staff. - -The CERN solution uses :term:`cells ` for segregation of compute -resources and for transparently scaling between different data centers. -This decision meant trading off support for security groups and live -migration. In addition, they must manually replicate some details, like -flavors, across cells. In spite of these drawbacks cells provide the -required scale while exposing a single public API endpoint to users. - -CERN created a compute cell for each of the two original data centers -and created a third when it added a new data center in 2013. Each cell -contains three availability zones to further segregate compute resources -and at least three RabbitMQ message brokers configured for clustering -with mirrored queues for high availability. - -The API cell, which resides behind a HAProxy load balancer, is in the -data center in Switzerland and directs API calls to compute cells using -a customized variation of the cell scheduler. The customizations allow -certain workloads to route to a specific data center or all data -centers, with cell RAM availability determining cell selection in the -latter case. - -.. figure:: figures/Generic_CERN_Example.png - -There is also some customization of the filter scheduler that handles -placement within the cells: - -ImagePropertiesFilter - Provides special handling depending on the guest operating system in - use (Linux-based or Windows-based). - -ProjectsToAggregateFilter - Provides special handling depending on which project the instance is - associated with. - -default_schedule_zones - Allows the selection of multiple default availability zones, rather - than a single default. - -A central database team manages the MySQL database server in each cell -in an active/passive configuration with a NetApp storage back end. -Backups run every 6 hours. - -Network architecture -~~~~~~~~~~~~~~~~~~~~ - -To integrate with existing networking infrastructure, CERN made -customizations to legacy networking (nova-network). This was in the form -of a driver to integrate with CERN's existing database for tracking MAC -and IP address assignments. - -The driver facilitates selection of a MAC address and IP for new -instances based on the compute node where the scheduler places the -instance. - -The driver considers the compute node where the scheduler placed an -instance and selects a MAC address and IP from the pre-registered list -associated with that node in the database. The database updates to -reflect the address assignment to that instance. - -Storage architecture -~~~~~~~~~~~~~~~~~~~~ - -CERN deploys the OpenStack Image service in the API cell and configures -it to expose version 1 (V1) of the API. This also requires the image -registry. The storage back end in use is a 3 PB Ceph cluster. - -CERN maintains a small set of Scientific Linux 5 and 6 images onto which -orchestration tools can place applications. Puppet manages instance -configuration and customization. - -Monitoring -~~~~~~~~~~ - -CERN does not require direct billing, but uses the Telemetry service to -perform metering for the purposes of adjusting project quotas. CERN uses -a sharded, replicated, MongoDB back-end. To spread API load, CERN -deploys instances of the nova-api service within the child cells for -Telemetry to query against. This also requires the configuration of -supporting services such as keystone, glance-api, and glance-registry in -the child cells. - -.. figure:: figures/Generic_CERN_Architecture.png - -Additional monitoring tools in use include -`Flume `__, `Elastic -Search `__, -`Kibana `__, and the CERN -developed `Lemon `__ -project. diff --git a/doc/arch-design-to-archive/source/compute-focus-technical-considerations.rst b/doc/arch-design-to-archive/source/compute-focus-technical-considerations.rst deleted file mode 100644 index 53e3dcdd7e..0000000000 --- a/doc/arch-design-to-archive/source/compute-focus-technical-considerations.rst +++ /dev/null @@ -1,214 +0,0 @@ -======================== -Technical considerations -======================== - -In a compute-focused OpenStack cloud, the type of instance workloads you -provision heavily influences technical decision making. - -Public and private clouds require deterministic capacity planning to -support elastic growth in order to meet user SLA expectations. -Deterministic capacity planning is the path to predicting the effort and -expense of making a given process perform consistently. This process is -important because, when a service becomes a critical part of a user's -infrastructure, the user's experience links directly to the SLAs of the -cloud itself. - -There are two aspects of capacity planning to consider: - -* Planning the initial deployment footprint - -* Planning expansion of the environment to stay ahead of cloud user demands - -Begin planning an initial OpenStack deployment footprint with -estimations of expected uptake, and existing infrastructure workloads. - -The starting point is the core count of the cloud. By applying relevant -ratios, the user can gather information about: - -* The number of expected concurrent instances: (overcommit fraction × - cores) / virtual cores per instance - -* Required storage: flavor disk size × number of instances - -These ratios determine the amount of additional infrastructure needed to -support the cloud. For example, consider a situation in which you -require 1600 instances, each with 2 vCPU and 50 GB of storage. Assuming -the default overcommit rate of 16:1, working out the math provides an -equation of: - -* 1600 = (16 × (number of physical cores)) / 2 - -* Storage required = 50 GB × 1600 - -On the surface, the equations reveal the need for 200 physical cores and -80 TB of storage for ``/var/lib/nova/instances/``. However, it is also -important to look at patterns of usage to estimate the load that the API -services, database servers, and queue servers are likely to encounter. - -Aside from the creation and termination of instances, consider the -impact of users accessing the service, particularly on nova-api and its -associated database. Listing instances gathers a great deal of -information and given the frequency with which users run this operation, -a cloud with a large number of users can increase the load -significantly. This can even occur unintentionally. For example, the -OpenStack Dashboard instances tab refreshes the list of instances every -30 seconds, so leaving it open in a browser window can cause unexpected -load. - -Consideration of these factors can help determine how many cloud -controller cores you require. A server with 8 CPU cores and 8 GB of RAM -server would be sufficient for a rack of compute nodes, given the above -caveats. - -Key hardware specifications are also crucial to the performance of user -instances. Be sure to consider budget and performance needs, including -storage performance (spindles/core), memory availability (RAM/core), -network bandwidth (Gbps/core), and overall CPU performance (CPU/core). - -The cloud resource calculator is a useful tool in examining the impacts -of different hardware and instance load outs. See `cloud-resource-calculator -`_. - -Expansion planning -~~~~~~~~~~~~~~~~~~ - -A key challenge for planning the expansion of cloud compute services is -the elastic nature of cloud infrastructure demands. - -Planning for expansion is a balancing act. Planning too conservatively -can lead to unexpected oversubscription of the cloud and dissatisfied -users. Planning for cloud expansion too aggressively can lead to -unexpected underuse of the cloud and funds spent unnecessarily -on operating infrastructure. - -The key is to carefully monitor the trends in cloud usage over time. The -intent is to measure the consistency with which you deliver services, -not the average speed or capacity of the cloud. Using this information -to model capacity performance enables users to more accurately determine -the current and future capacity of the cloud. - -CPU and RAM -~~~~~~~~~~~ - -OpenStack enables users to overcommit CPU and RAM on compute nodes. This -allows an increase in the number of instances running on the cloud at -the cost of reducing the performance of the instances. OpenStack Compute -uses the following ratios by default: - -* CPU allocation ratio: 16:1 - -* RAM allocation ratio: 1.5:1 - -The default CPU allocation ratio of 16:1 means that the scheduler -allocates up to 16 virtual cores per physical core. For example, if a -physical node has 12 cores, the scheduler sees 192 available virtual -cores. With typical flavor definitions of 4 virtual cores per instance, -this ratio would provide 48 instances on a physical node. - -Similarly, the default RAM allocation ratio of 1.5:1 means that the -scheduler allocates instances to a physical node as long as the total -amount of RAM associated with the instances is less than 1.5 times the -amount of RAM available on the physical node. - -You must select the appropriate CPU and RAM allocation ratio based on -particular use cases. - -Additional hardware -~~~~~~~~~~~~~~~~~~~ - -Certain use cases may benefit from exposure to additional devices on the -compute node. Examples might include: - -* High performance computing jobs that benefit from the availability of - graphics processing units (GPUs) for general-purpose computing. - -* Cryptographic routines that benefit from the availability of hardware - random number generators to avoid entropy starvation. - -* Database management systems that benefit from the availability of - SSDs for ephemeral storage to maximize read/write time. - -Host aggregates group hosts that share similar characteristics, which -can include hardware similarities. The addition of specialized hardware -to a cloud deployment is likely to add to the cost of each node, so -consider carefully whether all compute nodes, or just a subset targeted -by flavors, need the additional customization to support the desired -workloads. - -Utilization -~~~~~~~~~~~ - -Infrastructure-as-a-Service offerings, including OpenStack, use flavors -to provide standardized views of virtual machine resource requirements -that simplify the problem of scheduling instances while making the best -use of the available physical resources. - -In order to facilitate packing of virtual machines onto physical hosts, -the default selection of flavors provides a second largest flavor that -is half the size of the largest flavor in every dimension. It has half -the vCPUs, half the vRAM, and half the ephemeral disk space. The next -largest flavor is half that size again. The following figure provides a -visual representation of this concept for a general purpose computing -design: - -.. figure:: figures/Compute_Tech_Bin_Packing_General1.png - -The following figure displays a CPU-optimized, packed server: - -.. figure:: figures/Compute_Tech_Bin_Packing_CPU_optimized1.png - -These default flavors are well suited to typical configurations of -commodity server hardware. To maximize utilization, however, it may be -necessary to customize the flavors or create new ones in order to better -align instance sizes to the available hardware. - -Workload characteristics may also influence hardware choices and flavor -configuration, particularly where they present different ratios of CPU -versus RAM versus HDD requirements. - -For more information on Flavors see `OpenStack Operations Guide: -Flavors `_. - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -Due to the nature of the workloads in this scenario, a number of -components are highly beneficial for a Compute-focused cloud. This -includes the typical OpenStack components: - -* :term:`Compute service (nova)` - -* :term:`Image service (glance)` - -* :term:`Identity service (keystone)` - -Also consider several specialized components: - -* :term:`Orchestration service (heat)` - Given the nature of the applications involved in this scenario, these - are heavily automated deployments. Making use of Orchestration is - highly beneficial in this case. You can script the deployment of a - batch of instances and the running of tests, but it makes sense to - use the Orchestration service to handle all these actions. - -* :term:`Telemetry service (telemetry)` - Telemetry and the alarms it generates support autoscaling of - instances using Orchestration. Users that are not using the - Orchestration service do not need to deploy the Telemetry service and - may choose to use external solutions to fulfill their metering and - monitoring requirements. - -* :term:`Block Storage service (cinder)` - Due to the burst-able nature of the workloads and the applications - and instances that perform batch processing, this cloud mainly uses - memory or CPU, so the need for add-on storage to each instance is not - a likely requirement. This does not mean that you do not use - OpenStack Block Storage (cinder) in the infrastructure, but typically - it is not a central component. - -* :term:`Networking service (neutron)` - When choosing a networking platform, ensure that it either works with - all desired hypervisor and container technologies and their OpenStack - drivers, or that it includes an implementation of an ML2 mechanism - driver. You can mix networking platforms that provide ML2 mechanisms - drivers. diff --git a/doc/arch-design-to-archive/source/compute-focus.rst b/doc/arch-design-to-archive/source/compute-focus.rst deleted file mode 100644 index 2e918d512f..0000000000 --- a/doc/arch-design-to-archive/source/compute-focus.rst +++ /dev/null @@ -1,34 +0,0 @@ -=============== -Compute focused -=============== - -.. toctree:: - :maxdepth: 2 - - compute-focus-technical-considerations.rst - compute-focus-operational-considerations.rst - compute-focus-architecture.rst - compute-focus-prescriptive-examples.rst - -Compute-focused clouds are a specialized subset of the general -purpose OpenStack cloud architecture. A compute-focused cloud -specifically supports compute intensive workloads. - -.. note:: - - Compute intensive workloads may be CPU intensive, RAM intensive, - or both; they are not typically storage or network intensive. - -Compute-focused workloads may include the following use cases: - -* High performance computing (HPC) -* Big data analytics using Hadoop or other distributed data stores -* Continuous integration/continuous deployment (CI/CD) -* Platform-as-a-Service (PaaS) -* Signal processing for network function virtualization (NFV) - -.. note:: - - A compute-focused OpenStack cloud does not typically use raw - block storage services as it does not host applications that - require persistent block storage. diff --git a/doc/arch-design-to-archive/source/conf.py b/doc/arch-design-to-archive/source/conf.py deleted file mode 100644 index 636837b31f..0000000000 --- a/doc/arch-design-to-archive/source/conf.py +++ /dev/null @@ -1,291 +0,0 @@ -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or -# implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This file is execfile()d with the current directory set to its -# containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import os -# import sys - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = ['openstackdocstheme'] - -# Add any paths that contain templates here, relative to this directory. -# templates_path = ['_templates'] - -# The suffix of source filenames. -source_suffix = '.rst' - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -repository_name = "openstack/openstack-manuals" -bug_project = 'openstack-manuals' -project = u'Architecture Design Guide' -bug_tag = u'arch-design-to-archive' -copyright = u'2015-2017, OpenStack contributors' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = '0.9' -# The full version, including alpha/beta/rc tags. -release = '0.9' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['common/cli*', 'common/nova*', 'common/get-started-*'] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -# keep_warnings = False - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'openstackdocs' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [openstackdocstheme.get_html_theme_path()] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# html_logo = None - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = [] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -# html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# So that we can enable "log-a-bug" links from each output HTML page, this -# variable must be set to a format that includes year, month, day, hours and -# minutes. -html_last_updated_fmt = '%Y-%m-%d %H:%M' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -html_use_index = False - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -html_show_sourcelink = False - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = 'arch-design-to-archive' - -# If true, publish source files -html_copy_source = False - -# -- Options for LaTeX output --------------------------------------------- - -latex_engine = 'xelatex' - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # 'papersize': 'letterpaper', - - # set font (TODO: different fonts for translated PDF document builds) - 'fontenc': '\\usepackage{fontspec}', - 'fontpkg': '''\ -\defaultfontfeatures{Scale=MatchLowercase} -\setmainfont{Liberation Serif} -\setsansfont{Liberation Sans} -\setmonofont[SmallCapsFont={Liberation Mono}]{Liberation Mono} -''', - - # The font size ('10pt', '11pt' or '12pt'). - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # 'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - ('index', 'ArchGuideRst.tex', u'Architecture Design Guide', - u'OpenStack contributors', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ('index', 'ArchDesignRst', u'Architecture Design Guide', - [u'OpenStack contributors'], 1) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ('index', 'ArchDesignRst', u'Architecture Design Guide', - u'OpenStack contributors', 'ArchDesignRst', - 'To reap the benefits of OpenStack, you should plan, design,' - 'and architect your cloud properly, taking user needs into' - 'account and understanding the use cases.' - 'commands.', 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -# texinfo_no_detailmenu = False - -# -- Options for Internationalization output ------------------------------ -locale_dirs = ['locale/'] diff --git a/doc/arch-design-to-archive/source/figures/Compute_NSX.png 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purpose OpenStack cloud should reflect a cloud -with no pre-defined usage model, designed to run a wide variety of -applications with varying resource usage requirements. These -applications include any of the following: - -* RAM-intensive - -* CPU-intensive - -* Storage-intensive - -Certain hardware form factors may better suit a general purpose -OpenStack cloud due to the requirement for equal (or nearly equal) -balance of resources. Server hardware must provide the following: - -* Equal (or nearly equal) balance of compute capacity (RAM and CPU) - -* Network capacity (number and speed of links) - -* Storage capacity (gigabytes or terabytes as well as :term:`Input/Output - Operations Per Second (IOPS)` - -Evaluate server hardware around four conflicting dimensions: - -Server density - A measure of how many servers can fit into a given measure of - physical space, such as a rack unit [U]. - -Resource capacity - The number of CPU cores, amount of RAM, or amount of deliverable - storage. - -Expandability - Limit of additional resources you can add to a server. - -Cost - The relative purchase price of the hardware weighted against the - level of design effort needed to build the system. - -Increasing server density means sacrificing resource capacity or -expandability, however, increasing resource capacity and expandability -increases cost and decreases server density. As a result, determining -the best server hardware for a general purpose OpenStack architecture -means understanding how choice of form factor will impact the rest of -the design. The following list outlines the form factors to choose from: - -* Blade servers typically support dual-socket multi-core CPUs. Blades - also offer outstanding density. - -* 1U rack-mounted servers occupy only a single rack unit. Their - benefits include high density, support for dual-socket multi-core - CPUs, and support for reasonable RAM amounts. This form factor offers - limited storage capacity, limited network capacity, and limited - expandability. - -* 2U rack-mounted servers offer the expanded storage and networking - capacity that 1U servers tend to lack, but with a corresponding - decrease in server density (half the density offered by 1U - rack-mounted servers). - -* Larger rack-mounted servers, such as 4U servers, will tend to offer - even greater CPU capacity, often supporting four or even eight CPU - sockets. These servers often have much greater expandability so will - provide the best option for upgradability. This means, however, that - the servers have a much lower server density and a much greater - hardware cost. - -* *Sled servers* are rack-mounted servers that support multiple - independent servers in a single 2U or 3U enclosure. This form factor - offers increased density over typical 1U-2U rack-mounted servers but - tends to suffer from limitations in the amount of storage or network - capacity each individual server supports. - -The best form factor for server hardware supporting a general purpose -OpenStack cloud is driven by outside business and cost factors. No -single reference architecture applies to all implementations; the -decision must flow from user requirements, technical considerations, and -operational considerations. Here are some of the key factors that -influence the selection of server hardware: - -Instance density - Sizing is an important consideration for a general purpose OpenStack - cloud. The expected or anticipated number of instances that each - hypervisor can host is a common meter used in sizing the deployment. - The selected server hardware needs to support the expected or - anticipated instance density. - -Host density - Physical data centers have limited physical space, power, and - cooling. The number of hosts (or hypervisors) that can be fitted - into a given metric (rack, rack unit, or floor tile) is another - important method of sizing. Floor weight is an often overlooked - consideration. The data center floor must be able to support the - weight of the proposed number of hosts within a rack or set of - racks. These factors need to be applied as part of the host density - calculation and server hardware selection. - -Power density - Data centers have a specified amount of power fed to a given rack or - set of racks. Older data centers may have a power density as power - as low as 20 AMPs per rack, while more recent data centers can be - architected to support power densities as high as 120 AMP per rack. - The selected server hardware must take power density into account. - -Network connectivity - The selected server hardware must have the appropriate number of - network connections, as well as the right type of network - connections, in order to support the proposed architecture. Ensure - that, at a minimum, there are at least two diverse network - connections coming into each rack. - -The selection of form factors or architectures affects the selection of -server hardware. Ensure that the selected server hardware is configured -to support enough storage capacity (or storage expandability) to match -the requirements of selected scale-out storage solution. Similarly, the -network architecture impacts the server hardware selection and vice -versa. - -Selecting storage hardware -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Determine storage hardware architecture by selecting specific storage -architecture. Determine the selection of storage architecture by -evaluating possible solutions against the critical factors, the user -requirements, technical considerations, and operational considerations. -Incorporate the following facts into your storage architecture: - -Cost - Storage can be a significant portion of the overall system cost. For - an organization that is concerned with vendor support, a commercial - storage solution is advisable, although it comes with a higher price - tag. If initial capital expenditure requires minimization, designing - a system based on commodity hardware would apply. The trade-off is - potentially higher support costs and a greater risk of - incompatibility and interoperability issues. - -Scalability - Scalability, along with expandability, is a major consideration in a - general purpose OpenStack cloud. It might be difficult to predict - the final intended size of the implementation as there are no - established usage patterns for a general purpose cloud. It might - become necessary to expand the initial deployment in order to - accommodate growth and user demand. - -Expandability - Expandability is a major architecture factor for storage solutions - with general purpose OpenStack cloud. A storage solution that - expands to 50 PB is considered more expandable than a solution that - only scales to 10 PB. This meter is related to scalability, which is - the measure of a solution's performance as it expands. - -Using a scale-out storage solution with direct-attached storage (DAS) in -the servers is well suited for a general purpose OpenStack cloud. Cloud -services requirements determine your choice of scale-out solution. You -need to determine if a single, highly expandable and highly vertical, -scalable, centralized storage array is suitable for your design. After -determining an approach, select the storage hardware based on this -criteria. - -This list expands upon the potential impacts for including a particular -storage architecture (and corresponding storage hardware) into the -design for a general purpose OpenStack cloud: - -Connectivity - Ensure that, if storage protocols other than Ethernet are part of - the storage solution, the appropriate hardware has been selected. If - a centralized storage array is selected, ensure that the hypervisor - will be able to connect to that storage array for image storage. - -Usage - How the particular storage architecture will be used is critical for - determining the architecture. Some of the configurations that will - influence the architecture include whether it will be used by the - hypervisors for ephemeral instance storage or if OpenStack Object - Storage will use it for object storage. - -Instance and image locations - Where instances and images will be stored will influence the - architecture. - -Server hardware - If the solution is a scale-out storage architecture that includes - DAS, it will affect the server hardware selection. This could ripple - into the decisions that affect host density, instance density, power - density, OS-hypervisor, management tools and others. - -General purpose OpenStack cloud has multiple options. The key factors -that will have an influence on selection of storage hardware for a -general purpose OpenStack cloud are as follows: - -Capacity - Hardware resources selected for the resource nodes should be capable - of supporting enough storage for the cloud services. Defining the - initial requirements and ensuring the design can support adding - capacity is important. Hardware nodes selected for object storage - should be capable of support a large number of inexpensive disks - with no reliance on RAID controller cards. Hardware nodes selected - for block storage should be capable of supporting high speed storage - solutions and RAID controller cards to provide performance and - redundancy to storage at a hardware level. Selecting hardware RAID - controllers that automatically repair damaged arrays will assist - with the replacement and repair of degraded or deleted storage - devices. - -Performance - Disks selected for object storage services do not need to be fast - performing disks. We recommend that object storage nodes take - advantage of the best cost per terabyte available for storage. - Contrastingly, disks chosen for block storage services should take - advantage of performance boosting features that may entail the use - of SSDs or flash storage to provide high performance block storage - pools. Storage performance of ephemeral disks used for instances - should also be taken into consideration. - -Fault tolerance - Object storage resource nodes have no requirements for hardware - fault tolerance or RAID controllers. It is not necessary to plan for - fault tolerance within the object storage hardware because the - object storage service provides replication between zones as a - feature of the service. Block storage nodes, compute nodes, and - cloud controllers should all have fault tolerance built in at the - hardware level by making use of hardware RAID controllers and - varying levels of RAID configuration. The level of RAID chosen - should be consistent with the performance and availability - requirements of the cloud. - -Selecting networking hardware -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Selecting network architecture determines which network hardware will be -used. Networking software is determined by the selected networking -hardware. - -There are more subtle design impacts that need to be considered. The -selection of certain networking hardware (and the networking software) -affects the management tools that can be used. There are exceptions to -this; the rise of *open* networking software that supports a range of -networking hardware means that there are instances where the -relationship between networking hardware and networking software are not -as tightly defined. - -Some of the key considerations that should be included in the selection -of networking hardware include: - -Port count - The design will require networking hardware that has the requisite - port count. - -Port density - The network design will be affected by the physical space that is - required to provide the requisite port count. A higher port density - is preferred, as it leaves more rack space for compute or storage - components that may be required by the design. This can also lead - into concerns about fault domains and power density that should be - considered. Higher density switches are more expensive and should - also be considered, as it is important not to over design the - network if it is not required. - -Port speed - The networking hardware must support the proposed network speed, for - example: 1 GbE, 10 GbE, or 40 GbE (or even 100 GbE). - -Redundancy - The level of network hardware redundancy required is influenced by - the user requirements for high availability and cost considerations. - Network redundancy can be achieved by adding redundant power - supplies or paired switches. If this is a requirement, the hardware - will need to support this configuration. - -Power requirements - Ensure that the physical data center provides the necessary power - for the selected network hardware. - -.. note:: - - This may be an issue for spine switches in a leaf and spine - fabric, or end of row (EoR) switches. - -There is no single best practice architecture for the networking -hardware supporting a general purpose OpenStack cloud that will apply to -all implementations. Some of the key factors that will have a strong -influence on selection of networking hardware include: - -Connectivity - All nodes within an OpenStack cloud require network connectivity. In - some cases, nodes require access to more than one network segment. - The design must encompass sufficient network capacity and bandwidth - to ensure that all communications within the cloud, both north-south - and east-west traffic have sufficient resources available. - -Scalability - The network design should encompass a physical and logical network - design that can be easily expanded upon. Network hardware should - offer the appropriate types of interfaces and speeds that are - required by the hardware nodes. - -Availability - To ensure that access to nodes within the cloud is not interrupted, - we recommend that the network architecture identify any single - points of failure and provide some level of redundancy or fault - tolerance. With regard to the network infrastructure itself, this - often involves use of networking protocols such as LACP, VRRP or - others to achieve a highly available network connection. In - addition, it is important to consider the networking implications on - API availability. In order to ensure that the APIs, and potentially - other services in the cloud are highly available, we recommend you - design a load balancing solution within the network architecture to - accommodate for these requirements. - -Software selection -~~~~~~~~~~~~~~~~~~ - -Software selection for a general purpose OpenStack architecture design -needs to include these three areas: - -* Operating system (OS) and hypervisor - -* OpenStack components - -* Supplemental software - -Operating system and hypervisor -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The operating system (OS) and hypervisor have a significant impact on -the overall design. Selecting a particular operating system and -hypervisor can directly affect server hardware selection. Make sure the -storage hardware and topology support the selected operating system and -hypervisor combination. Also ensure the networking hardware selection -and topology will work with the chosen operating system and hypervisor -combination. - -Some areas that could be impacted by the selection of OS and hypervisor -include: - -Cost - Selecting a commercially supported hypervisor, such as Microsoft - Hyper-V, will result in a different cost model rather than - community-supported open source hypervisors including - :term:`KVM`, Kinstance or :term:`Xen`. When - comparing open source OS solutions, choosing Ubuntu over Red Hat - (or vice versa) will have an impact on cost due to support - contracts. - -Supportability - Depending on the selected hypervisor, staff should have the - appropriate training and knowledge to support the selected OS and - hypervisor combination. If they do not, training will need to be - provided which could have a cost impact on the design. - -Management tools - The management tools used for Ubuntu and Kinstance differ from the - management tools for VMware vSphere. Although both OS and hypervisor - combinations are supported by OpenStack, there will be very - different impacts to the rest of the design as a result of the - selection of one combination versus the other. - -Scale and performance - Ensure that selected OS and hypervisor combinations meet the - appropriate scale and performance requirements. The chosen - architecture will need to meet the targeted instance-host ratios - with the selected OS-hypervisor combinations. - -Security - Ensure that the design can accommodate regular periodic - installations of application security patches while maintaining - required workloads. The frequency of security patches for the - proposed OS-hypervisor combination will have an impact on - performance and the patch installation process could affect - maintenance windows. - -Supported features - Determine which features of OpenStack are required. This will often - determine the selection of the OS-hypervisor combination. Some - features are only available with specific operating systems or - hypervisors. - -Interoperability - You will need to consider how the OS and hypervisor combination - interactions with other operating systems and hypervisors, including - other software solutions. Operational troubleshooting tools for one - OS-hypervisor combination may differ from the tools used for another - OS-hypervisor combination and, as a result, the design will need to - address if the two sets of tools need to interoperate. - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -Selecting which OpenStack components are included in the overall design -is important. Some OpenStack components, like compute and Image service, -are required in every architecture. Other components, like -Orchestration, are not always required. - -Excluding certain OpenStack components can limit or constrain the -functionality of other components. For example, if the architecture -includes Orchestration but excludes Telemetry, then the design will not -be able to take advantage of Orchestrations' auto scaling functionality. -It is important to research the component interdependencies in -conjunction with the technical requirements before deciding on the final -architecture. - -Networking software -------------------- - -OpenStack Networking (neutron) provides a wide variety of networking -services for instances. There are many additional networking software -packages that can be useful when managing OpenStack components. Some -examples include: - -* Software to provide load balancing - -* Network redundancy protocols - -* Routing daemons - -Some of these software packages are described in more detail in the -OpenStack High Availability Guide (refer to the `OpenStack network -nodes -chapter `__ of -the OpenStack High Availability Guide). - -For a general purpose OpenStack cloud, the OpenStack infrastructure -components need to be highly available. If the design does not include -hardware load balancing, networking software packages like HAProxy will -need to be included. - -Management software -------------------- - -Selected supplemental software solution impacts and affects the overall -OpenStack cloud design. This includes software for providing clustering, -logging, monitoring and alerting. - -Inclusion of clustering software, such as Corosync or Pacemaker, is -determined primarily by the availability requirements. The impact of -including (or not including) these software packages is primarily -determined by the availability of the cloud infrastructure and the -complexity of supporting the configuration after it is deployed. The -`OpenStack High Availability -Guide `__ provides more details on -the installation and configuration of Corosync and Pacemaker, should -these packages need to be included in the design. - -Requirements for logging, monitoring, and alerting are determined by -operational considerations. Each of these sub-categories includes a -number of various options. - -If these software packages are required, the design must account for the -additional resource consumption (CPU, RAM, storage, and network -bandwidth). Some other potential design impacts include: - -* OS-hypervisor combination: Ensure that the selected logging, - monitoring, or alerting tools support the proposed OS-hypervisor - combination. - -* Network hardware: The network hardware selection needs to be - supported by the logging, monitoring, and alerting software. - -Database software ------------------ - -OpenStack components often require access to back-end database services -to store state and configuration information. Selecting an appropriate -back-end database that satisfies the availability and fault tolerance -requirements of the OpenStack services is required. OpenStack services -supports connecting to a database that is supported by the SQLAlchemy -python drivers, however, most common database deployments make use of -MySQL or variations of it. We recommend that the database, which -provides back-end service within a general purpose cloud, be made highly -available when using an available technology which can accomplish that -goal. diff --git a/doc/arch-design-to-archive/source/generalpurpose-operational-considerations.rst b/doc/arch-design-to-archive/source/generalpurpose-operational-considerations.rst deleted file mode 100644 index 14bc64c432..0000000000 --- a/doc/arch-design-to-archive/source/generalpurpose-operational-considerations.rst +++ /dev/null @@ -1,124 +0,0 @@ -========================== -Operational considerations -========================== - -In the planning and design phases of the build out, it is important to -include the operation's function. Operational factors affect the design -choices for a general purpose cloud, and operations staff are often -tasked with the maintenance of cloud environments for larger -installations. - -Expectations set by the Service Level Agreements (SLAs) directly affect -knowing when and where you should implement redundancy and high -availability. SLAs are contractual obligations that provide assurances -for service availability. They define the levels of availability that -drive the technical design, often with penalties for not meeting -contractual obligations. - -SLA terms that affect design include: - -* API availability guarantees implying multiple infrastructure services - and highly available load balancers. - -* Network uptime guarantees affecting switch design, which might - require redundant switching and power. - -* Factor in networking security policy requirements in to your - deployments. - -Support and maintainability -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -To be able to support and maintain an installation, OpenStack cloud -management requires operations staff to understand and comprehend design -architecture content. The operations and engineering staff skill level, -and level of separation, are dependent on size and purpose of the -installation. Large cloud service providers, or telecom providers, are -more likely to be managed by specially trained, dedicated operations -organizations. Smaller implementations are more likely to rely on -support staff that need to take on combined engineering, design and -operations functions. - -Maintaining OpenStack installations requires a variety of technical -skills. You may want to consider using a third-party management company -with special expertise in managing OpenStack deployment. - -Monitoring -~~~~~~~~~~ - -OpenStack clouds require appropriate monitoring platforms to ensure -errors are caught and managed appropriately. Specific meters that are -critically important to monitor include: - -* Image disk utilization - -* Response time to the :term:`Compute API ` - -Leveraging existing monitoring systems is an effective check to ensure -OpenStack environments can be monitored. - -Downtime -~~~~~~~~ - -To effectively run cloud installations, initial downtime planning -includes creating processes and architectures that support the -following: - -* Planned (maintenance) - -* Unplanned (system faults) - -Resiliency of overall system and individual components are going to be -dictated by the requirements of the SLA, meaning designing for -:term:`high availability (HA)` can have cost ramifications. - -Capacity planning -~~~~~~~~~~~~~~~~~ - -Capacity constraints for a general purpose cloud environment include: - -* Compute limits - -* Storage limits - -A relationship exists between the size of the compute environment and -the supporting OpenStack infrastructure controller nodes requiring -support. - -Increasing the size of the supporting compute environment increases the -network traffic and messages, adding load to the controller or -networking nodes. Effective monitoring of the environment will help with -capacity decisions on scaling. - -Compute nodes automatically attach to OpenStack clouds, resulting in a -horizontally scaling process when adding extra compute capacity to an -OpenStack cloud. Additional processes are required to place nodes into -appropriate availability zones and host aggregates. When adding -additional compute nodes to environments, ensure identical or functional -compatible CPUs are used, otherwise live migration features will break. -It is necessary to add rack capacity or network switches as scaling out -compute hosts directly affects network and datacenter resources. - -Assessing the average workloads and increasing the number of instances -that can run within the compute environment by adjusting the overcommit -ratio is another option. It is important to remember that changing the -CPU overcommit ratio can have a detrimental effect and cause a potential -increase in a noisy neighbor. The additional risk of increasing the -overcommit ratio is more instances failing when a compute host fails. - -Compute host components can also be upgraded to account for increases in -demand; this is known as vertical scaling. Upgrading CPUs with more -cores, or increasing the overall server memory, can add extra needed -capacity depending on whether the running applications are more CPU -intensive or memory intensive. - -Insufficient disk capacity could also have a negative effect on overall -performance including CPU and memory usage. Depending on the back-end -architecture of the OpenStack Block Storage layer, capacity includes -adding disk shelves to enterprise storage systems or installing -additional block storage nodes. Upgrading directly attached storage -installed in compute hosts, and adding capacity to the shared storage -for additional ephemeral storage to instances, may be necessary. - -For a deeper discussion on many of these topics, refer to the `OpenStack -Operations Guide `_. diff --git a/doc/arch-design-to-archive/source/generalpurpose-prescriptive-example.rst b/doc/arch-design-to-archive/source/generalpurpose-prescriptive-example.rst deleted file mode 100644 index 7b3bf9dda1..0000000000 --- a/doc/arch-design-to-archive/source/generalpurpose-prescriptive-example.rst +++ /dev/null @@ -1,85 +0,0 @@ -==================== -Prescriptive example -==================== - -An online classified advertising company wants to run web applications -consisting of Tomcat, Nginx and MariaDB in a private cloud. To be able -to meet policy requirements, the cloud infrastructure will run in their -own data center. The company has predictable load requirements, but -requires scaling to cope with nightly increases in demand. Their current -environment does not have the flexibility to align with their goal of -running an open source API environment. The current environment consists -of the following: - -* Between 120 and 140 installations of Nginx and Tomcat, each with 2 - vCPUs and 4 GB of RAM - -* A three-node MariaDB and Galera cluster, each with 4 vCPUs and 8 GB - RAM - -The company runs hardware load balancers and multiple web applications -serving their websites, and orchestrates environments using combinations -of scripts and Puppet. The website generates large amounts of log data -daily that requires archiving. - -The solution would consist of the following OpenStack components: - -* A firewall, switches and load balancers on the public facing network - connections. - -* OpenStack Controller service running Image, Identity, Networking, - combined with support services such as MariaDB and RabbitMQ, - configured for high availability on at least three controller nodes. - -* OpenStack compute nodes running the KVM hypervisor. - -* OpenStack Block Storage for use by compute instances, requiring - persistent storage (such as databases for dynamic sites). - -* OpenStack Object Storage for serving static objects (such as images). - -.. figure:: figures/General_Architecture3.png - -Running up to 140 web instances and the small number of MariaDB -instances requires 292 vCPUs available, as well as 584 GB RAM. On a -typical 1U server using dual-socket hex-core Intel CPUs with -Hyperthreading, and assuming 2:1 CPU overcommit ratio, this would -require 8 OpenStack compute nodes. - -The web application instances run from local storage on each of the -OpenStack compute nodes. The web application instances are stateless, -meaning that any of the instances can fail and the application will -continue to function. - -MariaDB server instances store their data on shared enterprise storage, -such as NetApp or Solidfire devices. If a MariaDB instance fails, -storage would be expected to be re-attached to another instance and -rejoined to the Galera cluster. - -Logs from the web application servers are shipped to OpenStack Object -Storage for processing and archiving. - -Additional capabilities can be realized by moving static web content to -be served from OpenStack Object Storage containers, and backing the -OpenStack Image service with OpenStack Object Storage. - -.. note:: - - Increasing OpenStack Object Storage means network bandwidth needs to - be taken into consideration. Running OpenStack Object Storage with - network connections offering 10 GbE or better connectivity is - advised. - -Leveraging Orchestration and Telemetry services is also a potential -issue when providing auto-scaling, orchestrated web application -environments. Defining the web applications in a -:term:`Heat Orchestration Template (HOT)` -negates the reliance on the current scripted Puppet -solution. - -OpenStack Networking can be used to control hardware load balancers -through the use of plug-ins and the Networking API. This allows users to -control hardware load balance pools and instances as members in these -pools, but their use in production environments must be carefully -weighed against current stability. - diff --git a/doc/arch-design-to-archive/source/generalpurpose-technical-considerations.rst b/doc/arch-design-to-archive/source/generalpurpose-technical-considerations.rst deleted file mode 100644 index 4e88a77929..0000000000 --- a/doc/arch-design-to-archive/source/generalpurpose-technical-considerations.rst +++ /dev/null @@ -1,618 +0,0 @@ -======================== -Technical considerations -======================== - -General purpose clouds are expected to include these base services: - -* Compute - -* Network - -* Storage - -Each of these services have different resource requirements. As a -result, you must make design decisions relating directly to the service, -as well as provide a balanced infrastructure for all services. - -Take into consideration the unique aspects of each service, as -individual characteristics and service mass can impact the hardware -selection process. Hardware designs should be generated for each of the -services. - -Hardware decisions are also made in relation to network architecture and -facilities planning. These factors play heavily into the overall -architecture of an OpenStack cloud. - -Compute resource design -~~~~~~~~~~~~~~~~~~~~~~~ - -When designing compute resource pools, a number of factors can impact -your design decisions. Factors such as number of processors, amount of -memory, and the quantity of storage required for each hypervisor must be -taken into account. - -You will also need to decide whether to provide compute resources in a -single pool or in multiple pools. In most cases, multiple pools of -resources can be allocated and addressed on demand. A compute design -that allocates multiple pools of resources makes best use of application -resources, and is commonly referred to as bin packing. - -In a bin packing design, each independent resource pool provides service -for specific flavors. This helps to ensure that, as instances are -scheduled onto compute hypervisors, each independent node's resources -will be allocated in a way that makes the most efficient use of the -available hardware. Bin packing also requires a common hardware design, -with all hardware nodes within a compute resource pool sharing a common -processor, memory, and storage layout. This makes it easier to deploy, -support, and maintain nodes throughout their lifecycle. - -An overcommit ratio is the ratio of available virtual resources to -available physical resources. This ratio is configurable for CPU and -memory. The default CPU overcommit ratio is 16:1, and the default memory -overcommit ratio is 1.5:1. Determining the tuning of the overcommit -ratios during the design phase is important as it has a direct impact on -the hardware layout of your compute nodes. - -When selecting a processor, compare features and performance -characteristics. Some processors include features specific to -virtualized compute hosts, such as hardware-assisted virtualization, and -technology related to memory paging (also known as EPT shadowing). These -types of features can have a significant impact on the performance of -your virtual machine. - -You will also need to consider the compute requirements of -non-hypervisor nodes (sometimes referred to as resource nodes). This -includes controller, object storage, and block storage nodes, and -networking services. - -The number of processor cores and threads impacts the number of worker -threads which can be run on a resource node. Design decisions must -relate directly to the service being run on it, as well as provide a -balanced infrastructure for all services. - -Workload can be unpredictable in a general purpose cloud, so consider -including the ability to add additional compute resource pools on -demand. In some cases, however, the demand for certain instance types or -flavors may not justify individual hardware design. In either case, -start by allocating hardware designs that are capable of servicing the -most common instance requests. If you want to add additional hardware to -the overall architecture, this can be done later. - -Designing network resources -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -OpenStack clouds generally have multiple network segments, with each -segment providing access to particular resources. The network services -themselves also require network communication paths which should be -separated from the other networks. When designing network services for a -general purpose cloud, plan for either a physical or logical separation -of network segments used by operators and projects. You can also create -an additional network segment for access to internal services such as -the message bus and database used by various services. Segregating these -services onto separate networks helps to protect sensitive data and -protects against unauthorized access to services. - -Choose a networking service based on the requirements of your instances. -The architecture and design of your cloud will impact whether you choose -OpenStack Networking (neutron), or legacy networking (nova-network). - -Legacy networking (nova-network) - The legacy networking (nova-network) service is primarily a layer-2 - networking service that functions in two modes, which use VLANs in - different ways. In a flat network mode, all network hardware nodes - and devices throughout the cloud are connected to a single layer-2 - network segment that provides access to application data. - - When the network devices in the cloud support segmentation using - VLANs, legacy networking can operate in the second mode. In this - design model, each project within the cloud is assigned a network - subnet which is mapped to a VLAN on the physical network. It is - especially important to remember the maximum number of 4096 VLANs - which can be used within a spanning tree domain. This places a hard - limit on the amount of growth possible within the data center. When - designing a general purpose cloud intended to support multiple - projects, we recommend the use of legacy networking with VLANs, and - not in flat network mode. - -Another consideration regarding network is the fact that legacy -networking is entirely managed by the cloud operator; projects do not -have control over network resources. If projects require the ability to -manage and create network resources such as network segments and -subnets, it will be necessary to install the OpenStack Networking -service to provide network access to instances. - -Networking (neutron) - OpenStack Networking (neutron) is a first class networking service - that gives full control over creation of virtual network resources - to projects. This is often accomplished in the form of tunneling - protocols which will establish encapsulated communication paths over - existing network infrastructure in order to segment project traffic. - These methods vary depending on the specific implementation, but - some of the more common methods include tunneling over GRE, - encapsulating with VXLAN, and VLAN tags. - -We recommend you design at least three network segments: - -* The first segment is a public network, used for access to REST APIs - by projects and operators. The controller nodes and swift proxies are - the only devices connecting to this network segment. In some cases, - this network might also be serviced by hardware load balancers and - other network devices. - -* The second segment is used by administrators to manage hardware - resources. Configuration management tools also use this for deploying - software and services onto new hardware. In some cases, this network - segment might also be used for internal services, including the - message bus and database services. This network needs to communicate - with every hardware node. Due to the highly sensitive nature of this - network segment, you also need to secure this network from - unauthorized access. - -* The third network segment is used by applications and consumers to - access the physical network, and for users to access applications. - This network is segregated from the one used to access the cloud APIs - and is not capable of communicating directly with the hardware - resources in the cloud. Compute resource nodes and network gateway - services which allow application data to access the physical network - from outside of the cloud need to communicate on this network - segment. - -Designing Object Storage -~~~~~~~~~~~~~~~~~~~~~~~~ - -When designing hardware resources for OpenStack Object Storage, the -primary goal is to maximize the amount of storage in each resource node -while also ensuring that the cost per terabyte is kept to a minimum. -This often involves utilizing servers which can hold a large number of -spinning disks. Whether choosing to use 2U server form factors with -directly attached storage or an external chassis that holds a larger -number of drives, the main goal is to maximize the storage available in -each node. - -.. note:: - - We do not recommended investing in enterprise class drives for an - OpenStack Object Storage cluster. The consistency and partition - tolerance characteristics of OpenStack Object Storage ensures that - data stays up to date and survives hardware faults without the use - of any specialized data replication devices. - -One of the benefits of OpenStack Object Storage is the ability to mix -and match drives by making use of weighting within the swift ring. When -designing your swift storage cluster, we recommend making use of the -most cost effective storage solution available at the time. - -To achieve durability and availability of data stored as objects it is -important to design object storage resource pools to ensure they can -provide the suggested availability. Considering rack-level and -zone-level designs to accommodate the number of replicas configured to -be stored in the Object Storage service (the default number of replicas -is three) is important when designing beyond the hardware node level. -Each replica of data should exist in its own availability zone with its -own power, cooling, and network resources available to service that -specific zone. - -Object storage nodes should be designed so that the number of requests -does not hinder the performance of the cluster. The object storage -service is a chatty protocol, therefore making use of multiple -processors that have higher core counts will ensure the IO requests do -not inundate the server. - -Designing Block Storage -~~~~~~~~~~~~~~~~~~~~~~~ - -When designing OpenStack Block Storage resource nodes, it is helpful to -understand the workloads and requirements that will drive the use of -block storage in the cloud. We recommend designing block storage pools -so that projects can choose appropriate storage solutions for their -applications. By creating multiple storage pools of different types, in -conjunction with configuring an advanced storage scheduler for the block -storage service, it is possible to provide projects with a large catalog -of storage services with a variety of performance levels and redundancy -options. - -Block storage also takes advantage of a number of enterprise storage -solutions. These are addressed via a plug-in driver developed by the -hardware vendor. A large number of enterprise storage plug-in drivers -ship out-of-the-box with OpenStack Block Storage (and many more -available via third party channels). General purpose clouds are more -likely to use directly attached storage in the majority of block storage -nodes, deeming it necessary to provide additional levels of service to -projects which can only be provided by enterprise class storage -solutions. - -Redundancy and availability requirements impact the decision to use a -RAID controller card in block storage nodes. The input-output per second -(IOPS) demand of your application will influence whether or not you -should use a RAID controller, and which level of RAID is required. -Making use of higher performing RAID volumes is suggested when -considering performance. However, where redundancy of block storage -volumes is more important we recommend making use of a redundant RAID -configuration such as RAID 5 or RAID 6. Some specialized features, such -as automated replication of block storage volumes, may require the use -of third-party plug-ins and enterprise block storage solutions in order -to provide the high demand on storage. Furthermore, where extreme -performance is a requirement it may also be necessary to make use of -high speed SSD disk drives' high performing flash storage solutions. - -Software selection -~~~~~~~~~~~~~~~~~~ - -The software selection process plays a large role in the architecture of -a general purpose cloud. The following have a large impact on the design -of the cloud: - -* Choice of operating system - -* Selection of OpenStack software components - -* Choice of hypervisor - -* Selection of supplemental software - -Operating system (OS) selection plays a large role in the design and -architecture of a cloud. There are a number of OSes which have native -support for OpenStack including: - -* Ubuntu - -* Red Hat Enterprise Linux (RHEL) - -* CentOS - -* SUSE Linux Enterprise Server (SLES) - -.. note:: - - Native support is not a constraint on the choice of OS; users are - free to choose just about any Linux distribution (or even Microsoft - Windows) and install OpenStack directly from source (or compile - their own packages). However, many organizations will prefer to - install OpenStack from distribution-supplied packages or - repositories (although using the distribution vendor's OpenStack - packages might be a requirement for support). - -OS selection also directly influences hypervisor selection. A cloud -architect who selects Ubuntu, RHEL, or SLES has some flexibility in -hypervisor; KVM, Xen, and LXC are supported virtualization methods -available under OpenStack Compute (nova) on these Linux distributions. -However, a cloud architect who selects Windows Server is limited to Hyper-V. -Similarly, a cloud architect who selects XenServer is limited to the -CentOS-based dom0 operating system provided with XenServer. - -The primary factors that play into OS-hypervisor selection include: - -User requirements - The selection of OS-hypervisor combination first and foremost needs - to support the user requirements. - -Support - The selected OS-hypervisor combination needs to be supported by - OpenStack. - -Interoperability - The OS-hypervisor needs to be interoperable with other features and - services in the OpenStack design in order to meet the user - requirements. - -Hypervisor -~~~~~~~~~~ - -OpenStack supports a wide variety of hypervisors, one or more of which -can be used in a single cloud. These hypervisors include: - -* KVM (and QEMU) - -* XCP/XenServer - -* vSphere (vCenter and ESXi) - -* Hyper-V - -* LXC - -* Docker - -* Bare-metal - -A complete list of supported hypervisors and their capabilities can be -found at `OpenStack Hypervisor Support -Matrix `_. - -We recommend general purpose clouds use hypervisors that support the -most general purpose use cases, such as KVM and Xen. More specific -hypervisors should be chosen to account for specific functionality or a -supported feature requirement. In some cases, there may also be a -mandated requirement to run software on a certified hypervisor including -solutions from VMware, Microsoft, and Citrix. - -The features offered through the OpenStack cloud platform determine the -best choice of a hypervisor. Each hypervisor has their own hardware -requirements which may affect the decisions around designing a general -purpose cloud. - -In a mixed hypervisor environment, specific aggregates of compute -resources, each with defined capabilities, enable workloads to utilize -software and hardware specific to their particular requirements. This -functionality can be exposed explicitly to the end user, or accessed -through defined metadata within a particular flavor of an instance. - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -A general purpose OpenStack cloud design should incorporate the core -OpenStack services to provide a wide range of services to end-users. The -OpenStack core services recommended in a general purpose cloud are: - -* :term:`Compute service (nova)` - -* :term:`Networking service (neutron)` - -* :term:`Image service (glance)` - -* :term:`Identity service (keystone)` - -* :term:`Dashboard (horizon)` - -* :term:`Telemetry service (telemetry)` - -A general purpose cloud may also include :term:`Object Storage service -(swift)`. :term:`Block Storage service (cinder)`. -These may be selected to provide storage to applications and instances. - -Supplemental software -~~~~~~~~~~~~~~~~~~~~~ - -A general purpose OpenStack deployment consists of more than just -OpenStack-specific components. A typical deployment involves services -that provide supporting functionality, including databases and message -queues, and may also involve software to provide high availability of -the OpenStack environment. Design decisions around the underlying -message queue might affect the required number of controller services, -as well as the technology to provide highly resilient database -functionality, such as MariaDB with Galera. In such a scenario, -replication of services relies on quorum. - -Where many general purpose deployments use hardware load balancers to -provide highly available API access and SSL termination, software -solutions, for example HAProxy, can also be considered. It is vital to -ensure that such software implementations are also made highly -available. High availability can be achieved by using software such as -Keepalived or Pacemaker with Corosync. Pacemaker and Corosync can -provide active-active or active-passive highly available configuration -depending on the specific service in the OpenStack environment. Using -this software can affect the design as it assumes at least a 2-node -controller infrastructure where one of those nodes may be running -certain services in standby mode. - -Memcached is a distributed memory object caching system, and Redis is a -key-value store. Both are deployed on general purpose clouds to assist -in alleviating load to the Identity service. The memcached service -caches tokens, and due to its distributed nature it can help alleviate -some bottlenecks to the underlying authentication system. Using -memcached or Redis does not affect the overall design of your -architecture as they tend to be deployed onto the infrastructure nodes -providing the OpenStack services. - -Controller infrastructure -~~~~~~~~~~~~~~~~~~~~~~~~~ - -The Controller infrastructure nodes provide management services to the -end-user as well as providing services internally for the operating of -the cloud. The Controllers run message queuing services that carry -system messages between each service. Performance issues related to the -message bus would lead to delays in sending that message to where it -needs to go. The result of this condition would be delays in operation -functions such as spinning up and deleting instances, provisioning new -storage volumes and managing network resources. Such delays could -adversely affect an application’s ability to react to certain -conditions, especially when using auto-scaling features. It is important -to properly design the hardware used to run the controller -infrastructure as outlined above in the Hardware Selection section. - -Performance of the controller services is not limited to processing -power, but restrictions may emerge in serving concurrent users. Ensure -that the APIs and Horizon services are load tested to ensure that you -are able to serve your customers. Particular attention should be made to -the OpenStack Identity Service (Keystone), which provides the -authentication and authorization for all services, both internally to -OpenStack itself and to end-users. This service can lead to a -degradation of overall performance if this is not sized appropriately. - -Network performance -~~~~~~~~~~~~~~~~~~~ - -In a general purpose OpenStack cloud, the requirements of the network -help determine performance capabilities. It is possible to design -OpenStack environments that run a mix of networking capabilities. By -utilizing the different interface speeds, the users of the OpenStack -environment can choose networks that are fit for their purpose. - -Network performance can be boosted considerably by implementing hardware -load balancers to provide front-end service to the cloud APIs. The -hardware load balancers also perform SSL termination if that is a -requirement of your environment. When implementing SSL offloading, it is -important to understand the SSL offloading capabilities of the devices -selected. - -Compute host -~~~~~~~~~~~~ - -The choice of hardware specifications used in compute nodes including -CPU, memory and disk type directly affects the performance of the -instances. Other factors which can directly affect performance include -tunable parameters within the OpenStack services, for example the -overcommit ratio applied to resources. The defaults in OpenStack Compute -set a 16:1 over-commit of the CPU and 1.5 over-commit of the memory. -Running at such high ratios leads to an increase in "noisy-neighbor" -activity. Care must be taken when sizing your Compute environment to -avoid this scenario. For running general purpose OpenStack environments -it is possible to keep to the defaults, but make sure to monitor your -environment as usage increases. - -Storage performance -~~~~~~~~~~~~~~~~~~~ - -When considering performance of Block Storage, hardware and -architecture choice is important. Block Storage can use enterprise -back-end systems such as NetApp or EMC, scale out storage such as -GlusterFS and Ceph, or simply use the capabilities of directly attached -storage in the nodes themselves. Block Storage may be deployed so that -traffic traverses the host network, which could affect, and be adversely -affected by, the front-side API traffic performance. As such, consider -using a dedicated data storage network with dedicated interfaces on the -Controller and Compute hosts. - -When considering performance of Object Storage, a number of design -choices will affect performance. A user’s access to the Object -Storage is through the proxy services, which sit behind hardware load -balancers. By the very nature of a highly resilient storage system, -replication of the data would affect performance of the overall system. -In this case, 10 GbE (or better) networking is recommended throughout -the storage network architecture. - -High Availability -~~~~~~~~~~~~~~~~~ - -In OpenStack, the infrastructure is integral to providing services and -should always be available, especially when operating with SLAs. -Ensuring network availability is accomplished by designing the network -architecture so that no single point of failure exists. A consideration -of the number of switches, routes and redundancies of power should be -factored into core infrastructure, as well as the associated bonding of -networks to provide diverse routes to your highly available switch -infrastructure. - -The OpenStack services themselves should be deployed across multiple -servers that do not represent a single point of failure. Ensuring API -availability can be achieved by placing these services behind highly -available load balancers that have multiple OpenStack servers as -members. - -OpenStack lends itself to deployment in a highly available manner where -it is expected that at least 2 servers be utilized. These can run all -the services involved from the message queuing service, for example -RabbitMQ or QPID, and an appropriately deployed database service such as -MySQL or MariaDB. As services in the cloud are scaled out, back-end -services will need to scale too. Monitoring and reporting on server -utilization and response times, as well as load testing your systems, -will help determine scale out decisions. - -Care must be taken when deciding network functionality. Currently, -OpenStack supports both the legacy networking (nova-network) system and -the newer, extensible OpenStack Networking (neutron). Both have their -pros and cons when it comes to providing highly available access. Legacy -networking, which provides networking access maintained in the OpenStack -Compute code, provides a feature that removes a single point of failure -when it comes to routing, and this feature is currently missing in -OpenStack Networking. The effect of legacy networking’s multi-host -functionality restricts failure domains to the host running that -instance. - -When using Networking, the OpenStack controller servers or -separate Networking hosts handle routing. For a deployment that requires -features available in only Networking, it is possible to remove this -restriction by using third party software that helps maintain highly -available L3 routes. Doing so allows for common APIs to control network -hardware, or to provide complex multi-tier web applications in a secure -manner. It is also possible to completely remove routing from -Networking, and instead rely on hardware routing capabilities. In this -case, the switching infrastructure must support L3 routing. - -OpenStack Networking and legacy networking both have their advantages -and disadvantages. They are both valid and supported options that fit -different network deployment models described in the -`Networking deployment options table ` -of OpenStack Operations Guide. - -Ensure your deployment has adequate back-up capabilities. - -Application design must also be factored into the capabilities of the -underlying cloud infrastructure. If the compute hosts do not provide a -seamless live migration capability, then it must be expected that when a -compute host fails, that instance and any data local to that instance -will be deleted. However, when providing an expectation to users that -instances have a high-level of uptime guarantees, the infrastructure -must be deployed in a way that eliminates any single point of failure -when a compute host disappears. This may include utilizing shared file -systems on enterprise storage or OpenStack Block storage to provide a -level of guarantee to match service features. - -For more information on high availability in OpenStack, see the -`OpenStack High Availability -Guide `_. - -Security -~~~~~~~~ - -A security domain comprises users, applications, servers or networks -that share common trust requirements and expectations within a system. -Typically they have the same authentication and authorization -requirements and users. - -These security domains are: - -* Public - -* Guest - -* Management - -* Data - -These security domains can be mapped to an OpenStack deployment -individually, or combined. In each case, the cloud operator should be -aware of the appropriate security concerns. Security domains should be -mapped out against your specific OpenStack deployment topology. The -domains and their trust requirements depend upon whether the cloud -instance is public, private, or hybrid. - -* The public security domain is an entirely untrusted area of the cloud - infrastructure. It can refer to the internet as a whole or simply to - networks over which you have no authority. This domain should always - be considered untrusted. - -* The guest security domain handles compute data generated by instances - on the cloud but not services that support the operation of the - cloud, such as API calls. Public cloud providers and private cloud - providers who do not have stringent controls on instance use or who - allow unrestricted internet access to instances should consider this - domain to be untrusted. Private cloud providers may want to consider - this network as internal and therefore trusted only if they have - controls in place to assert that they trust instances and all their - projects. - -* The management security domain is where services interact. Sometimes - referred to as the control plane, the networks in this domain - transport confidential data such as configuration parameters, user - names, and passwords. In most deployments this domain is considered - trusted. - -* The data security domain is concerned primarily with information - pertaining to the storage services within OpenStack. Much of the data - that crosses this network has high integrity and confidentiality - requirements and, depending on the type of deployment, may also have - strong availability requirements. The trust level of this network is - heavily dependent on other deployment decisions. - -When deploying OpenStack in an enterprise as a private cloud it is -usually behind the firewall and within the trusted network alongside -existing systems. Users of the cloud are employees that are bound by the -security requirements set forth by the company. This tends to push most -of the security domains towards a more trusted model. However, when -deploying OpenStack in a public facing role, no assumptions can be made -and the attack vectors significantly increase. - -Consideration must be taken when managing the users of the system for -both public and private clouds. The identity service allows for LDAP to -be part of the authentication process. Including such systems in an -OpenStack deployment may ease user management if integrating into -existing systems. - -It is important to understand that user authentication requests include -sensitive information including user names, passwords, and -authentication tokens. For this reason, placing the API services behind -hardware that performs SSL termination is strongly recommended. - -For more information OpenStack Security, see the `OpenStack Security -Guide `_. diff --git a/doc/arch-design-to-archive/source/generalpurpose-user-requirements.rst b/doc/arch-design-to-archive/source/generalpurpose-user-requirements.rst deleted file mode 100644 index b06f2608a3..0000000000 --- a/doc/arch-design-to-archive/source/generalpurpose-user-requirements.rst +++ /dev/null @@ -1,99 +0,0 @@ -================= -User requirements -================= - -When building a general purpose cloud, you should follow the -:term:`Infrastructure-as-a-Service (IaaS)` model; a platform best suited -for use cases with simple requirements. General purpose cloud user -requirements are not complex. However, it is important to capture them -even if the project has minimum business and technical requirements, such -as a proof of concept (PoC), or a small lab platform. - -.. note:: - The following user considerations are written from the perspective - of the cloud builder, not from the perspective of the end user. - -Business requirements -~~~~~~~~~~~~~~~~~~~~~ - -Cost - Financial factors are a primary concern for any organization. Cost - is an important criterion as general purpose clouds are considered - the baseline from which all other cloud architecture environments - derive. General purpose clouds do not always provide the most - cost-effective environment for specialized applications or - situations. Unless razor-thin margins and costs have been mandated - as a critical factor, cost should not be the sole consideration when - choosing or designing a general purpose architecture. - -Time to market - The ability to deliver services or products within a flexible time - frame is a common business factor when building a general purpose - cloud. Delivering a product in six months instead of two years is a - driving force behind the decision to build general purpose clouds. - General purpose clouds allow users to self-provision and gain access - to compute, network, and storage resources on-demand thus decreasing - time to market. - -Revenue opportunity - Revenue opportunities for a cloud will vary greatly based on the - intended use case of that particular cloud. Some general purpose - clouds are built for commercial customer facing products, but there - are alternatives that might make the general purpose cloud the right - choice. - -Technical requirements -~~~~~~~~~~~~~~~~~~~~~~ - -Technical cloud architecture requirements should be weighted against the -business requirements. - -Performance - As a baseline product, general purpose clouds do not provide - optimized performance for any particular function. While a general - purpose cloud should provide enough performance to satisfy average - user considerations, performance is not a general purpose cloud - customer driver. - -No predefined usage model - The lack of a pre-defined usage model enables the user to run a wide - variety of applications without having to know the application - requirements in advance. This provides a degree of independence and - flexibility that no other cloud scenarios are able to provide. - -On-demand and self-service application - By definition, a cloud provides end users with the ability to - self-provision computing power, storage, networks, and software in a - simple and flexible way. The user must be able to scale their - resources up to a substantial level without disrupting the - underlying host operations. One of the benefits of using a general - purpose cloud architecture is the ability to start with limited - resources and increase them over time as the user demand grows. - -Public cloud - For a company interested in building a commercial public cloud - offering based on OpenStack, the general purpose architecture model - might be the best choice. Designers are not always going to know the - purposes or workloads for which the end users will use the cloud. - -Internal consumption (private) cloud - Organizations need to determine if it is logical to create their own - clouds internally. Using a private cloud, organizations are able to - maintain complete control over architectural and cloud components. - - .. note:: - Users will want to combine using the internal cloud with access - to an external cloud. If that case is likely, it might be worth - exploring the possibility of taking a multi-cloud approach with - regard to at least some of the architectural elements. - - Designs that incorporate the use of multiple clouds, such as a - private cloud and a public cloud offering, are described in the - "Multi-Cloud" scenario, see :doc:`multi-site`. - -Security - Security should be implemented according to asset, threat, and - vulnerability risk assessment matrices. For cloud domains that - require increased computer security, network security, or - information security, a general purpose cloud is not considered an - appropriate choice. diff --git a/doc/arch-design-to-archive/source/generalpurpose.rst b/doc/arch-design-to-archive/source/generalpurpose.rst deleted file mode 100644 index 195cdbc6be..0000000000 --- a/doc/arch-design-to-archive/source/generalpurpose.rst +++ /dev/null @@ -1,57 +0,0 @@ -=============== -General purpose -=============== - -.. toctree:: - :maxdepth: 2 - - generalpurpose-user-requirements.rst - generalpurpose-technical-considerations.rst - generalpurpose-operational-considerations.rst - generalpurpose-architecture.rst - generalpurpose-prescriptive-example.rst - - -An OpenStack general purpose cloud is often considered a starting -point for building a cloud deployment. They are designed to balance -the components and do not emphasize any particular aspect of the -overall computing environment. Cloud design must give equal weight -to the compute, network, and storage components. General purpose clouds -are found in private, public, and hybrid environments, lending -themselves to many different use cases. - -.. note:: - - General purpose clouds are homogeneous deployments. - They are not suited to specialized environments or edge case situations. - -Common uses of a general purpose cloud include: - -* Providing a simple database -* A web application runtime environment -* A shared application development platform -* Lab test bed - -Use cases that benefit from scale-out rather than scale-up approaches -are good candidates for general purpose cloud architecture. - -A general purpose cloud is designed to have a range of potential -uses or functions; not specialized for specific use cases. General -purpose architecture is designed to address 80% of potential use -cases available. The infrastructure, in itself, is a specific use -case, enabling it to be used as a base model for the design process. - -General purpose clouds are designed to be platforms that are suited -for general purpose applications. - -General purpose clouds are limited to the most basic components, -but they can include additional resources such as: - -* Virtual-machine disk image library -* Raw block storage -* File or object storage -* Firewalls -* Load balancers -* IP addresses -* Network overlays or virtual local area networks (VLANs) -* Software bundles diff --git a/doc/arch-design-to-archive/source/hybrid-architecture.rst b/doc/arch-design-to-archive/source/hybrid-architecture.rst deleted file mode 100644 index 02a0b041b7..0000000000 --- a/doc/arch-design-to-archive/source/hybrid-architecture.rst +++ /dev/null @@ -1,149 +0,0 @@ -============ -Architecture -============ - -Map out the dependencies of the expected workloads and the cloud -infrastructures required to support them to architect a solution -for the broadest compatibility between cloud platforms, minimizing -the need to create workarounds and processes to fill identified gaps. - -For your chosen cloud management platform, note the relative -levels of support for both monitoring and orchestration. - -.. figure:: figures/Multi-Cloud_Priv-AWS4.png - :width: 100% - -Image portability -~~~~~~~~~~~~~~~~~ - -The majority of cloud workloads currently run on instances using -hypervisor technologies. The challenge is that each of these hypervisors -uses an image format that may not be compatible with the others. -When possible, standardize on a single hypervisor and instance image format. -This may not be possible when using externally-managed public clouds. - -Conversion tools exist to address image format compatibility. -Examples include `virt-p2v/virt-v2v `_ -and `virt-edit `_. -These tools cannot serve beyond basic cloud instance specifications. - -Alternatively, build a thin operating system image as the base for -new instances. -This facilitates rapid creation of cloud instances using cloud orchestration -or configuration management tools for more specific templating. -Remember if you intend to use portable images for disaster recovery, -application diversity, or high availability, your users could move -the images and instances between cloud platforms regularly. - -Upper-layer services -~~~~~~~~~~~~~~~~~~~~ - -Many clouds offer complementary services beyond the -basic compute, network, and storage components. -These additional services often simplify the deployment -and management of applications on a cloud platform. - -When moving workloads from the source to the destination -cloud platforms, consider that the destination cloud platform -may not have comparable services. Implement workloads -in a different way or by using a different technology. - -For example, moving an application that uses a NoSQL database -service such as MongoDB could cause difficulties in maintaining -the application between the platforms. - -There are a number of options that are appropriate for -the hybrid cloud use case: - -* Implementing a baseline of upper-layer services across all - of the cloud platforms. For platforms that do not support - a given service, create a service on top of that platform - and apply it to the workloads as they are launched on that cloud. -* For example, through the :term:`Database service ` for OpenStack (:term:`trove`), OpenStack supports MySQL - as a service but not NoSQL databases in production. - To move from or run alongside AWS, a NoSQL workload must use - an automation tool, such as the Orchestration service (heat), - to recreate the NoSQL database on top of OpenStack. -* Deploying a :term:`Platform-as-a-Service (PaaS)` technology that - abstracts the upper-layer services from the underlying cloud platform. - The unit of application deployment and migration is the PaaS. - It leverages the services of the PaaS and only consumes the base - infrastructure services of the cloud platform. -* Using automation tools to create the required upper-layer services - that are portable across all cloud platforms. - - For example, instead of using database services that are inherent - in the cloud platforms, launch cloud instances and deploy the - databases on those instances using scripts or configuration and - application deployment tools. - -Network services -~~~~~~~~~~~~~~~~ - -Network services functionality is a critical component of -multiple cloud architectures. It is an important factor -to assess when choosing a CMP and cloud provider. -Considerations include: - -* Functionality -* Security -* Scalability -* High availability (HA) - -Verify and test critical cloud endpoint features. - -* After selecting the network functionality framework, - you must confirm the functionality is compatible. - This ensures testing and functionality persists - during and after upgrades. - - .. note:: - - Diverse cloud platforms may de-synchronize over time - if you do not maintain their mutual compatibility. - This is a particular issue with APIs. - -* Scalability across multiple cloud providers determines - your choice of underlying network framework. - It is important to have the network API functions presented - and to verify that the desired functionality persists across - all chosen cloud endpoint. - -* High availability implementations vary in functionality and design. - Examples of some common methods are active-hot-standby, - active-passive, and active-active. - Develop your high availability implementation and a test framework to - understand the functionality and limitations of the environment. - -* It is imperative to address security considerations. - For example, addressing how data is secured between client and - endpoint and any traffic that traverses the multiple clouds. - Business and regulatory requirements dictate what security - approach to take. For more information, see the - :ref:`Security requirements ` chapter. - -Data -~~~~ - -Traditionally, replication has been the best method of protecting -object store implementations. A variety of replication methods exist -in storage architectures, for example synchronous and asynchronous -mirroring. Most object stores and back-end storage systems implement -methods for replication at the storage subsystem layer. -Object stores also tailor replication techniques -to fit a cloud's requirements. - -Organizations must find the right balance between -data integrity and data availability. Replication strategy may -also influence disaster recovery methods. - -Replication across different racks, data centers, and geographical -regions increases focus on determining and ensuring data locality. -The ability to guarantee data is accessed from the nearest or -fastest storage can be necessary for applications to perform well. - -.. note:: - - When running embedded object store methods, ensure that you do not - instigate extra data replication as this can cause performance issues. diff --git a/doc/arch-design-to-archive/source/hybrid-operational-considerations.rst b/doc/arch-design-to-archive/source/hybrid-operational-considerations.rst deleted file mode 100644 index f007421218..0000000000 --- a/doc/arch-design-to-archive/source/hybrid-operational-considerations.rst +++ /dev/null @@ -1,80 +0,0 @@ -========================== -Operational considerations -========================== - -Hybrid cloud deployments present complex operational challenges. -Differences between provider clouds can cause incompatibilities -with workloads or Cloud Management Platforms (CMP). -Cloud providers may also offer different levels of integration -with competing cloud offerings. - -Monitoring is critical to maintaining a hybrid cloud, and it is -important to determine if a CMP supports monitoring of all the -clouds involved, or if compatible APIs are available to be queried -for necessary information. - -Agility -~~~~~~~ - -Hybrid clouds provide application availability across different -cloud environments and technologies. -This availability enables the deployment to survive disaster -in any single cloud environment. -Each cloud should provide the means to create instances quickly in -response to capacity issues or failure elsewhere in the hybrid cloud. - -Application readiness -~~~~~~~~~~~~~~~~~~~~~ - -Enterprise workloads that depend on the underlying infrastructure -for availability are not designed to run on OpenStack. -If the application cannot tolerate infrastructure failures, -it is likely to require significant operator intervention to recover. -Applications for hybrid clouds must be fault tolerant, with an SLA -that is not tied to the underlying infrastructure. -Ideally, cloud applications should be able to recover when entire -racks and data centers experience an outage. - -Upgrades -~~~~~~~~ - -If a deployment includes a public cloud, predicting upgrades may -not be possible. Carefully examine provider SLAs. - -.. note:: - - At massive scale, even when dealing with a cloud that offers - an SLA with a high percentage of uptime, workloads must be able - to recover quickly. - -When upgrading private cloud deployments, minimize disruption by -making incremental changes and providing a facility to either rollback -or continue to roll forward when using a continuous delivery model. - -You may need to coordinate CMP upgrades with hybrid cloud upgrades -if there are API changes. - -Network Operation Center -~~~~~~~~~~~~~~~~~~~~~~~~ - -Consider infrastructure control when planning the Network Operation -Center (NOC) for a hybrid cloud environment. -If a significant portion of the cloud is on externally managed systems, -prepare for situations where it may not be possible to make changes. -Additionally, providers may differ on how infrastructure must be -managed and exposed. This can lead to delays in root cause analysis -where each insists the blame lies with the other provider. - -Ensure that the network structure connects all clouds to form -integrated system, keeping in mind the state of handoffs. -These handoffs must both be as reliable as possible and -include as little latency as possible to ensure the best -performance of the overall system. - -Maintainability -~~~~~~~~~~~~~~~ - -Hybrid clouds rely on third party systems and processes. -As a result, it is not possible to guarantee proper maintenance -of the overall system. Instead, be prepared to abandon workloads -and recreate them in an improved state. diff --git a/doc/arch-design-to-archive/source/hybrid-prescriptive-examples.rst b/doc/arch-design-to-archive/source/hybrid-prescriptive-examples.rst deleted file mode 100644 index d1e379b51e..0000000000 --- a/doc/arch-design-to-archive/source/hybrid-prescriptive-examples.rst +++ /dev/null @@ -1,155 +0,0 @@ -===================== -Prescriptive examples -===================== - -Hybrid cloud environments are designed for these use cases: - -* Bursting workloads from private to public OpenStack clouds -* Bursting workloads from private to public non-OpenStack clouds -* High availability across clouds (for technical diversity) - -This chapter provides examples of environments that address -each of these use cases. - -Bursting to a public OpenStack cloud -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Company A's data center is running low on capacity. -It is not possible to expand the data center in the foreseeable future. -In order to accommodate the continuously growing need for -development resources in the organization, -Company A decides to use resources in the public cloud. - -Company A has an established data center with a substantial amount -of hardware. Migrating the workloads to a public cloud is not feasible. - -The company has an internal cloud management platform that directs -requests to the appropriate cloud, depending on the local capacity. -This is a custom in-house application written for this specific purpose. - -This solution is depicted in the figure below: - -.. figure:: figures/Multi-Cloud_Priv-Pub3.png - :width: 100% - -This example shows two clouds with a Cloud Management -Platform (CMP) connecting them. This guide does not -discuss a specific CMP, but describes how the Orchestration and -Telemetry services handle, manage, and control workloads. - -The private OpenStack cloud has at least one controller and at least -one compute node. It includes metering using the Telemetry service. -The Telemetry service captures the load increase and the CMP -processes the information. If there is available capacity, -the CMP uses the OpenStack API to call the Orchestration service. -This creates instances on the private cloud in response to user requests. -When capacity is not available on the private cloud, the CMP issues -a request to the Orchestration service API of the public cloud. -This creates the instance on the public cloud. - -In this example, Company A does not direct the deployments to an -external public cloud due to concerns regarding resource control, -security, and increased operational expense. - -Bursting to a public non-OpenStack cloud -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The second example examines bursting workloads from the private cloud -into a non-OpenStack public cloud using Amazon Web Services (AWS) -to take advantage of additional capacity and to scale applications. - -The following diagram demonstrates an OpenStack-to-AWS hybrid cloud: - -.. figure:: figures/Multi-Cloud_Priv-AWS4.png - :width: 100% - -Company B states that its developers are already using AWS -and do not want to change to a different provider. - -If the CMP is capable of connecting to an external cloud -provider with an appropriate API, the workflow process remains -the same as the previous scenario. -The actions the CMP takes, such as monitoring loads and -creating new instances, stay the same. -However, the CMP performs actions in the public cloud -using applicable API calls. - -If the public cloud is AWS, the CMP would use the -EC2 API to create a new instance and assign an Elastic IP. -It can then add that IP to HAProxy in the private cloud. -The CMP can also reference AWS-specific -tools such as CloudWatch and CloudFormation. - -Several open source tool kits for building CMPs are -available and can handle this kind of translation. -Examples include ManageIQ, jClouds, and JumpGate. - -High availability and disaster recovery -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Company C requires their local data center to be able to -recover from failure. Some of the workloads currently in -use are running on their private OpenStack cloud. -Protecting the data involves Block Storage, Object Storage, -and a database. The architecture supports the failure of -large components of the system while ensuring that the -system continues to deliver services. -While the services remain available to users, the failed -components are restored in the background based on standard -best practice data replication policies. -To achieve these objectives, Company C replicates data to -a second cloud in a geographically distant location. -The following diagram describes this system: - -.. figure:: figures/Multi-Cloud_failover2.png - :width: 100% - -This example includes two private OpenStack clouds connected with a CMP. -The source cloud, OpenStack Cloud 1, includes a controller and -at least one instance running MySQL. It also includes at least -one Block Storage volume and one Object Storage volume. -This means that data is available to the users at all times. -The details of the method for protecting each of these sources -of data differs. - -Object Storage relies on the replication capabilities of -the Object Storage provider. -Company C enables OpenStack Object Storage so that it creates -geographically separated replicas that take advantage of this feature. -The company configures storage so that at least one replica -exists in each cloud. In order to make this work, the company -configures a single array spanning both clouds with OpenStack Identity. -Using Federated Identity, the array talks to both clouds, communicating -with OpenStack Object Storage through the Swift proxy. - -For Block Storage, the replication is a little more difficult, -and involves tools outside of OpenStack itself. -The OpenStack Block Storage volume is not set as the drive itself -but as a logical object that points to a physical back end. -Disaster recovery is configured for Block Storage for -synchronous backup for the highest level of data protection, -but asynchronous backup could have been set as an alternative -that is not as latency sensitive. -For asynchronous backup, the Block Storage API makes it possible -to export the data and also the metadata of a particular volume, -so that it can be moved and replicated elsewhere. -More information can be found here: -`Add volume metadata support to Cinder backup -`_. - -The synchronous backups create an identical volume in both -clouds and chooses the appropriate flavor so that each cloud -has an identical back end. This is done by creating volumes -through the CMP. After this is configured, a solution -involving DRDB synchronizes the physical drives. - -The database component is backed up using synchronous backups. -MySQL does not support geographically diverse replication, -so disaster recovery is provided by replicating the file itself. -As it is not possible to use Object Storage as the back end of -a database like MySQL, Swift replication is not an option. -Company C decides not to store the data on another geo-tiered -storage system, such as Ceph, as Block Storage. -This would have given another layer of protection. -Another option would have been to store the database on an OpenStack -Block Storage volume and backing it up like any other Block Storage. diff --git a/doc/arch-design-to-archive/source/hybrid-technical-considerations.rst b/doc/arch-design-to-archive/source/hybrid-technical-considerations.rst deleted file mode 100644 index d2a12d33c0..0000000000 --- a/doc/arch-design-to-archive/source/hybrid-technical-considerations.rst +++ /dev/null @@ -1,155 +0,0 @@ -======================== -Technical considerations -======================== - -A hybrid cloud environment requires inspection and -understanding of technical issues in external data centers that may -not be in your control. Ideally, select an architecture -and CMP that are adaptable to changing environments. - -Using diverse cloud platforms increases the risk of compatibility -issues, but clouds using the same version and distribution -of OpenStack are less likely to experience problems. - -Clouds that exclusively use the same versions of OpenStack should -have no issues, regardless of distribution. More recent distributions -are less likely to encounter incompatibility between versions. -An OpenStack community initiative defines core functions that need to -remain backward compatible between supported versions. For example, the -DefCore initiative defines basic functions that every distribution must -support in order to use the name OpenStack. - -Vendors can add proprietary customization to their distributions. -If an application or architecture makes use of these features, it can be -difficult to migrate to or use other types of environments. - -If an environment includes non-OpenStack clouds, it may experience -compatibility problems. CMP tools must account for the differences in -the handling of operations and the implementation of services. - -**Possible cloud incompatibilities** - -* Instance deployment -* Network management -* Application management -* Services implementation - -Capacity planning -~~~~~~~~~~~~~~~~~ - -One of the primary reasons many organizations use a hybrid cloud -is to increase capacity without making large capital investments. - -Capacity and the placement of workloads are key design considerations -for hybrid clouds. The long-term capacity plan for these designs must -incorporate growth over time to prevent permanent consumption of more -expensive external clouds. -To avoid this scenario, account for future applications' capacity -requirements and plan growth appropriately. - -It is difficult to predict the amount of load a particular -application might incur if the number of users fluctuates, or the -application experiences an unexpected increase in use. -It is possible to define application requirements in terms of -vCPU, RAM, bandwidth, or other resources and plan appropriately. -However, other clouds might not use the same meter or even the same -oversubscription rates. - -Oversubscription is a method to emulate more capacity than -may physically be present. -For example, a physical hypervisor node with 32 GB RAM may host -24 instances, each provisioned with 2 GB RAM. -As long as all 24 instances do not concurrently use 2 full -gigabytes, this arrangement works well. -However, some hosts take oversubscription to extremes and, -as a result, performance can be inconsistent. -If at all possible, determine what the oversubscription rates -of each host are and plan capacity accordingly. - -Utilization -~~~~~~~~~~~ - -A CMP must be aware of what workloads are running, where they are -running, and their preferred utilizations. -For example, in most cases it is desirable to run as many workloads -internally as possible, utilizing other resources only when necessary. -On the other hand, situations exist in which the opposite is true, -such as when an internal cloud is only for development and stressing -it is undesirable. A cost model of various scenarios and -consideration of internal priorities helps with this decision. -To improve efficiency, automate these decisions when possible. - -The Telemetry service (ceilometer) provides information on the usage -of various OpenStack components. Note the following: - -* If Telemetry must retain a large amount of data, for - example when monitoring a large or active cloud, we recommend - using a NoSQL back end such as MongoDB. -* You must monitor connections to non-OpenStack clouds - and report this information to the CMP. - -Performance -~~~~~~~~~~~ - -Performance is critical to hybrid cloud deployments, and they are -affected by many of the same issues as multi-site deployments, such -as network latency between sites. Also consider the time required to -run a workload in different clouds and methods for reducing this time. -This may require moving data closer to applications or applications -closer to the data they process, and grouping functionality so that -connections that require low latency take place over a single cloud -rather than spanning clouds. -This may also require a CMP that can determine which cloud can most -efficiently run which types of workloads. - -As with utilization, native OpenStack tools help improve performance. -For example, you can use Telemetry to measure performance and the -Orchestration service (heat) to react to changes in demand. - -.. note:: - - Orchestration requires special client configurations to integrate - with Amazon Web Services. For other types of clouds, use CMP features. - -Components -~~~~~~~~~~ - -Using more than one cloud in any design requires consideration of -four OpenStack tools: - -OpenStack Compute (nova) - Regardless of deployment location, hypervisor choice has a direct - effect on how difficult it is to integrate with additional clouds. - -Networking (neutron) - Whether using OpenStack Networking (neutron) or legacy - networking (nova-network), it is necessary to understand - network integration capabilities in order to connect between clouds. - -Telemetry (ceilometer) - Use of Telemetry depends, in large part, on what the other parts - of the cloud you are using. - -Orchestration (heat) - Orchestration can be a valuable tool in orchestrating tasks a - CMP decides are necessary in an OpenStack-based cloud. - -Special considerations -~~~~~~~~~~~~~~~~~~~~~~ - -Hybrid cloud deployments require consideration of two issues that -are not common in other situations: - -Image portability - As of the Kilo release, there is no common image format that is - usable by all clouds. Conversion or recreation of images is necessary - if migrating between clouds. To simplify deployment, use the smallest - and simplest images feasible, install only what is necessary, and - use a deployment manager such as Chef or Puppet. Do not use golden - images to speed up the process unless you repeatedly deploy the same - images on the same cloud. - -API differences - Avoid using a hybrid cloud deployment with more than just - OpenStack (or with different versions of OpenStack) as API changes - can cause compatibility issues. diff --git a/doc/arch-design-to-archive/source/hybrid-user-requirements.rst b/doc/arch-design-to-archive/source/hybrid-user-requirements.rst deleted file mode 100644 index a2134f3e43..0000000000 --- a/doc/arch-design-to-archive/source/hybrid-user-requirements.rst +++ /dev/null @@ -1,178 +0,0 @@ -================= -User requirements -================= - -Hybrid cloud architectures are complex, especially those -that use heterogeneous cloud platforms. -Ensure that design choices match requirements so that the -benefits outweigh the inherent additional complexity and risks. - -Business considerations -~~~~~~~~~~~~~~~~~~~~~~~ - -Business considerations when designing a hybrid cloud deployment ----------------------------------------------------------------- - -Cost - A hybrid cloud architecture involves multiple vendors and - technical architectures. - These architectures may be more expensive to deploy and maintain. - Operational costs can be higher because of the need for more - sophisticated orchestration and brokerage tools than in other architectures. - In contrast, overall operational costs might be lower by - virtue of using a cloud brokerage tool to deploy the - workloads to the most cost effective platform. - -Revenue opportunity - Revenue opportunities vary based on the intent and use case of the cloud. - As a commercial, customer-facing product, you must consider whether building - over multiple platforms makes the design more attractive to customers. - -Time-to-market - One common reason to use cloud platforms is to improve the - time-to-market of a new product or application. - For example, using multiple cloud platforms is viable because - there is an existing investment in several applications. - It is faster to tie the investments together rather than migrate - the components and refactoring them to a single platform. - -Business or technical diversity - Organizations leveraging cloud-based services can embrace business - diversity and utilize a hybrid cloud design to spread their - workloads across multiple cloud providers. This ensures that - no single cloud provider is the sole host for an application. - -Application momentum - Businesses with existing applications may find that it is - more cost effective to integrate applications on multiple - cloud platforms than migrating them to a single platform. - -Workload considerations -~~~~~~~~~~~~~~~~~~~~~~~ - -A workload can be a single application or a suite of applications -that work together. It can also be a duplicate set of applications that -need to run on multiple cloud environments. -In a hybrid cloud deployment, the same workload often needs to function -equally well on radically different public and private cloud environments. -The architecture needs to address these potential conflicts, -complexity, and platform incompatibilities. - -Use cases for a hybrid cloud architecture ------------------------------------------ - -Dynamic resource expansion or bursting - An application that requires additional resources may suit a multiple - cloud architecture. For example, a retailer needs additional resources - during the holiday season, but does not want to add private cloud - resources to meet the peak demand. - The user can accommodate the increased load by bursting to - a public cloud for these peak load periods. These bursts could be - for long or short cycles ranging from hourly to yearly. - -Disaster recovery and business continuity - Cheaper storage makes the public cloud suitable for maintaining - backup applications. - -Federated hypervisor and instance management - Adding self-service, charge back, and transparent delivery of - the resources from a federated pool can be cost effective. - In a hybrid cloud environment, this is a particularly important - consideration. Look for a cloud that provides cross-platform - hypervisor support and robust instance management tools. - -Application portfolio integration - An enterprise cloud delivers efficient application portfolio - management and deployments by leveraging self-service features - and rules according to use. - Integrating existing cloud environments is a common driver - when building hybrid cloud architectures. - -Migration scenarios - Hybrid cloud architecture enables the migration of - applications between different clouds. - -High availability - A combination of locations and platforms enables a level of - availability that is not possible with a single platform. - This approach increases design complexity. - -As running a workload on multiple cloud platforms increases design -complexity, we recommend first exploring options such as transferring -workloads across clouds at the application, instance, cloud platform, -hypervisor, and network levels. - -Tools considerations -~~~~~~~~~~~~~~~~~~~~ - -Hybrid cloud designs must incorporate tools to facilitate working -across multiple clouds. - -Tool functions --------------- - -Broker between clouds - Brokering software evaluates relative costs between different - cloud platforms. Cloud Management Platforms (CMP) - allow the designer to determine the right location for the - workload based on predetermined criteria. - -Facilitate orchestration across the clouds - CMPs simplify the migration of application workloads between - public, private, and hybrid cloud platforms. - We recommend using cloud orchestration tools for managing a diverse - portfolio of systems and applications across multiple cloud platforms. - -Network considerations -~~~~~~~~~~~~~~~~~~~~~~ - -It is important to consider the functionality, security, scalability, -availability, and testability of network when choosing a CMP and cloud -provider. - -* Decide on a network framework and design minimum functionality tests. - This ensures testing and functionality persists during and after - upgrades. -* Scalability across multiple cloud providers may dictate which underlying - network framework you choose in different cloud providers. - It is important to present the network API functions and to verify - that functionality persists across all cloud endpoints chosen. -* High availability implementations vary in functionality and design. - Examples of some common methods are active-hot-standby, active-passive, - and active-active. - Development of high availability and test frameworks is necessary to - insure understanding of functionality and limitations. -* Consider the security of data between the client and the endpoint, - and of traffic that traverses the multiple clouds. - -Risk mitigation and management considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Hybrid cloud architectures introduce additional risk because -they are more complex than a single cloud design and may involve -incompatible components or tools. However, they also reduce -risk by spreading workloads over multiple providers. - -Hybrid cloud risks ------------------- - -Provider availability or implementation details - Business changes can affect provider availability. - Likewise, changes in a provider's service can disrupt - a hybrid cloud environment or increase costs. - -Differing SLAs - Hybrid cloud designs must accommodate differences in SLAs - between providers, and consider their enforceability. - -Security levels - Securing multiple cloud environments is more complex than - securing single cloud environments. We recommend addressing - concerns at the application, network, and cloud platform levels. - Be aware that each cloud platform approaches security differently, - and a hybrid cloud design must address and compensate for these differences. - -Provider API changes - Consumers of external clouds rarely have control over provider - changes to APIs, and changes can break compatibility. - Using only the most common and basic APIs can minimize potential conflicts. diff --git a/doc/arch-design-to-archive/source/hybrid.rst b/doc/arch-design-to-archive/source/hybrid.rst deleted file mode 100644 index b5880767ea..0000000000 --- a/doc/arch-design-to-archive/source/hybrid.rst +++ /dev/null @@ -1,45 +0,0 @@ -====== -Hybrid -====== - -.. toctree:: - :maxdepth: 2 - - hybrid-user-requirements.rst - hybrid-technical-considerations.rst - hybrid-architecture.rst - hybrid-operational-considerations.rst - hybrid-prescriptive-examples.rst - -A :term:`hybrid cloud` design is one that uses more than one cloud. -For example, designs that use both an OpenStack-based private -cloud and an OpenStack-based public cloud, or that use an -OpenStack cloud and a non-OpenStack cloud, are hybrid clouds. - -:term:`Bursting ` describes the practice of creating new instances -in an external cloud to alleviate capacity issues in a private cloud. - -**Example scenarios suited to hybrid clouds** - -* Bursting from a private cloud to a public cloud -* Disaster recovery -* Development and testing -* Federated cloud, enabling users to choose resources from multiple providers -* Supporting legacy systems as they transition to the cloud - -Hybrid clouds interact with systems that are outside the -control of the private cloud administrator, and require -careful architecture to prevent conflicts with hardware, -software, and APIs under external control. - -The degree to which the architecture is OpenStack-based affects your ability -to accomplish tasks with native OpenStack tools. By definition, -this is a situation in which no single cloud can provide all -of the necessary functionality. In order to manage the entire -system, we recommend using a cloud management platform (CMP). - -There are several commercial and open source CMPs available, -but there is no single CMP that can address all needs in all -scenarios, and sometimes a manually-built solution is the best -option. This chapter includes discussion of using CMPs for -managing a hybrid cloud. diff --git a/doc/arch-design-to-archive/source/index.rst b/doc/arch-design-to-archive/source/index.rst deleted file mode 100644 index b18a05fc2d..0000000000 --- a/doc/arch-design-to-archive/source/index.rst +++ /dev/null @@ -1,35 +0,0 @@ -.. meta:: - :description: This guide targets OpenStack Architects - for architectural design - :keywords: Architecture, OpenStack - -=================================== -OpenStack Architecture Design Guide -=================================== - -Abstract -~~~~~~~~ - -To reap the benefits of OpenStack, you should plan, design, -and architect your cloud properly, taking user's needs into -account and understanding the use cases. - -Contents -~~~~~~~~ - -.. toctree:: - :maxdepth: 2 - - common/conventions.rst - introduction.rst - legal-security-requirements.rst - generalpurpose.rst - compute-focus.rst - storage-focus.rst - network-focus.rst - multi-site.rst - hybrid.rst - massively-scalable.rst - specialized.rst - references.rst - common/appendix.rst diff --git a/doc/arch-design-to-archive/source/introduction-how-this-book-is-organized.rst b/doc/arch-design-to-archive/source/introduction-how-this-book-is-organized.rst deleted file mode 100644 index b008dad540..0000000000 --- a/doc/arch-design-to-archive/source/introduction-how-this-book-is-organized.rst +++ /dev/null @@ -1,33 +0,0 @@ -How this book is organized -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -This book examines some of the most common uses for OpenStack clouds, -and explains the considerations for each use case. Cloud architects may -use this book as a comprehensive guide by reading all of the use cases, -but it is also possible to review only the chapters which pertain to a -specific use case. The use cases covered in this guide include: - -* :doc:`General purpose`: Uses common components that - address 80% of common use cases. - -* :doc:`Compute focused`: For compute intensive workloads - such as high performance computing (HPC). - -* :doc:`Storage focused`: For storage intensive workloads - such as data analytics with parallel file systems. - -* :doc:`Network focused`: For high performance and - reliable networking, such as a :term:`content delivery network (CDN)`. - -* :doc:`Multi-site`: For applications that require multiple - site deployments for geographical, reliability or data locality - reasons. - -* :doc:`Hybrid cloud`: Uses multiple disparate clouds connected - either for failover, hybrid cloud bursting, or availability. - -* :doc:`Massively scalable`: For cloud service - providers or other large installations. - -* :doc:`Specialized cases`: Architectures that have not - previously been covered in the defined use cases. diff --git a/doc/arch-design-to-archive/source/introduction-how-this-book-was-written.rst b/doc/arch-design-to-archive/source/introduction-how-this-book-was-written.rst deleted file mode 100644 index fa0635aef6..0000000000 --- a/doc/arch-design-to-archive/source/introduction-how-this-book-was-written.rst +++ /dev/null @@ -1,55 +0,0 @@ -Why and how we wrote this book -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -We wrote this book to guide you through designing an OpenStack cloud -architecture. This guide identifies design considerations for common -cloud use cases and provides examples. - -The Architecture Design Guide was written in a book sprint format, which -is a facilitated, rapid development production method for books. The -Book Sprint was facilitated by Faith Bosworth and Adam Hyde of Book -Sprints, for more information, see the Book Sprints website -(www.booksprints.net). - -This book was written in five days during July 2014 while exhausting the -M&M, Mountain Dew and healthy options supply, complete with juggling -entertainment during lunches at VMware's headquarters in Palo Alto. - -We would like to thank VMware for their generous hospitality, as well as -our employers, Cisco, Cloudscaling, Comcast, EMC, Mirantis, Rackspace, -Red Hat, Verizon, and VMware, for enabling us to contribute our time. We -would especially like to thank Anne Gentle and Kenneth Hui for all of -their shepherding and organization in making this happen. - -The author team includes: - -* Kenneth Hui (EMC) `@hui\_kenneth `__ - -* Alexandra Settle (Rackspace) - `@dewsday `__ - -* Anthony Veiga (Comcast) `@daaelar `__ - -* Beth Cohen (Verizon) `@bfcohen `__ - -* Kevin Jackson (Rackspace) - `@itarchitectkev `__ - -* Maish Saidel-Keesing (Cisco) - `@maishsk `__ - -* Nick Chase (Mirantis) `@NickChase `__ - -* Scott Lowe (VMware) `@scott\_lowe `__ - -* Sean Collins (Comcast) `@sc68cal `__ - -* Sean Winn (Cloudscaling) - `@seanmwinn `__ - -* Sebastian Gutierrez (Red Hat) `@gutseb `__ - -* Stephen Gordon (Red Hat) `@xsgordon `__ - -* Vinny Valdez (Red Hat) - `@VinnyValdez `__ diff --git a/doc/arch-design-to-archive/source/introduction-intended-audience.rst b/doc/arch-design-to-archive/source/introduction-intended-audience.rst deleted file mode 100644 index 6779234c3a..0000000000 --- a/doc/arch-design-to-archive/source/introduction-intended-audience.rst +++ /dev/null @@ -1,11 +0,0 @@ -Intended audience -~~~~~~~~~~~~~~~~~ - -This book has been written for architects and designers of OpenStack -clouds. For a guide on deploying and operating OpenStack, please refer -to the `OpenStack Operations Guide `_. - -Before reading this book, we recommend prior knowledge of cloud -architecture and principles, experience in enterprise system design, -Linux and virtualization experience, and a basic understanding of -networking principles and protocols. diff --git a/doc/arch-design-to-archive/source/introduction-methodology.rst b/doc/arch-design-to-archive/source/introduction-methodology.rst deleted file mode 100644 index 3d37bf882b..0000000000 --- a/doc/arch-design-to-archive/source/introduction-methodology.rst +++ /dev/null @@ -1,146 +0,0 @@ -Methodology -~~~~~~~~~~~ - -The best way to design your cloud architecture is through creating and -testing use cases. Planning for applications that support thousands of -sessions per second, variable workloads, and complex, changing data, -requires you to identify the key meters. Identifying these key meters, -such as number of concurrent transactions per second, and size of -database, makes it possible to build a method for testing your -assumptions. - -Use a functional user scenario to develop test cases, and to measure -overall project trajectory. - -.. note:: - - If you do not want to use an application to develop user - requirements automatically, you need to create requirements to build - test harnesses and develop usable meters. - -Establishing these meters allows you to respond to changes quickly -without having to set exact requirements in advance. This creates ways -to configure the system, rather than redesigning it every time there is -a requirements change. - -.. important:: - - It is important to limit scope creep. Ensure you address tool - limitations, but do not recreate the entire suite of tools. Work - with technical product owners to establish critical features that - are needed for a successful cloud deployment. - -Application cloud readiness ---------------------------- - -The cloud does more than host virtual machines and their applications. -This *lift and shift* approach works in certain situations, but there is -a fundamental difference between clouds and traditional bare-metal-based -environments, or even traditional virtualized environments. - -In traditional environments, with traditional enterprise applications, -the applications and the servers that run on them are *pets*. They are -lovingly crafted and cared for, the servers have names like Gandalf or -Tardis, and if they get sick someone nurses them back to health. All of -this is designed so that the application does not experience an outage. - -In cloud environments, servers are more like cattle. There are thousands -of them, they get names like NY-1138-Q, and if they get sick, they get -put down and a sysadmin installs another one. Traditional applications -that are unprepared for this kind of environment may suffer outages, -loss of data, or complete failure. - -There are other reasons to design applications with the cloud in mind. -Some are defensive, such as the fact that because applications cannot be -certain of exactly where or on what hardware they will be launched, they -need to be flexible, or at least adaptable. Others are proactive. For -example, one of the advantages of using the cloud is scalability. -Applications need to be designed in such a way that they can take -advantage of these and other opportunities. - -Determining whether an application is cloud-ready -------------------------------------------------- - -There are several factors to take into consideration when looking at -whether an application is a good fit for the cloud. - -Structure - A large, monolithic, single-tiered, legacy application typically is - not a good fit for the cloud. Efficiencies are gained when load can - be spread over several instances, so that a failure in one part of - the system can be mitigated without affecting other parts of the - system, or so that scaling can take place where the app needs it. - -Dependencies - Applications that depend on specific hardware, such as a particular - chip set or an external device such as a fingerprint reader, might - not be a good fit for the cloud, unless those dependencies are - specifically addressed. Similarly, if an application depends on an - operating system or set of libraries that cannot be used in the - cloud, or cannot be virtualized, that is a problem. - -Connectivity - Self-contained applications, or those that depend on resources that - are not reachable by the cloud in question, will not run. In some - situations, you can work around these issues with custom network - setup, but how well this works depends on the chosen cloud - environment. - -Durability and resilience - Despite the existence of SLAs, things break: servers go down, - network connections are disrupted, or too many projects on a server - make a server unusable. An application must be sturdy enough to - contend with these issues. - -Designing for the cloud ------------------------ - -Here are some guidelines to keep in mind when designing an application -for the cloud: - -* Be a pessimist: Assume everything fails and design backwards. - -* Put your eggs in multiple baskets: Leverage multiple providers, - geographic regions and availability zones to accommodate for local - availability issues. Design for portability. - -* Think efficiency: Inefficient designs will not scale. Efficient - designs become cheaper as they scale. Kill off unneeded components or - capacity. - -* Be paranoid: Design for defense in depth and zero tolerance by - building in security at every level and between every component. - Trust no one. - -* But not too paranoid: Not every application needs the platinum - solution. Architect for different SLA's, service tiers, and security - levels. - -* Manage the data: Data is usually the most inflexible and complex area - of a cloud and cloud integration architecture. Do not short change - the effort in analyzing and addressing data needs. - -* Hands off: Leverage automation to increase consistency and quality - and reduce response times. - -* Divide and conquer: Pursue partitioning and parallel layering - wherever possible. Make components as small and portable as possible. - Use load balancing between layers. - -* Think elasticity: Increasing resources should result in a - proportional increase in performance and scalability. Decreasing - resources should have the opposite effect. - -* Be dynamic: Enable dynamic configuration changes such as auto - scaling, failure recovery and resource discovery to adapt to changing - environments, faults, and workload volumes. - -* Stay close: Reduce latency by moving highly interactive components - and data near each other. - -* Keep it loose: Loose coupling, service interfaces, separation of - concerns, abstraction, and well defined API's deliver flexibility. - -* Be cost aware: Autoscaling, data transmission, virtual software - licenses, reserved instances, and similar costs can rapidly increase - monthly usage charges. Monitor usage closely. diff --git a/doc/arch-design-to-archive/source/introduction.rst b/doc/arch-design-to-archive/source/introduction.rst deleted file mode 100644 index ef8b8e34c2..0000000000 --- a/doc/arch-design-to-archive/source/introduction.rst +++ /dev/null @@ -1,15 +0,0 @@ -============ -Introduction -============ - -.. toctree:: - :maxdepth: 2 - - introduction-intended-audience.rst - introduction-how-this-book-is-organized.rst - introduction-how-this-book-was-written.rst - introduction-methodology.rst - -:term:`OpenStack` is a fully-featured, self-service cloud. This book takes you -through some of the considerations you have to make when designing your -cloud. diff --git a/doc/arch-design-to-archive/source/legal-security-requirements.rst b/doc/arch-design-to-archive/source/legal-security-requirements.rst deleted file mode 100644 index f84a1409ff..0000000000 --- a/doc/arch-design-to-archive/source/legal-security-requirements.rst +++ /dev/null @@ -1,254 +0,0 @@ -=============================== -Security and legal requirements -=============================== - -This chapter discusses the legal and security requirements you -need to consider for the different OpenStack scenarios. - -Legal requirements -~~~~~~~~~~~~~~~~~~ - -Many jurisdictions have legislative and regulatory -requirements governing the storage and management of data in -cloud environments. Common areas of regulation include: - -* Data retention policies ensuring storage of persistent data - and records management to meet data archival requirements. -* Data ownership policies governing the possession and - responsibility for data. -* Data sovereignty policies governing the storage of data in - foreign countries or otherwise separate jurisdictions. -* Data compliance policies governing certain types of - information needing to reside in certain locations due to - regulatory issues - and more importantly, cannot reside in - other locations for the same reason. - -Examples of such legal frameworks include the -`data protection framework `_ -of the European Union and the requirements of the -`Financial Industry Regulatory Authority -`_ -in the United States. -Consult a local regulatory body for more information. - -.. _security: - -Security -~~~~~~~~ - -When deploying OpenStack in an enterprise as a private cloud, -despite activating a firewall and binding employees with security -agreements, cloud architecture should not make assumptions about -safety and protection. -In addition to considering the users, operators, or administrators -who will use the environment, consider also negative or hostile users who -would attack or compromise the security of your deployment regardless -of firewalls or security agreements. - -Attack vectors increase further in a public facing OpenStack deployment. -For example, the API endpoints and the software behind it become -vulnerable to hostile entities attempting to gain unauthorized access -or prevent access to services. -This can result in loss of reputation and you must protect against -it through auditing and appropriate filtering. - -It is important to understand that user authentication requests -encase sensitive information such as user names, passwords, and -authentication tokens. For this reason, place the API services -behind hardware that performs SSL termination. - -.. warning:: - - Be mindful of consistency when utilizing third party - clouds to explore authentication options. - -Security domains -~~~~~~~~~~~~~~~~ - -A security domain comprises users, applications, servers or networks -that share common trust requirements and expectations within a system. -Typically, security domains have the same authentication and -authorization requirements and users. - -You can map security domains individually to the installation, -or combine them. For example, some deployment topologies combine both -guest and data domains onto one physical network. -In other cases these networks are physically separate. -Map out the security domains against specific OpenStack topologies needs. -The domains and their trust requirements depend on whether the cloud -instance is public, private, or hybrid. - -Public security domains ------------------------ - -The public security domain is an untrusted area of the cloud -infrastructure. It can refer to the internet as a whole or simply -to networks over which the user has no authority. -Always consider this domain untrusted. For example, -in a hybrid cloud deployment, any information traversing between and -beyond the clouds is in the public domain and untrustworthy. - -Guest security domains ----------------------- - -Typically used for compute instance-to-instance traffic, the -guest security domain handles compute data generated by -instances on the cloud but not services that support the -operation of the cloud, such as API calls. Public cloud -providers and private cloud providers who do not have -stringent controls on instance use or who allow unrestricted -internet access to instances should consider this domain to be -untrusted. Private cloud providers may want to consider this -network as internal and therefore trusted only if they have -controls in place to assert that they trust instances and all -their projects. - -Management security domains ---------------------------- - -The management security domain is where services interact. -The networks in this domain transport confidential data such as -configuration parameters, user names, and passwords. Trust this -domain when it is behind an organization's firewall in deployments. - -Data security domains ---------------------- - -The data security domain is concerned primarily with -information pertaining to the storage services within OpenStack. -The data that crosses this network has integrity and -confidentiality requirements. Depending on the type of deployment there -may also be availability requirements. The trust level of this network -is heavily dependent on deployment decisions and does not have a default -level of trust. - -Hypervisor-security -~~~~~~~~~~~~~~~~~~~ - -The hypervisor also requires a security assessment. In a -public cloud, organizations typically do not have control -over the choice of hypervisor. Properly securing your -hypervisor is important. Attacks made upon the -unsecured hypervisor are called a **hypervisor breakout**. -Hypervisor breakout describes the event of a -compromised or malicious instance breaking out of the resource -controls of the hypervisor and gaining access to the bare -metal operating system and hardware resources. - -There is not an issue if the security of instances is not important. -However, enterprises need to avoid vulnerability. The only way to -do this is to avoid the situation where the instances are running -on a public cloud. That does not mean that there is a -need to own all of the infrastructure on which an OpenStack -installation operates; it suggests avoiding situations in which -sharing hardware with others occurs. - -Baremetal security -~~~~~~~~~~~~~~~~~~ - -There are other services worth considering that provide a -bare metal instance instead of a cloud. In other cases, it is -possible to replicate a second private cloud by integrating -with a private Cloud-as-a-Service deployment. The -organization does not buy the hardware, but also does not share -with other projects. It is also possible to use a provider that -hosts a bare-metal public cloud instance for which the -hardware is dedicated only to one customer, or a provider that -offers private Cloud-as-a-Service. - -.. important:: - - Each cloud implements services differently. - What keeps data secure in one cloud may not do the same in another. - Be sure to know the security requirements of every cloud that - handles the organization's data or workloads. - -More information on OpenStack Security can be found in the -`OpenStack Security Guide `_. - -Networking security -~~~~~~~~~~~~~~~~~~~ - -Consider security implications and requirements before designing the -physical and logical network topologies. Make sure that the networks are -properly segregated and traffic flows are going to the correct -destinations without crossing through locations that are undesirable. -Consider the following example factors: - -* Firewalls -* Overlay interconnects for joining separated project networks -* Routing through or avoiding specific networks - -How networks attach to hypervisors can expose security -vulnerabilities. To mitigate against exploiting hypervisor breakouts, -separate networks from other systems and schedule instances for the -network onto dedicated compute nodes. This prevents attackers -from having access to the networks from a compromised instance. - -Multi-site security -~~~~~~~~~~~~~~~~~~~ - -Securing a multi-site OpenStack installation brings -extra challenges. Projects may expect a project-created network -to be secure. In a multi-site installation the use of a -non-private connection between sites may be required. This may -mean that traffic would be visible to third parties and, in -cases where an application requires security, this issue -requires mitigation. In these instances, install a VPN or -encrypted connection between sites to conceal sensitive traffic. - -Another security consideration with regard to multi-site -deployments is Identity. Centralize authentication within a -multi-site deployment. Centralization provides a -single authentication point for users across the deployment, -as well as a single point of administration for traditional -create, read, update, and delete operations. Centralized -authentication is also useful for auditing purposes because -all authentication tokens originate from the same source. - -Just as projects in a single-site deployment need isolation -from each other, so do projects in multi-site installations. -The extra challenges in multi-site designs revolve around -ensuring that project networks function across regions. -OpenStack Networking (neutron) does not presently support -a mechanism to provide this functionality, therefore an -external system may be necessary to manage these mappings. -Project networks may contain sensitive information requiring -that this mapping be accurate and consistent to ensure that a -project in one site does not connect to a different project in -another site. - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -Most OpenStack installations require a bare minimum set of -pieces to function. These include OpenStack Identity -(keystone) for authentication, OpenStack Compute -(nova) for compute, OpenStack Image service (glance) for image -storage, OpenStack Networking (neutron) for networking, and -potentially an object store in the form of OpenStack Object -Storage (swift). Bringing multi-site into play also demands extra -components in order to coordinate between regions. Centralized -Identity service is necessary to provide the single authentication -point. Centralized dashboard is also recommended to provide a -single login point and a mapped experience to the API and CLI -options available. If needed, use a centralized Object Storage service, -installing the required swift proxy service alongside the Object -Storage service. - -It may also be helpful to install a few extra options in -order to facilitate certain use cases. For instance, -installing DNS service may assist in automatically generating -DNS domains for each region with an automatically-populated -zone full of resource records for each instance. This -facilitates using DNS as a mechanism for determining which -region would be selected for certain applications. - -Another useful tool for managing a multi-site installation -is Orchestration (heat). The Orchestration service allows -the use of templates to define a set of instances to be launched -together or for scaling existing sets. -It can set up matching or differentiated groupings based on regions. -For instance, if an application requires an equally balanced -number of nodes across sites, the same heat template can be used -to cover each site with small alterations to only the region name. diff --git a/doc/arch-design-to-archive/source/massively-scalable-operational-considerations.rst b/doc/arch-design-to-archive/source/massively-scalable-operational-considerations.rst deleted file mode 100644 index 16e2eac788..0000000000 --- a/doc/arch-design-to-archive/source/massively-scalable-operational-considerations.rst +++ /dev/null @@ -1,85 +0,0 @@ -Operational considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In order to run efficiently at massive scale, automate as many of the -operational processes as possible. Automation includes the configuration of -provisioning, monitoring and alerting systems. Part of the automation process -includes the capability to determine when human intervention is required and -who should act. The objective is to decrease the ratio of operational staff to -running systems as much as possible in order to reduce maintenance costs. In a -massively scaled environment, it is very difficult for staff to give each -system individual care. - -Configuration management tools such as Puppet and Chef enable operations staff -to categorize systems into groups based on their roles and thus create -configurations and system states that the provisioning system enforces. -Systems that fall out of the defined state due to errors or failures are -quickly removed from the pool of active nodes and replaced. - -At large scale the resource cost of diagnosing failed individual systems is -far greater than the cost of replacement. It is more economical to replace the -failed system with a new system, provisioning and configuring it automatically -and adding it to the pool of active nodes. By automating tasks that are -labor-intensive, repetitive, and critical to operations, cloud operations -teams can work more efficiently because fewer resources are required for these -common tasks. Administrators are then free to tackle tasks that are not easy -to automate and that have longer-term impacts on the business, for example, -capacity planning. - -The bleeding edge ------------------ - -Running OpenStack at massive scale requires striking a balance between -stability and features. For example, it might be tempting to run an older -stable release branch of OpenStack to make deployments easier. However, when -running at massive scale, known issues that may be of some concern or only -have minimal impact in smaller deployments could become pain points. Recent -releases may address well known issues. The OpenStack community can help -resolve reported issues by applying the collective expertise of the OpenStack -developers. - -The number of organizations running at massive scales is a small proportion of -the OpenStack community, therefore it is important to share related issues -with the community and be a vocal advocate for resolving them. Some issues -only manifest when operating at large scale, and the number of organizations -able to duplicate and validate an issue is small, so it is important to -document and dedicate resources to their resolution. - -In some cases, the resolution to the problem is ultimately to deploy a more -recent version of OpenStack. Alternatively, when you must resolve an issue in -a production environment where rebuilding the entire environment is not an -option, it is sometimes possible to deploy updates to specific underlying -components in order to resolve issues or gain significant performance -improvements. Although this may appear to expose the deployment to increased -risk and instability, in many cases it could be an undiscovered issue. - -We recommend building a development and operations organization that is -responsible for creating desired features, diagnosing and resolving issues, -and building the infrastructure for large scale continuous integration tests -and continuous deployment. This helps catch bugs early and makes deployments -faster and easier. In addition to development resources, we also recommend the -recruitment of experts in the fields of message queues, databases, distributed -systems, networking, cloud, and storage. - -Growth and capacity planning ----------------------------- - -An important consideration in running at massive scale is projecting growth -and utilization trends in order to plan capital expenditures for the short and -long term. Gather utilization meters for compute, network, and storage, along -with historical records of these meters. While securing major anchor projects -can lead to rapid jumps in the utilization rates of all resources, the steady -adoption of the cloud inside an organization or by consumers in a public -offering also creates a steady trend of increased utilization. - -Skills and training -------------------- - -Projecting growth for storage, networking, and compute is only one aspect of a -growth plan for running OpenStack at massive scale. Growing and nurturing -development and operational staff is an additional consideration. Sending team -members to OpenStack conferences, meetup events, and encouraging active -participation in the mailing lists and committees is a very important way to -maintain skills and forge relationships in the community. For a list of -OpenStack training providers in the marketplace, see the `Openstack Marketplace -`_. diff --git a/doc/arch-design-to-archive/source/massively-scalable-technical-considerations.rst b/doc/arch-design-to-archive/source/massively-scalable-technical-considerations.rst deleted file mode 100644 index ab167320cb..0000000000 --- a/doc/arch-design-to-archive/source/massively-scalable-technical-considerations.rst +++ /dev/null @@ -1,110 +0,0 @@ -Technical considerations -~~~~~~~~~~~~~~~~~~~~~~~~ - -Repurposing an existing OpenStack environment to be massively scalable is a -formidable task. When building a massively scalable environment from the -ground up, ensure you build the initial deployment with the same principles -and choices that apply as the environment grows. For example, a good approach -is to deploy the first site as a multi-site environment. This enables you to -use the same deployment and segregation methods as the environment grows to -separate locations across dedicated links or wide area networks. In a -hyperscale cloud, scale trumps redundancy. Modify applications with this in -mind, relying on the scale and homogeneity of the environment to provide -reliability rather than redundant infrastructure provided by non-commodity -hardware solutions. - -Infrastructure segregation --------------------------- - -OpenStack services support massive horizontal scale. Be aware that this is -not the case for the entire supporting infrastructure. This is particularly a -problem for the database management systems and message queues that OpenStack -services use for data storage and remote procedure call communications. - -Traditional clustering techniques typically provide high availability and some -additional scale for these environments. In the quest for massive scale, -however, you must take additional steps to relieve the performance pressure on -these components in order to prevent them from negatively impacting the -overall performance of the environment. Ensure that all the components are in -balance so that if the massively scalable environment fails, all the -components are near maximum capacity and a single component is not causing the -failure. - -Regions segregate completely independent installations linked only by an -Identity and Dashboard (optional) installation. Services have separate API -endpoints for each region, and include separate database and queue -installations. This exposes some awareness of the environment's fault domains -to users and gives them the ability to ensure some degree of application -resiliency while also imposing the requirement to specify which region to -apply their actions to. - -Environments operating at massive scale typically need their regions or sites -subdivided further without exposing the requirement to specify the failure -domain to the user. This provides the ability to further divide the -installation into failure domains while also providing a logical unit for -maintenance and the addition of new hardware. At hyperscale, instead of adding -single compute nodes, administrators can add entire racks or even groups of -racks at a time with each new addition of nodes exposed via one of the -segregation concepts mentioned herein. - -:term:`Cells ` provide the ability to subdivide the compute portion of -an OpenStack installation, including regions, while still exposing a single -endpoint. Each region has an API cell along with a number of compute cells -where the workloads actually run. Each cell has its own database and message -queue setup (ideally clustered), providing the ability to subdivide the load -on these subsystems, improving overall performance. - -Each compute cell provides a complete compute installation, complete with full -database and queue installations, scheduler, conductor, and multiple compute -hosts. The cells scheduler handles placement of user requests from the single -API endpoint to a specific cell from those available. The normal filter -scheduler then handles placement within the cell. - -Unfortunately, Compute is the only OpenStack service that provides good -support for cells. In addition, cells do not adequately support some standard -OpenStack functionality such as security groups and host aggregates. Due to -their relative newness and specialized use, cells receive relatively little -testing in the OpenStack gate. Despite these issues, cells play an important -role in well known OpenStack installations operating at massive scale, such as -those at CERN and Rackspace. - -Host aggregates ---------------- - -Host aggregates enable partitioning of OpenStack Compute deployments into -logical groups for load balancing and instance distribution. You can also use -host aggregates to further partition an availability zone. Consider a cloud -which might use host aggregates to partition an availability zone into groups -of hosts that either share common resources, such as storage and network, or -have a special property, such as trusted computing hardware. You cannot target -host aggregates explicitly. Instead, select instance flavors that map to host -aggregate metadata. These flavors target host aggregates implicitly. - -Availability zones ------------------- - -Availability zones provide another mechanism for subdividing an installation -or region. They are, in effect, host aggregates exposed for (optional) -explicit targeting by users. - -Unlike cells, availability zones do not have their own database server or -queue broker but represent an arbitrary grouping of compute nodes. Typically, -nodes are grouped into availability zones using a shared failure domain based -on a physical characteristic such as a shared power source or physical network -connections. Users can target exposed availability zones; however, this is not -a requirement. An alternative approach is to set a default availability zone -to schedule instances to a non-default availability zone of nova. - -Segregation example -------------------- - -In this example, the cloud is divided into two regions, an API cell and -three child cells for each region, with three availability zones in each -cell based on the power layout of the data centers. -The below figure describes the relationship between them within one region. - -.. figure:: figures/Massively_Scalable_Cells_regions_azs.png - -A number of host aggregates enable targeting of virtual machine instances -using flavors, that require special capabilities shared by the target hosts -such as SSDs, 10 GbE networks, or GPU cards. diff --git a/doc/arch-design-to-archive/source/massively-scalable-user-requirements.rst b/doc/arch-design-to-archive/source/massively-scalable-user-requirements.rst deleted file mode 100644 index 553007a119..0000000000 --- a/doc/arch-design-to-archive/source/massively-scalable-user-requirements.rst +++ /dev/null @@ -1,91 +0,0 @@ -User requirements -~~~~~~~~~~~~~~~~~ - -Defining user requirements for a massively scalable OpenStack design -architecture dictates approaching the design from two different, yet sometimes -opposing, perspectives: the cloud user, and the cloud operator. The -expectations and perceptions of the consumption and management of resources of -a massively scalable OpenStack cloud from these two perspectives are -distinctly different. - -Massively scalable OpenStack clouds have the following user requirements: - -* The cloud user expects repeatable, dependable, and deterministic processes - for launching and deploying cloud resources. You could deliver this through - a web-based interface or publicly available API endpoints. All appropriate - options for requesting cloud resources must be available through some type - of user interface, a command-line interface (CLI), or API endpoints. - -* Cloud users expect a fully self-service and on-demand consumption model. - When an OpenStack cloud reaches the massively scalable size, expect - consumption as a service in each and every way. - -* For a user of a massively scalable OpenStack public cloud, there are no - expectations for control over security, performance, or availability. Users - expect only SLAs related to uptime of API services, and very basic SLAs for - services offered. It is the user's responsibility to address these issues on - their own. The exception to this expectation is the rare case of a massively - scalable cloud infrastructure built for a private or government organization - that has specific requirements. - -The cloud user's requirements and expectations that determine the cloud design -focus on the consumption model. The user expects to consume cloud resources in -an automated and deterministic way, without any need for knowledge of the -capacity, scalability, or other attributes of the cloud's underlying -infrastructure. - -Operator requirements ---------------------- - -While the cloud user can be completely unaware of the underlying -infrastructure of the cloud and its attributes, the operator must build and -support the infrastructure for operating at scale. This presents a very -demanding set of requirements for building such a cloud from the operator's -perspective: - -* Everything must be capable of automation. For example, everything from - compute hardware, storage hardware, networking hardware, to the installation - and configuration of the supporting software. Manual processes are - impractical in a massively scalable OpenStack design architecture. - -* The cloud operator requires that capital expenditure (CapEx) is minimized at - all layers of the stack. Operators of massively scalable OpenStack clouds - require the use of dependable commodity hardware and freely available open - source software components to reduce deployment costs and operational - expenses. Initiatives like OpenCompute (more information available at - `Open Compute Project `_) - provide additional information and pointers. To - cut costs, many operators sacrifice redundancy. For example, using redundant - power supplies, network connections, and rack switches. - -* Companies operating a massively scalable OpenStack cloud also require that - operational expenditures (OpEx) be minimized as much as possible. We - recommend using cloud-optimized hardware when managing operational overhead. - Some of the factors to consider include power, cooling, and the physical - design of the chassis. Through customization, it is possible to optimize the - hardware and systems for this type of workload because of the scale of these - implementations. - -* Massively scalable OpenStack clouds require extensive metering and - monitoring functionality to maximize the operational efficiency by keeping - the operator informed about the status and state of the infrastructure. This - includes full scale metering of the hardware and software status. A - corresponding framework of logging and alerting is also required to store - and enable operations to act on the meters provided by the metering and - monitoring solutions. The cloud operator also needs a solution that uses the - data provided by the metering and monitoring solution to provide capacity - planning and capacity trending analysis. - -* Invariably, massively scalable OpenStack clouds extend over several sites. - Therefore, the user-operator requirements for a multi-site OpenStack - architecture design are also applicable here. This includes various legal - requirements; other jurisdictional legal or compliance requirements; image - consistency-availability; storage replication and availability (both block - and file/object storage); and authentication, authorization, and auditing - (AAA). See :doc:`multi-site` for more details on requirements and - considerations for multi-site OpenStack clouds. - -* The design architecture of a massively scalable OpenStack cloud must address - considerations around physical facilities such as space, floor weight, rack - height and type, environmental considerations, power usage and power usage - efficiency (PUE), and physical security. diff --git a/doc/arch-design-to-archive/source/massively-scalable.rst b/doc/arch-design-to-archive/source/massively-scalable.rst deleted file mode 100644 index 701f45cc9b..0000000000 --- a/doc/arch-design-to-archive/source/massively-scalable.rst +++ /dev/null @@ -1,57 +0,0 @@ -================== -Massively scalable -================== - -.. toctree:: - :maxdepth: 2 - - massively-scalable-user-requirements.rst - massively-scalable-technical-considerations.rst - massively-scalable-operational-considerations.rst - -A massively scalable architecture is a cloud implementation -that is either a very large deployment, such as a commercial -service provider might build, or one that has the capability -to support user requests for large amounts of cloud resources. - -An example is an infrastructure in which requests to service -500 or more instances at a time is common. A massively scalable -infrastructure fulfills such a request without exhausting the -available cloud infrastructure resources. While the high capital -cost of implementing such a cloud architecture means that it -is currently in limited use, many organizations are planning for -massive scalability in the future. - -A massively scalable OpenStack cloud design presents a unique -set of challenges and considerations. For the most part it is -similar to a general purpose cloud architecture, as it is built -to address a non-specific range of potential use cases or -functions. Typically, it is rare that particular workloads determine -the design or configuration of massively scalable clouds. The -massively scalable cloud is most often built as a platform for -a variety of workloads. Because private organizations rarely -require or have the resources for them, massively scalable -OpenStack clouds are generally built as commercial, public -cloud offerings. - -Services provided by a massively scalable OpenStack cloud -include: - -* Virtual-machine disk image library -* Raw block storage -* File or object storage -* Firewall functionality -* Load balancing functionality -* Private (non-routable) and public (floating) IP addresses -* Virtualized network topologies -* Software bundles -* Virtual compute resources - -Like a general purpose cloud, the instances deployed in a -massively scalable OpenStack cloud do not necessarily use -any specific aspect of the cloud offering (compute, network, or storage). -As the cloud grows in scale, the number of workloads can cause -stress on all the cloud components. This adds further stresses -to supporting infrastructure such as databases and message brokers. -The architecture design for such a cloud must account for these -performance pressures without negatively impacting user experience. diff --git a/doc/arch-design-to-archive/source/multi-site-architecture.rst b/doc/arch-design-to-archive/source/multi-site-architecture.rst deleted file mode 100644 index 885694a421..0000000000 --- a/doc/arch-design-to-archive/source/multi-site-architecture.rst +++ /dev/null @@ -1,118 +0,0 @@ -============ -Architecture -============ - -:ref:`ms-openstack-architecture` illustrates a high level multi-site -OpenStack architecture. Each site is an OpenStack cloud but it may be -necessary to architect the sites on different versions. For example, -if the second site is intended to be a replacement for the first site, -they would be different. Another common design would be a private -OpenStack cloud with a replicated site that would be used for high -availability or disaster recovery. The most important design decision -is configuring storage as a single shared pool or separate pools, depending -on user and technical requirements. - -.. _ms-openstack-architecture: - -.. figure:: figures/Multi-Site_shared_keystone_horizon_swift1.png - - **Multi-site OpenStack architecture** - - -OpenStack services architecture -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The Identity service, which is used by all other OpenStack components -for authorization and the catalog of service endpoints, supports the -concept of regions. A region is a logical construct used to group -OpenStack services in close proximity to one another. The concept of -regions is flexible; it may contain OpenStack service endpoints located -within a distinct geographic region or regions. It may be smaller in -scope, where a region is a single rack within a data center, with -multiple regions existing in adjacent racks in the same data center. - -The majority of OpenStack components are designed to run within the -context of a single region. The Compute service is designed to manage -compute resources within a region, with support for subdivisions of -compute resources by using availability zones and cells. The Networking -service can be used to manage network resources in the same broadcast -domain or collection of switches that are linked. The OpenStack Block -Storage service controls storage resources within a region with all -storage resources residing on the same storage network. Like the -OpenStack Compute service, the OpenStack Block Storage service also -supports the availability zone construct which can be used to subdivide -storage resources. - -The OpenStack dashboard, OpenStack Identity, and OpenStack Object -Storage services are components that can each be deployed centrally in -order to serve multiple regions. - -Storage -~~~~~~~ - -With multiple OpenStack regions, it is recommended to configure a single -OpenStack Object Storage service endpoint to deliver shared file storage -for all regions. The Object Storage service internally replicates files -to multiple nodes which can be used by applications or workloads in -multiple regions. This simplifies high availability failover and -disaster recovery rollback. - -In order to scale the Object Storage service to meet the workload of -multiple regions, multiple proxy workers are run and load-balanced, -storage nodes are installed in each region, and the entire Object -Storage Service can be fronted by an HTTP caching layer. This is done so -client requests for objects can be served out of caches rather than -directly from the storage modules themselves, reducing the actual load -on the storage network. In addition to an HTTP caching layer, use a -caching layer like Memcache to cache objects between the proxy and -storage nodes. - -If the cloud is designed with a separate Object Storage service endpoint -made available in each region, applications are required to handle -synchronization (if desired) and other management operations to ensure -consistency across the nodes. For some applications, having multiple -Object Storage Service endpoints located in the same region as the -application may be desirable due to reduced latency, cross region -bandwidth, and ease of deployment. - -.. note:: - - For the Block Storage service, the most important decisions are the - selection of the storage technology, and whether a dedicated network - is used to carry storage traffic from the storage service to the - compute nodes. - -Networking -~~~~~~~~~~ - -When connecting multiple regions together, there are several design -considerations. The overlay network technology choice determines how -packets are transmitted between regions and how the logical network and -addresses present to the application. If there are security or -regulatory requirements, encryption should be implemented to secure the -traffic between regions. For networking inside a region, the overlay -network technology for project networks is equally important. The overlay -technology and the network traffic that an application generates or -receives can be either complementary or serve cross purposes. For -example, using an overlay technology for an application that transmits a -large amount of small packets could add excessive latency or overhead to -each packet if not configured properly. - -Dependencies -~~~~~~~~~~~~ - -The architecture for a multi-site OpenStack installation is dependent on -a number of factors. One major dependency to consider is storage. When -designing the storage system, the storage mechanism needs to be -determined. Once the storage type is determined, how it is accessed is -critical. For example, we recommend that storage should use a dedicated -network. Another concern is how the storage is configured to protect the -data. For example, the Recovery Point Objective (RPO) and the Recovery -Time Objective (RTO). How quickly recovery from a fault can be -completed, determines how often the replication of data is required. -Ensure that enough storage is allocated to support the data protection -strategy. - -Networking decisions include the encapsulation mechanism that can be -used for the project networks, how large the broadcast domains should be, -and the contracted SLAs for the interconnects. diff --git a/doc/arch-design-to-archive/source/multi-site-operational-considerations.rst b/doc/arch-design-to-archive/source/multi-site-operational-considerations.rst deleted file mode 100644 index df5385a36e..0000000000 --- a/doc/arch-design-to-archive/source/multi-site-operational-considerations.rst +++ /dev/null @@ -1,156 +0,0 @@ -========================== -Operational considerations -========================== - -Multi-site OpenStack cloud deployment using regions requires that the -service catalog contains per-region entries for each service deployed -other than the Identity service. Most off-the-shelf OpenStack deployment -tools have limited support for defining multiple regions in this -fashion. - -Deployers should be aware of this and provide the appropriate -customization of the service catalog for their site either manually, or -by customizing deployment tools in use. - -.. note:: - - As of the Kilo release, documentation for implementing this feature - is in progress. See this bug for more information: - https://bugs.launchpad.net/openstack-manuals/+bug/1340509. - -Licensing -~~~~~~~~~ - -Multi-site OpenStack deployments present additional licensing -considerations over and above regular OpenStack clouds, particularly -where site licenses are in use to provide cost efficient access to -software licenses. The licensing for host operating systems, guest -operating systems, OpenStack distributions (if applicable), -software-defined infrastructure including network controllers and -storage systems, and even individual applications need to be evaluated. - -Topics to consider include: - -* The definition of what constitutes a site in the relevant licenses, - as the term does not necessarily denote a geographic or otherwise - physically isolated location. - -* Differentiations between "hot" (active) and "cold" (inactive) sites, - where significant savings may be made in situations where one site is - a cold standby for disaster recovery purposes only. - -* Certain locations might require local vendors to provide support and - services for each site which may vary with the licensing agreement in - place. - -Logging and monitoring -~~~~~~~~~~~~~~~~~~~~~~ - -Logging and monitoring does not significantly differ for a multi-site -OpenStack cloud. The tools described in the `Logging and monitoring -chapter `__ -of the OpenStack Operations Guide remain applicable. Logging and monitoring -can be provided on a per-site basis, and in a common centralized location. - -When attempting to deploy logging and monitoring facilities to a -centralized location, care must be taken with the load placed on the -inter-site networking links. - -Upgrades -~~~~~~~~ - -In multi-site OpenStack clouds deployed using regions, sites are -independent OpenStack installations which are linked together using -shared centralized services such as OpenStack Identity. At a high level -the recommended order of operations to upgrade an individual OpenStack -environment is (see the `Upgrades -chapter `__ -of the OpenStack Operations Guide for details): - -#. Upgrade the OpenStack Identity service (keystone). - -#. Upgrade the OpenStack Image service (glance). - -#. Upgrade OpenStack Compute (nova), including networking components. - -#. Upgrade OpenStack Block Storage (cinder). - -#. Upgrade the OpenStack dashboard (horizon). - -The process for upgrading a multi-site environment is not significantly -different: - -#. Upgrade the shared OpenStack Identity service (keystone) deployment. - -#. Upgrade the OpenStack Image service (glance) at each site. - -#. Upgrade OpenStack Compute (nova), including networking components, at - each site. - -#. Upgrade OpenStack Block Storage (cinder) at each site. - -#. Upgrade the OpenStack dashboard (horizon), at each site or in the - single central location if it is shared. - -Compute upgrades within each site can also be performed in a rolling -fashion. Compute controller services (API, Scheduler, and Conductor) can -be upgraded prior to upgrading of individual compute nodes. This allows -operations staff to keep a site operational for users of Compute -services while performing an upgrade. - -Quota management -~~~~~~~~~~~~~~~~ - -Quotas are used to set operational limits to prevent system capacities -from being exhausted without notification. They are currently enforced -at the project level rather than at the user level. - -Quotas are defined on a per-region basis. Operators can define identical -quotas for projects in each region of the cloud to provide a consistent -experience, or even create a process for synchronizing allocated quotas -across regions. It is important to note that only the operational limits -imposed by the quotas will be aligned consumption of quotas by users -will not be reflected between regions. - -For example, given a cloud with two regions, if the operator grants a -user a quota of 25 instances in each region then that user may launch a -total of 50 instances spread across both regions. They may not, however, -launch more than 25 instances in any single region. - -For more information on managing quotas refer to the `Managing projects -and users -chapter `__ -of the OpenStack Operators Guide. - -Policy management -~~~~~~~~~~~~~~~~~ - -OpenStack provides a default set of Role Based Access Control (RBAC) -policies, defined in a ``policy.json`` file, for each service. Operators -edit these files to customize the policies for their OpenStack -installation. If the application of consistent RBAC policies across -sites is a requirement, then it is necessary to ensure proper -synchronization of the ``policy.json`` files to all installations. - -This must be done using system administration tools such as rsync as -functionality for synchronizing policies across regions is not currently -provided within OpenStack. - -Documentation -~~~~~~~~~~~~~ - -Users must be able to leverage cloud infrastructure and provision new -resources in the environment. It is important that user documentation is -accessible by users to ensure they are given sufficient information to -help them leverage the cloud. As an example, by default OpenStack -schedules instances on a compute node automatically. However, when -multiple regions are available, the end user needs to decide in which -region to schedule the new instance. The dashboard presents the user -with the first region in your configuration. The API and CLI tools do -not execute commands unless a valid region is specified. It is therefore -important to provide documentation to your users describing the region -layout as well as calling out that quotas are region-specific. If a user -reaches his or her quota in one region, OpenStack does not automatically -build new instances in another. Documenting specific examples helps -users understand how to operate the cloud, thereby reducing calls and -tickets filed with the help desk. diff --git a/doc/arch-design-to-archive/source/multi-site-prescriptive-examples.rst b/doc/arch-design-to-archive/source/multi-site-prescriptive-examples.rst deleted file mode 100644 index 609461d2d7..0000000000 --- a/doc/arch-design-to-archive/source/multi-site-prescriptive-examples.rst +++ /dev/null @@ -1,192 +0,0 @@ -===================== -Prescriptive examples -===================== - -There are multiple ways to build a multi-site OpenStack installation, -based on the needs of the intended workloads. Below are example -architectures based on different requirements. These examples are meant -as a reference, and not a hard and fast rule for deployments. Use the -previous sections of this chapter to assist in selecting specific -components and implementations based on specific needs. - -A large content provider needs to deliver content to customers that are -geographically dispersed. The workload is very sensitive to latency and -needs a rapid response to end-users. After reviewing the user, technical -and operational considerations, it is determined beneficial to build a -number of regions local to the customer's edge. Rather than build a few -large, centralized data centers, the intent of the architecture is to -provide a pair of small data centers in locations that are closer to the -customer. In this use case, spreading applications out allows for -different horizontal scaling than a traditional compute workload scale. -The intent is to scale by creating more copies of the application in -closer proximity to the users that need it most, in order to ensure -faster response time to user requests. This provider deploys two -datacenters at each of the four chosen regions. The implications of this -design are based around the method of placing copies of resources in -each of the remote regions. Swift objects, Glance images, and block -storage need to be manually replicated into each region. This may be -beneficial for some systems, such as the case of content service, where -only some of the content needs to exist in some but not all regions. A -centralized Keystone is recommended to ensure authentication and that -access to the API endpoints is easily manageable. - -It is recommended that you install an automated DNS system such as -Designate. Application administrators need a way to manage the mapping -of which application copy exists in each region and how to reach it, -unless an external Dynamic DNS system is available. Designate assists by -making the process automatic and by populating the records in the each -region's zone. - -Telemetry for each region is also deployed, as each region may grow -differently or be used at a different rate. Ceilometer collects each -region's meters from each of the controllers and report them back to a -central location. This is useful both to the end user and the -administrator of the OpenStack environment. The end user will find this -method useful, as it makes possible to determine if certain locations -are experiencing higher load than others, and take appropriate action. -Administrators also benefit by possibly being able to forecast growth -per region, rather than expanding the capacity of all regions -simultaneously, therefore maximizing the cost-effectiveness of the -multi-site design. - -One of the key decisions of running this infrastructure is whether or -not to provide a redundancy model. Two types of redundancy and high -availability models in this configuration can be implemented. The first -type is the availability of central OpenStack components. Keystone can -be made highly available in three central data centers that host the -centralized OpenStack components. This prevents a loss of any one of the -regions causing an outage in service. It also has the added benefit of -being able to run a central storage repository as a primary cache for -distributing content to each of the regions. - -The second redundancy type is the edge data center itself. A second data -center in each of the edge regional locations house a second region near -the first region. This ensures that the application does not suffer -degraded performance in terms of latency and availability. - -:ref:`ms-customer-edge` depicts the solution designed to have both a -centralized set of core data centers for OpenStack services and paired edge -data centers: - -.. _ms-customer-edge: - -.. figure:: figures/Multi-Site_Customer_Edge.png - - **Multi-site architecture example** - -Geo-redundant load balancing -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -A large-scale web application has been designed with cloud principles in -mind. The application is designed provide service to application store, -on a 24/7 basis. The company has typical two tier architecture with a -web front-end servicing the customer requests, and a NoSQL database back -end storing the information. - -As of late there has been several outages in number of major public -cloud providers due to applications running out of a single geographical -location. The design therefore should mitigate the chance of a single -site causing an outage for their business. - -The solution would consist of the following OpenStack components: - -* A firewall, switches and load balancers on the public facing network - connections. - -* OpenStack Controller services running, Networking, dashboard, Block - Storage and Compute running locally in each of the three regions. - Identity service, Orchestration service, Telemetry service, Image - service and Object Storage service can be installed centrally, with - nodes in each of the region providing a redundant OpenStack - Controller plane throughout the globe. - -* OpenStack compute nodes running the KVM hypervisor. - -* OpenStack Object Storage for serving static objects such as images - can be used to ensure that all images are standardized across all the - regions, and replicated on a regular basis. - -* A distributed DNS service available to all regions that allows for - dynamic update of DNS records of deployed instances. - -* A geo-redundant load balancing service can be used to service the - requests from the customers based on their origin. - -An autoscaling heat template can be used to deploy the application in -the three regions. This template includes: - -* Web Servers, running Apache. - -* Appropriate ``user_data`` to populate the central DNS servers upon - instance launch. - -* Appropriate Telemetry alarms that maintain state of the application - and allow for handling of region or instance failure. - -Another autoscaling Heat template can be used to deploy a distributed -MongoDB shard over the three locations, with the option of storing -required data on a globally available swift container. According to the -usage and load on the database server, additional shards can be -provisioned according to the thresholds defined in Telemetry. - -Two data centers would have been sufficient had the requirements been -met. But three regions are selected here to avoid abnormal load on a -single region in the event of a failure. - -Orchestration is used because of the built-in functionality of -autoscaling and auto healing in the event of increased load. Additional -configuration management tools, such as Puppet or Chef could also have -been used in this scenario, but were not chosen since Orchestration had -the appropriate built-in hooks into the OpenStack cloud, whereas the -other tools were external and not native to OpenStack. In addition, -external tools were not needed since this deployment scenario was -straight forward. - -OpenStack Object Storage is used here to serve as a back end for the -Image service since it is the most suitable solution for a globally -distributed storage solution with its own replication mechanism. Home -grown solutions could also have been used including the handling of -replication, but were not chosen, because Object Storage is already an -intricate part of the infrastructure and a proven solution. - -An external load balancing service was used and not the LBaaS in -OpenStack because the solution in OpenStack is not redundant and does -not have any awareness of geo location. - -.. _ms-geo-redundant: - -.. figure:: figures/Multi-site_Geo_Redundant_LB.png - - **Multi-site geo-redundant architecture** - -Location-local service -~~~~~~~~~~~~~~~~~~~~~~ - -A common use for multi-site OpenStack deployment is creating a Content -Delivery Network. An application that uses a location-local architecture -requires low network latency and proximity to the user to provide an -optimal user experience and reduce the cost of bandwidth and transit. -The content resides on sites closer to the customer, instead of a -centralized content store that requires utilizing higher cost -cross-country links. - -This architecture includes a geo-location component that places user -requests to the closest possible node. In this scenario, 100% redundancy -of content across every site is a goal rather than a requirement, with -the intent to maximize the amount of content available within a minimum -number of network hops for end users. Despite these differences, the -storage replication configuration has significant overlap with that of a -geo-redundant load balancing use case. - -In :ref:`ms-shared-keystone`, the application utilizing this multi-site -OpenStack install that is location-aware would launch web server or content -serving instances on the compute cluster in each site. Requests from clients -are first sent to a global services load balancer that determines the location -of the client, then routes the request to the closest OpenStack site where the -application completes the request. - -.. _ms-shared-keystone: - -.. figure:: figures/Multi-Site_shared_keystone1.png - - **Multi-site shared keystone architecture** diff --git a/doc/arch-design-to-archive/source/multi-site-technical-considerations.rst b/doc/arch-design-to-archive/source/multi-site-technical-considerations.rst deleted file mode 100644 index 5554921319..0000000000 --- a/doc/arch-design-to-archive/source/multi-site-technical-considerations.rst +++ /dev/null @@ -1,164 +0,0 @@ -======================== -Technical considerations -======================== - -There are many technical considerations to take into account with regard -to designing a multi-site OpenStack implementation. An OpenStack cloud -can be designed in a variety of ways to handle individual application -needs. A multi-site deployment has additional challenges compared to -single site installations and therefore is a more complex solution. - -When determining capacity options be sure to take into account not just -the technical issues, but also the economic or operational issues that -might arise from specific decisions. - -Inter-site link capacity describes the capabilities of the connectivity -between the different OpenStack sites. This includes parameters such as -bandwidth, latency, whether or not a link is dedicated, and any business -policies applied to the connection. The capability and number of the -links between sites determine what kind of options are available for -deployment. For example, if two sites have a pair of high-bandwidth -links available between them, it may be wise to configure a separate -storage replication network between the two sites to support a single -Swift endpoint and a shared Object Storage capability between them. An -example of this technique, as well as a configuration walk-through, is -available at `Dedicated replication network -`_. -Another option in this scenario is to build a dedicated set of project -private networks across the secondary link, using overlay networks with -a third party mapping the site overlays to each other. - -The capacity requirements of the links between sites is driven by -application behavior. If the link latency is too high, certain -applications that use a large number of small packets, for example RPC -calls, may encounter issues communicating with each other or operating -properly. Additionally, OpenStack may encounter similar types of issues. -To mitigate this, Identity service call timeouts can be tuned to prevent -issues authenticating against a central Identity service. - -Another network capacity consideration for a multi-site deployment is -the amount and performance of overlay networks available for project -networks. If using shared project networks across zones, it is imperative -that an external overlay manager or controller be used to map these -overlays together. It is necessary to ensure the amount of possible IDs -between the zones are identical. - -.. note:: - - As of the Kilo release, OpenStack Networking was not capable of - managing tunnel IDs across installations. So if one site runs out of - IDs, but another does not, that project's network is unable to reach - the other site. - -Capacity can take other forms as well. The ability for a region to grow -depends on scaling out the number of available compute nodes. This topic -is covered in greater detail in the section for compute-focused -deployments. However, it may be necessary to grow cells in an individual -region, depending on the size of your cluster and the ratio of virtual -machines per hypervisor. - -A third form of capacity comes in the multi-region-capable components of -OpenStack. Centralized Object Storage is capable of serving objects -through a single namespace across multiple regions. Since this works by -accessing the object store through swift proxy, it is possible to -overload the proxies. There are two options available to mitigate this -issue: - -* Deploy a large number of swift proxies. The drawback is that the - proxies are not load-balanced and a large file request could - continually hit the same proxy. - -* Add a caching HTTP proxy and load balancer in front of the swift - proxies. Since swift objects are returned to the requester via HTTP, - this load balancer would alleviate the load required on the swift - proxies. - -Utilization -~~~~~~~~~~~ - -While constructing a multi-site OpenStack environment is the goal of -this guide, the real test is whether an application can utilize it. - -The Identity service is normally the first interface for OpenStack users -and is required for almost all major operations within OpenStack. -Therefore, it is important that you provide users with a single URL for -Identity service authentication, and document the configuration of -regions within the Identity service. Each of the sites defined in your -installation is considered to be a region in Identity nomenclature. This -is important for the users, as it is required to define the region name -when providing actions to an API endpoint or in the dashboard. - -Load balancing is another common issue with multi-site installations. -While it is still possible to run HAproxy instances with -Load-Balancer-as-a-Service, these are defined to a specific region. Some -applications can manage this using internal mechanisms. Other -applications may require the implementation of an external system, -including global services load balancers or anycast-advertised DNS. - -Depending on the storage model chosen during site design, storage -replication and availability are also a concern for end-users. If an -application can support regions, then it is possible to keep the object -storage system separated by region. In this case, users who want to have -an object available to more than one region need to perform cross-site -replication. However, with a centralized swift proxy, the user may need -to benchmark the replication timing of the Object Storage back end. -Benchmarking allows the operational staff to provide users with an -understanding of the amount of time required for a stored or modified -object to become available to the entire environment. - -Performance -~~~~~~~~~~~ - -Determining the performance of a multi-site installation involves -considerations that do not come into play in a single-site deployment. -Being a distributed deployment, performance in multi-site deployments -may be affected in certain situations. - -Since multi-site systems can be geographically separated, there may be -greater latency or jitter when communicating across regions. This can -especially impact systems like the OpenStack Identity service when -making authentication attempts from regions that do not contain the -centralized Identity implementation. It can also affect applications -which rely on Remote Procedure Call (RPC) for normal operation. An -example of this can be seen in high performance computing workloads. - -Storage availability can also be impacted by the architecture of a -multi-site deployment. A centralized Object Storage service requires -more time for an object to be available to instances locally in regions -where the object was not created. Some applications may need to be tuned -to account for this effect. Block Storage does not currently have a -method for replicating data across multiple regions, so applications -that depend on available block storage need to manually cope with this -limitation by creating duplicate block storage entries in each region. - -OpenStack components -~~~~~~~~~~~~~~~~~~~~ - -Most OpenStack installations require a bare minimum set of pieces to -function. These include the OpenStack Identity (keystone) for -authentication, OpenStack Compute (nova) for compute, OpenStack Image -service (glance) for image storage, OpenStack Networking (neutron) for -networking, and potentially an object store in the form of OpenStack -Object Storage (swift). Deploying a multi-site installation also demands -extra components in order to coordinate between regions. A centralized -Identity service is necessary to provide the single authentication -point. A centralized dashboard is also recommended to provide a single -login point and a mapping to the API and CLI options available. A -centralized Object Storage service may also be used, but will require -the installation of the swift proxy service. - -It may also be helpful to install a few extra options in order to -facilitate certain use cases. For example, installing Designate may -assist in automatically generating DNS domains for each region with an -automatically-populated zone full of resource records for each instance. -This facilitates using DNS as a mechanism for determining which region -will be selected for certain applications. - -Another useful tool for managing a multi-site installation is -Orchestration (heat). The Orchestration service allows the use of -templates to define a set of instances to be launched together or for -scaling existing sets. It can also be used to set up matching or -differentiated groupings based on regions. For instance, if an -application requires an equally balanced number of nodes across sites, -the same heat template can be used to cover each site with small -alterations to only the region name. diff --git a/doc/arch-design-to-archive/source/multi-site-user-requirements.rst b/doc/arch-design-to-archive/source/multi-site-user-requirements.rst deleted file mode 100644 index 9b5449e28d..0000000000 --- a/doc/arch-design-to-archive/source/multi-site-user-requirements.rst +++ /dev/null @@ -1,168 +0,0 @@ -================= -User requirements -================= - -Workload characteristics -~~~~~~~~~~~~~~~~~~~~~~~~ - -An understanding of the expected workloads for a desired multi-site -environment and use case is an important factor in the decision-making -process. In this context, ``workload`` refers to the way the systems are -used. A workload could be a single application or a suite of -applications that work together. It could also be a duplicate set of -applications that need to run in multiple cloud environments. Often in a -multi-site deployment, the same workload will need to work identically -in more than one physical location. - -This multi-site scenario likely includes one or more of the other -scenarios in this book with the additional requirement of having the -workloads in two or more locations. The following are some possible -scenarios: - -For many use cases the proximity of the user to their workloads has a -direct influence on the performance of the application and therefore -should be taken into consideration in the design. Certain applications -require zero to minimal latency that can only be achieved by deploying -the cloud in multiple locations. These locations could be in different -data centers, cities, countries or geographical regions, depending on -the user requirement and location of the users. - -Consistency of images and templates across different sites -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -It is essential that the deployment of instances is consistent across -the different sites and built into the infrastructure. If the OpenStack -Object Storage is used as a back end for the Image service, it is -possible to create repositories of consistent images across multiple -sites. Having central endpoints with multiple storage nodes allows -consistent centralized storage for every site. - -Not using a centralized object store increases the operational overhead -of maintaining a consistent image library. This could include -development of a replication mechanism to handle the transport of images -and the changes to the images across multiple sites. - -High availability -~~~~~~~~~~~~~~~~~ - -If high availability is a requirement to provide continuous -infrastructure operations, a basic requirement of high availability -should be defined. - -The OpenStack management components need to have a basic and minimal -level of redundancy. The simplest example is the loss of any single site -should have minimal impact on the availability of the OpenStack -services. - -The `OpenStack High Availability -Guide `_ contains more information -on how to provide redundancy for the OpenStack components. - -Multiple network links should be deployed between sites to provide -redundancy for all components. This includes storage replication, which -should be isolated to a dedicated network or VLAN with the ability to -assign QoS to control the replication traffic or provide priority for -this traffic. Note that if the data store is highly changeable, the -network requirements could have a significant effect on the operational -cost of maintaining the sites. - -The ability to maintain object availability in both sites has -significant implications on the object storage design and -implementation. It also has a significant impact on the WAN network -design between the sites. - -Connecting more than two sites increases the challenges and adds more -complexity to the design considerations. Multi-site implementations -require planning to address the additional topology used for internal -and external connectivity. Some options include full mesh topology, hub -spoke, spine leaf, and 3D Torus. - -If applications running in a cloud are not cloud-aware, there should be -clear measures and expectations to define what the infrastructure can -and cannot support. An example would be shared storage between sites. It -is possible, however such a solution is not native to OpenStack and -requires a third-party hardware vendor to fulfill such a requirement. -Another example can be seen in applications that are able to consume -resources in object storage directly. These applications need to be -cloud aware to make good use of an OpenStack Object Store. - -Application readiness -~~~~~~~~~~~~~~~~~~~~~ - -Some applications are tolerant of the lack of synchronized object -storage, while others may need those objects to be replicated and -available across regions. Understanding how the cloud implementation -impacts new and existing applications is important for risk mitigation, -and the overall success of a cloud project. Applications may have to be -written or rewritten for an infrastructure with little to no redundancy, -or with the cloud in mind. - -Cost -~~~~ - -A greater number of sites increase cost and complexity for a multi-site -deployment. Costs can be broken down into the following categories: - -* Compute resources - -* Networking resources - -* Replication - -* Storage - -* Management - -* Operational costs - -Site loss and recovery -~~~~~~~~~~~~~~~~~~~~~~ - -Outages can cause partial or full loss of site functionality. Strategies -should be implemented to understand and plan for recovery scenarios. - -* The deployed applications need to continue to function and, more - importantly, you must consider the impact on the performance and - reliability of the application when a site is unavailable. - -* It is important to understand what happens to the replication of - objects and data between the sites when a site goes down. If this - causes queues to start building up, consider how long these queues - can safely exist until an error occurs. - -* After an outage, ensure the method for resuming proper operations of - a site is implemented when it comes back online. We recommend you - architect the recovery to avoid race conditions. - -Compliance and geo-location -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -An organization may have certain legal obligations and regulatory -compliance measures which could require certain workloads or data to not -be located in certain regions. - -Auditing -~~~~~~~~ - -A well thought-out auditing strategy is important in order to be able to -quickly track down issues. Keeping track of changes made to security -groups and project changes can be useful in rolling back the changes if -they affect production. For example, if all security group rules for a -project disappeared, the ability to quickly track down the issue would be -important for operational and legal reasons. - -Separation of duties -~~~~~~~~~~~~~~~~~~~~ - -A common requirement is to define different roles for the different -cloud administration functions. An example would be a requirement to -segregate the duties and permissions by site. - -Authentication between sites -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -It is recommended to have a single authentication domain rather than a -separate implementation for each and every site. This requires an -authentication mechanism that is highly available and distributed to -ensure continuous operation. Authentication server locality might be -required and should be planned for. diff --git a/doc/arch-design-to-archive/source/multi-site.rst b/doc/arch-design-to-archive/source/multi-site.rst deleted file mode 100644 index 09cb56d1b4..0000000000 --- a/doc/arch-design-to-archive/source/multi-site.rst +++ /dev/null @@ -1,26 +0,0 @@ -========== -Multi-site -========== - -.. toctree:: - :maxdepth: 2 - - multi-site-user-requirements.rst - multi-site-technical-considerations.rst - multi-site-operational-considerations.rst - multi-site-architecture.rst - multi-site-prescriptive-examples.rst - -OpenStack is capable of running in a multi-region configuration. This -enables some parts of OpenStack to effectively manage a group of sites -as a single cloud. - -Some use cases that might indicate a need for a multi-site deployment of -OpenStack include: - -* An organization with a diverse geographic footprint. - -* Geo-location sensitive data. - -* Data locality, in which specific data or functionality should be - close to users. diff --git a/doc/arch-design-to-archive/source/network-focus-architecture.rst b/doc/arch-design-to-archive/source/network-focus-architecture.rst deleted file mode 100644 index 31c8b1eeaa..0000000000 --- a/doc/arch-design-to-archive/source/network-focus-architecture.rst +++ /dev/null @@ -1,184 +0,0 @@ -Architecture -~~~~~~~~~~~~ - -Network-focused OpenStack architectures have many similarities to other -OpenStack architecture use cases. There are several factors to consider -when designing for a network-centric or network-heavy application -environment. - -Networks exist to serve as a medium of transporting data between -systems. It is inevitable that an OpenStack design has -inter-dependencies with non-network portions of OpenStack as well as on -external systems. Depending on the specific workload, there may be major -interactions with storage systems both within and external to the -OpenStack environment. For example, in the case of content delivery -network, there is twofold interaction with storage. Traffic flows to and -from the storage array for ingesting and serving content in a -north-south direction. In addition, there is replication traffic flowing -in an east-west direction. - -Compute-heavy workloads may also induce interactions with the network. -Some high performance compute applications require network-based memory -mapping and data sharing and, as a result, induce a higher network load -when they transfer results and data sets. Others may be highly -transactional and issue transaction locks, perform their functions, and -revoke transaction locks at high rates. This also has an impact on the -network performance. - -Some network dependencies are external to OpenStack. While OpenStack -Networking is capable of providing network ports, IP addresses, some -level of routing, and overlay networks, there are some other functions -that it cannot provide. For many of these, you may require external -systems or equipment to fill in the functional gaps. Hardware load -balancers are an example of equipment that may be necessary to -distribute workloads or offload certain functions. OpenStack Networking -provides a tunneling feature, however it is constrained to a -Networking-managed region. If the need arises to extend a tunnel beyond -the OpenStack region to either another region or an external system, -implement the tunnel itself outside OpenStack or use a tunnel management -system to map the tunnel or overlay to an external tunnel. - -Depending on the selected design, Networking itself might not support -the required :term:`layer-3 network` functionality. If -you choose to use the provider networking mode without running the layer-3 -agent, you must install an external router to provide layer-3 connectivity -to outside systems. - -Interaction with orchestration services is inevitable in larger-scale -deployments. The Orchestration service is capable of allocating network -resource defined in templates to map to project networks and for port -creation, as well as allocating floating IPs. If there is a requirement -to define and manage network resources when using orchestration, we -recommend that the design include the Orchestration service to meet the -demands of users. - -Design impacts --------------- - -A wide variety of factors can affect a network-focused OpenStack -architecture. While there are some considerations shared with a general -use case, specific workloads related to network requirements influence -network design decisions. - -One decision includes whether or not to use Network Address Translation -(NAT) and where to implement it. If there is a requirement for floating -IPs instead of public fixed addresses then you must use NAT. An example -of this is a DHCP relay that must know the IP of the DHCP server. In -these cases it is easier to automate the infrastructure to apply the -target IP to a new instance rather than to reconfigure legacy or -external systems for each new instance. - -NAT for floating IPs managed by Networking resides within the hypervisor -but there are also versions of NAT that may be running elsewhere. If -there is a shortage of IPv4 addresses there are two common methods to -mitigate this externally to OpenStack. The first is to run a load -balancer either within OpenStack as an instance, or use an external load -balancing solution. In the internal scenario, Networking's -Load-Balancer-as-a-Service (LBaaS) can manage load balancing software, -for example HAproxy. This is specifically to manage the Virtual IP (VIP) -while a dual-homed connection from the HAproxy instance connects the -public network with the project private network that hosts all of the -content servers. In the external scenario, a load balancer needs to -serve the VIP and also connect to the project overlay network through -external means or through private addresses. - -Another kind of NAT that may be useful is protocol NAT. In some cases it -may be desirable to use only IPv6 addresses on instances and operate -either an instance or an external service to provide a NAT-based -transition technology such as NAT64 and DNS64. This provides the ability -to have a globally routable IPv6 address while only consuming IPv4 -addresses as necessary or in a shared manner. - -Application workloads affect the design of the underlying network -architecture. If a workload requires network-level redundancy, the -routing and switching architecture have to accommodate this. There are -differing methods for providing this that are dependent on the selected -network hardware, the performance of the hardware, and which networking -model you deploy. Examples include Link aggregation (LAG) and Hot -Standby Router Protocol (HSRP). Also consider whether to deploy -OpenStack Networking or legacy networking (nova-network), and which -plug-in to select for OpenStack Networking. If using an external system, -configure Networking to run :term:`layer-2` with a provider -network configuration. For example, implement HSRP to terminate layer-3 -connectivity. - -Depending on the workload, overlay networks may not be the best -solution. Where application network connections are small, short lived, -or bursty, running a dynamic overlay can generate as much bandwidth as -the packets it carries. It also can induce enough latency to cause -issues with certain applications. There is an impact to the device -generating the overlay which, in most installations, is the hypervisor. -This causes performance degradation on packet per second and connection -per second rates. - -Overlays also come with a secondary option that may not be appropriate -to a specific workload. While all of them operate in full mesh by -default, there might be good reasons to disable this function because it -may cause excessive overhead for some workloads. Conversely, other -workloads operate without issue. For example, most web services -applications do not have major issues with a full mesh overlay network, -while some network monitoring tools or storage replication workloads -have performance issues with throughput or excessive broadcast traffic. - -Many people overlook an important design decision: The choice of layer-3 -protocols. While OpenStack was initially built with only IPv4 support, -Networking now supports IPv6 and dual-stacked networks. Some workloads -are possible through the use of IPv6 and IPv6 to IPv4 reverse transition -mechanisms such as NAT64 and DNS64 or :term:`6to4`. This alters the -requirements for any address plan as single-stacked and transitional IPv6 -deployments can alleviate the need for IPv4 addresses. - -OpenStack has limited support for dynamic routing, however there are a -number of options available by incorporating third party solutions to -implement routing within the cloud including network equipment, hardware -nodes, and instances. Some workloads perform well with nothing more than -static routes and default gateways configured at the layer-3 termination -point. In most cases this is sufficient, however some cases require the -addition of at least one type of dynamic routing protocol if not -multiple protocols. Having a form of interior gateway protocol (IGP) -available to the instances inside an OpenStack installation opens up the -possibility of use cases for anycast route injection for services that -need to use it as a geographic location or failover mechanism. Other -applications may wish to directly participate in a routing protocol, -either as a passive observer, as in the case of a looking glass, or as -an active participant in the form of a route reflector. Since an -instance might have a large amount of compute and memory resources, it -is trivial to hold an entire unpartitioned routing table and use it to -provide services such as network path visibility to other applications -or as a monitoring tool. - -Path maximum transmission unit (MTU) failures are lesser known but -harder to diagnose. The MTU must be large enough to handle normal -traffic, overhead from an overlay network, and the desired layer-3 -protocol. Adding externally built tunnels reduces the MTU packet size. -In this case, you must pay attention to the fully calculated MTU size -because some systems ignore or drop path MTU discovery packets. - -Tunable networking components ------------------------------ - -Consider configurable networking components related to an OpenStack -architecture design when designing for network intensive workloads that -include MTU and QoS. Some workloads require a larger MTU than normal due -to the transfer of large blocks of data. When providing network service -for applications such as video streaming or storage replication, we -recommend that you configure both OpenStack hardware nodes and the -supporting network equipment for jumbo frames where possible. This -allows for better use of available bandwidth. Configure jumbo frames -across the complete path the packets traverse. If one network component -is not capable of handling jumbo frames then the entire path reverts to -the default MTU. - -:term:`Quality of Service (QoS)` also has a great impact on network intensive -workloads as it provides instant service to packets which have a higher -priority due to the impact of poor network performance. In applications -such as Voice over IP (VoIP), differentiated services code points are a -near requirement for proper operation. You can also use QoS in the -opposite direction for mixed workloads to prevent low priority but high -bandwidth applications, for example backup services, video conferencing, -or file sharing, from blocking bandwidth that is needed for the proper -operation of other workloads. It is possible to tag file storage traffic -as a lower class, such as best effort or scavenger, to allow the higher -priority traffic through. In cases where regions within a cloud might be -geographically distributed it may also be necessary to plan accordingly -to implement WAN optimization to combat latency or packet loss. diff --git a/doc/arch-design-to-archive/source/network-focus-operational-considerations.rst b/doc/arch-design-to-archive/source/network-focus-operational-considerations.rst deleted file mode 100644 index 79a13e0c3c..0000000000 --- a/doc/arch-design-to-archive/source/network-focus-operational-considerations.rst +++ /dev/null @@ -1,64 +0,0 @@ -Operational considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Network-focused OpenStack clouds have a number of operational -considerations that influence the selected design, including: - -* Dynamic routing of static routes - -* Service level agreements (SLAs) - -* Ownership of user management - -An initial network consideration is the selection of a telecom company -or transit provider. - -Make additional design decisions about monitoring and alarming. This can -be an internal responsibility or the responsibility of the external -provider. In the case of using an external provider, service level -agreements (SLAs) likely apply. In addition, other operational -considerations such as bandwidth, latency, and jitter can be part of an -SLA. - -Consider the ability to upgrade the infrastructure. As demand for -network resources increase, operators add additional IP address blocks -and add additional bandwidth capacity. In addition, consider managing -hardware and software lifecycle events, for example upgrades, -decommissioning, and outages, while avoiding service interruptions for -projects. - -Factor maintainability into the overall network design. This includes -the ability to manage and maintain IP addresses as well as the use of -overlay identifiers including VLAN tag IDs, GRE tunnel IDs, and MPLS -tags. As an example, if you may need to change all of the IP addresses -on a network, a process known as renumbering, then the design must -support this function. - -Address network-focused applications when considering certain -operational realities. For example, consider the impending exhaustion of -IPv4 addresses, the migration to IPv6, and the use of private networks -to segregate different types of traffic that an application receives or -generates. In the case of IPv4 to IPv6 migrations, applications should -follow best practices for storing IP addresses. We recommend you avoid -relying on IPv4 features that did not carry over to the IPv6 protocol or -have differences in implementation. - -To segregate traffic, allow applications to create a private project -network for database and storage network traffic. Use a public network -for services that require direct client access from the internet. Upon -segregating the traffic, consider :term:`quality of service (QoS)` and -security to ensure each network has the required level of service. - -Finally, consider the routing of network traffic. For some applications, -develop a complex policy framework for routing. To create a routing -policy that satisfies business requirements, consider the economic cost -of transmitting traffic over expensive links versus cheaper links, in -addition to bandwidth, latency, and jitter requirements. - -Additionally, consider how to respond to network events. As an example, -how load transfers from one link to another during a failure scenario -could be a factor in the design. If you do not plan network capacity -correctly, failover traffic could overwhelm other ports or network links -and create a cascading failure scenario. In this case, traffic that -fails over to one link overwhelms that link and then moves to the -subsequent links until all network traffic stops. diff --git a/doc/arch-design-to-archive/source/network-focus-prescriptive-examples.rst b/doc/arch-design-to-archive/source/network-focus-prescriptive-examples.rst deleted file mode 100644 index 4d8dc6d84e..0000000000 --- a/doc/arch-design-to-archive/source/network-focus-prescriptive-examples.rst +++ /dev/null @@ -1,165 +0,0 @@ -Prescriptive examples -~~~~~~~~~~~~~~~~~~~~~ - -An organization designs a large-scale web application with cloud -principles in mind. The application scales horizontally in a bursting -fashion and generates a high instance count. The application requires an -SSL connection to secure data and must not lose connection state to -individual servers. - -The figure below depicts an example design for this workload. In this -example, a hardware load balancer provides SSL offload functionality and -connects to project networks in order to reduce address consumption. This -load balancer links to the routing architecture as it services the VIP -for the application. The router and load balancer use the GRE tunnel ID -of the application's project network and an IP address within the project -subnet but outside of the address pool. This is to ensure that the load -balancer can communicate with the application's HTTP servers without -requiring the consumption of a public IP address. - -Because sessions persist until closed, the routing and switching -architecture provides high availability. Switches mesh to each -hypervisor and each other, and also provide an MLAG implementation to -ensure that layer-2 connectivity does not fail. Routers use VRRP and -fully mesh with switches to ensure layer-3 connectivity. Since GRE is -provides an overlay network, Networking is present and uses the Open -vSwitch agent in GRE tunnel mode. This ensures all devices can reach all -other devices and that you can create project networks for private -addressing links to the load balancer. - -.. figure:: figures/Network_Web_Services1.png - -A web service architecture has many options and optional components. Due -to this, it can fit into a large number of other OpenStack designs. A -few key components, however, need to be in place to handle the nature of -most web-scale workloads. You require the following components: - -* OpenStack Controller services (Image, Identity, Networking and - supporting services such as MariaDB and RabbitMQ) - -* OpenStack Compute running KVM hypervisor - -* OpenStack Object Storage - -* Orchestration service - -* Telemetry service - -Beyond the normal Identity, Compute, Image service, and Object Storage -components, we recommend the Orchestration service component to handle -the proper scaling of workloads to adjust to demand. Due to the -requirement for auto-scaling, the design includes the Telemetry service. -Web services tend to be bursty in load, have very defined peak and -valley usage patterns and, as a result, benefit from automatic scaling -of instances based upon traffic. At a network level, a split network -configuration works well with databases residing on private project -networks since these do not emit a large quantity of broadcast traffic -and may need to interconnect to some databases for content. - -Load balancing --------------- - -Load balancing spreads requests across multiple instances. This workload -scales well horizontally across large numbers of instances. This enables -instances to run without publicly routed IP addresses and instead to -rely on the load balancer to provide a globally reachable service. Many -of these services do not require direct server return. This aids in -address planning and utilization at scale since only the virtual IP -(VIP) must be public. - -Overlay networks ----------------- - -The overlay functionality design includes OpenStack Networking in Open -vSwitch GRE tunnel mode. In this case, the layer-3 external routers pair -with VRRP, and switches pair with an implementation of MLAG to ensure -that you do not lose connectivity with the upstream routing -infrastructure. - -Performance tuning ------------------- - -Network level tuning for this workload is minimal. :term:`Quality of Service -(QoS)` applies to these workloads for a middle ground Class Selector -depending on existing policies. It is higher than a best effort queue -but lower than an Expedited Forwarding or Assured Forwarding queue. -Since this type of application generates larger packets with -longer-lived connections, you can optimize bandwidth utilization for -long duration TCP. Normal bandwidth planning applies here with regards -to benchmarking a session's usage multiplied by the expected number of -concurrent sessions with overhead. - -Network functions ------------------ - -Network functions is a broad category but encompasses workloads that -support the rest of a system's network. These workloads tend to consist -of large amounts of small packets that are very short lived, such as DNS -queries or SNMP traps. These messages need to arrive quickly and do not -deal with packet loss as there can be a very large volume of them. There -are a few extra considerations to take into account for this type of -workload and this can change a configuration all the way to the -hypervisor level. For an application that generates 10 TCP sessions per -user with an average bandwidth of 512 kilobytes per second per flow and -expected user count of ten thousand concurrent users, the expected -bandwidth plan is approximately 4.88 gigabits per second. - -The supporting network for this type of configuration needs to have a -low latency and evenly distributed availability. This workload benefits -from having services local to the consumers of the service. Use a -multi-site approach as well as deploying many copies of the application -to handle load as close as possible to consumers. Since these -applications function independently, they do not warrant running -overlays to interconnect project networks. Overlays also have the -drawback of performing poorly with rapid flow setup and may incur too -much overhead with large quantities of small packets and therefore we do -not recommend them. - -QoS is desirable for some workloads to ensure delivery. DNS has a major -impact on the load times of other services and needs to be reliable and -provide rapid responses. Configure rules in upstream devices to apply a -higher Class Selector to DNS to ensure faster delivery or a better spot -in queuing algorithms. - -Cloud storage -------------- - -Another common use case for OpenStack environments is providing a -cloud-based file storage and sharing service. You might consider this a -storage-focused use case, but its network-side requirements make it a -network-focused use case. - -For example, consider a cloud backup application. This workload has two -specific behaviors that impact the network. Because this workload is an -externally-facing service and an internally-replicating application, it -has both :term:`north-south` and -:term:`east-west` traffic considerations: - -north-south traffic - When a user uploads and stores content, that content moves into the - OpenStack installation. When users download this content, the - content moves out from the OpenStack installation. Because this - service operates primarily as a backup, most of the traffic moves - southbound into the environment. In this situation, it benefits you - to configure a network to be asymmetrically downstream because the - traffic that enters the OpenStack installation is greater than the - traffic that leaves the installation. - -east-west traffic - Likely to be fully symmetric. Because replication originates from - any node and might target multiple other nodes algorithmically, it - is less likely for this traffic to have a larger volume in any - specific direction. However this traffic might interfere with - north-south traffic. - -.. figure:: figures/Network_Cloud_Storage2.png - -This application prioritizes the north-south traffic over east-west -traffic: the north-south traffic involves customer-facing data. - -The network design in this case is less dependent on availability and -more dependent on being able to handle high bandwidth. As a direct -result, it is beneficial to forgo redundant links in favor of bonding -those connections. This increases available bandwidth. It is also -beneficial to configure all devices in the path, including OpenStack, to -generate and pass jumbo frames. diff --git a/doc/arch-design-to-archive/source/network-focus-technical-considerations.rst b/doc/arch-design-to-archive/source/network-focus-technical-considerations.rst deleted file mode 100644 index ce6cc67da2..0000000000 --- a/doc/arch-design-to-archive/source/network-focus-technical-considerations.rst +++ /dev/null @@ -1,367 +0,0 @@ -Technical considerations -~~~~~~~~~~~~~~~~~~~~~~~~ - -When you design an OpenStack network architecture, you must consider -layer-2 and layer-3 issues. Layer-2 decisions involve those made at the -data-link layer, such as the decision to use Ethernet versus Token Ring. -Layer-3 decisions involve those made about the protocol layer and the -point when IP comes into the picture. As an example, a completely -internal OpenStack network can exist at layer 2 and ignore layer 3. In -order for any traffic to go outside of that cloud, to another network, -or to the Internet, however, you must use a layer-3 router or switch. - -The past few years have seen two competing trends in networking. One -trend leans towards building data center network architectures based on -layer-2 networking. Another trend treats the cloud environment -essentially as a miniature version of the Internet. This approach is -radically different from the network architecture approach in the -staging environment: the Internet only uses layer-3 routing rather than -layer-2 switching. - -A network designed on layer-2 protocols has advantages over one designed -on layer-3 protocols. In spite of the difficulties of using a bridge to -perform the network role of a router, many vendors, customers, and -service providers choose to use Ethernet in as many parts of their -networks as possible. The benefits of selecting a layer-2 design are: - -* Ethernet frames contain all the essentials for networking. These - include, but are not limited to, globally unique source addresses, - globally unique destination addresses, and error control. - -* Ethernet frames can carry any kind of packet. Networking at layer-2 - is independent of the layer-3 protocol. - -* Adding more layers to the Ethernet frame only slows the networking - process down. This is known as 'nodal processing delay'. - -* You can add adjunct networking features, for example class of service - (CoS) or multicasting, to Ethernet as readily as IP networks. - -* VLANs are an easy mechanism for isolating networks. - -Most information starts and ends inside Ethernet frames. Today this -applies to data, voice (for example, VoIP), and video (for example, web -cameras). The concept is that if you can perform more of the end-to-end -transfer of information from a source to a destination in the form of -Ethernet frames, the network benefits more from the advantages of -Ethernet. Although it is not a substitute for IP networking, networking -at layer-2 can be a powerful adjunct to IP networking. - -Layer-2 Ethernet usage has these advantages over layer-3 IP network -usage: - -* Speed - -* Reduced overhead of the IP hierarchy. - -* No need to keep track of address configuration as systems move - around. Whereas the simplicity of layer-2 protocols might work well - in a data center with hundreds of physical machines, cloud data - centers have the additional burden of needing to keep track of all - virtual machine addresses and networks. In these data centers, it is - not uncommon for one physical node to support 30-40 instances. - - .. important:: - - Networking at the frame level says nothing about the presence or - absence of IP addresses at the packet level. Almost all ports, - links, and devices on a network of LAN switches still have IP - addresses, as do all the source and destination hosts. There are - many reasons for the continued need for IP addressing. The largest - one is the need to manage the network. A device or link without an - IP address is usually invisible to most management applications. - Utilities including remote access for diagnostics, file transfer of - configurations and software, and similar applications cannot run - without IP addresses as well as MAC addresses. - -Layer-2 architecture limitations --------------------------------- - -Outside of the traditional data center the limitations of layer-2 -network architectures become more obvious. - -* Number of VLANs is limited to 4096. - -* The number of MACs stored in switch tables is limited. - -* You must accommodate the need to maintain a set of layer-4 devices to - handle traffic control. - -* MLAG, often used for switch redundancy, is a proprietary solution - that does not scale beyond two devices and forces vendor lock-in. - -* It can be difficult to troubleshoot a network without IP addresses - and ICMP. - -* Configuring :term:`ARP
` can be - complicated on large layer-2 networks. - -* All network devices need to be aware of all MACs, even instance MACs, - so there is constant churn in MAC tables and network state changes as - instances start and stop. - -* Migrating MACs (instance migration) to different physical locations - are a potential problem if you do not set ARP table timeouts - properly. - -It is important to know that layer-2 has a very limited set of network -management tools. It is very difficult to control traffic, as it does -not have mechanisms to manage the network or shape the traffic, and -network troubleshooting is very difficult. One reason for this -difficulty is network devices have no IP addresses. As a result, there -is no reasonable way to check network delay in a layer-2 network. - -On large layer-2 networks, configuring ARP learning can also be -complicated. The setting for the MAC address timer on switches is -critical and, if set incorrectly, can cause significant performance -problems. As an example, the Cisco default MAC address timer is -extremely long. Migrating MACs to different physical locations to -support instance migration can be a significant problem. In this case, -the network information maintained in the switches could be out of sync -with the new location of the instance. - -In a layer-2 network, all devices are aware of all MACs, even those that -belong to instances. The network state information in the backbone -changes whenever an instance starts or stops. As a result there is far -too much churn in the MAC tables on the backbone switches. - -Layer-3 architecture advantages -------------------------------- - -In the layer-3 case, there is no churn in the routing tables due to -instances starting and stopping. The only time there would be a routing -state change is in the case of a Top of Rack (ToR) switch failure or a -link failure in the backbone itself. Other advantages of using a layer-3 -architecture include: - -* Layer-3 networks provide the same level of resiliency and scalability - as the Internet. - -* Controlling traffic with routing metrics is straightforward. - -* You can configure layer 3 to use :term:`BGP` - confederation for scalability so core routers have state proportional to the - number of racks, not to the number of servers or instances. - -* Routing takes instance MAC and IP addresses out of the network core, - reducing state churn. Routing state changes only occur in the case of - a ToR switch failure or backbone link failure. - -* There are a variety of well tested tools, for example ICMP, to - monitor and manage traffic. - -* Layer-3 architectures enable the use of :term:`quality of service (QoS)` to - manage network performance. - -Layer-3 architecture limitations -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The main limitation of layer 3 is that there is no built-in isolation -mechanism comparable to the VLANs in layer-2 networks. Furthermore, the -hierarchical nature of IP addresses means that an instance is on the -same subnet as its physical host. This means that you cannot migrate it -outside of the subnet easily. For these reasons, network virtualization -needs to use IP :term:`encapsulation` and software at the end hosts for -isolation and the separation of the addressing in the virtual layer from -the addressing in the physical layer. Other potential disadvantages of -layer 3 include the need to design an IP addressing scheme rather than -relying on the switches to keep track of the MAC addresses automatically -and to configure the interior gateway routing protocol in the switches. - -Network recommendations overview --------------------------------- - -OpenStack has complex networking requirements for several reasons. Many -components interact at different levels of the system stack that adds -complexity. Data flows are complex. Data in an OpenStack cloud moves -both between instances across the network (also known as East-West), as -well as in and out of the system (also known as North-South). Physical -server nodes have network requirements that are independent of instance -network requirements, which you must isolate from the core network to -account for scalability. We recommend functionally separating the -networks for security purposes and tuning performance through traffic -shaping. - -You must consider a number of important general technical and business -factors when planning and designing an OpenStack network. They include: - -* A requirement for vendor independence. To avoid hardware or software - vendor lock-in, the design should not rely on specific features of a - vendor's router or switch. - -* A requirement to massively scale the ecosystem to support millions of - end users. - -* A requirement to support indeterminate platforms and applications. - -* A requirement to design for cost efficient operations to take - advantage of massive scale. - -* A requirement to ensure that there is no single point of failure in - the cloud ecosystem. - -* A requirement for high availability architecture to meet customer SLA - requirements. - -* A requirement to be tolerant of rack level failure. - -* A requirement to maximize flexibility to architect future production - environments. - -Bearing in mind these considerations, we recommend the following: - -* Layer-3 designs are preferable to layer-2 architectures. - -* Design a dense multi-path network core to support multi-directional - scaling and flexibility. - -* Use hierarchical addressing because it is the only viable option to - scale network ecosystem. - -* Use virtual networking to isolate instance service network traffic - from the management and internal network traffic. - -* Isolate virtual networks using encapsulation technologies. - -* Use traffic shaping for performance tuning. - -* Use eBGP to connect to the Internet up-link. - -* Use iBGP to flatten the internal traffic on the layer-3 mesh. - -* Determine the most effective configuration for block storage network. - -Additional considerations -------------------------- - -There are several further considerations when designing a -network-focused OpenStack cloud. - -OpenStack Networking versus legacy networking (nova-network) considerations -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Selecting the type of networking technology to implement depends on many -factors. OpenStack Networking (neutron) and legacy networking -(nova-network) both have their advantages and disadvantages. They are -both valid and supported options that fit different use cases: - -.. list-table:: **Redundant networking: ToR switch high availability risk - analysis** - :widths: 50 40 - :header-rows: 1 - - * - Legacy networking (nova-network) - - OpenStack Networking - * - Simple, single agent - - Complex, multiple agents - * - More mature, established - - Newer, maturing - * - Flat or VLAN - - Flat, VLAN, Overlays, L2-L3, SDN - * - No plug-in support - - Plug-in support for 3rd parties - * - Scales well - - Scaling requires 3rd party plug-ins - * - No multi-tier topologies - - Multi-tier topologies - -Redundant networking: ToR switch high availability risk analysis -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A technical consideration of networking is the idea that you should -install switching gear in a data center with backup switches in case of -hardware failure. - -Research indicates the mean time between failures (MTBF) on switches is -between 100,000 and 200,000 hours. This number is dependent on the -ambient temperature of the switch in the data center. When properly -cooled and maintained, this translates to between 11 and 22 years before -failure. Even in the worst case of poor ventilation and high ambient -temperatures in the data center, the MTBF is still 2-3 years. See -`Ethernet switch reliablity: Temperature vs. moving parts -`_ -for further information. - -In most cases, it is much more economical to use a single switch with a -small pool of spare switches to replace failed units than it is to -outfit an entire data center with redundant switches. Applications -should tolerate rack level outages without affecting normal operations, -since network and compute resources are easily provisioned and -plentiful. - -Preparing for the future: IPv6 support -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -One of the most important networking topics today is the impending -exhaustion of IPv4 addresses. In early 2014, ICANN announced that they -started allocating the final IPv4 address blocks to the `Regional -Internet Registries -`_. -This means the IPv4 address space is close to being fully allocated. As -a result, it will soon become difficult to allocate more IPv4 addresses -to an application that has experienced growth, or that you expect to -scale out, due to the lack of unallocated IPv4 address blocks. - -For network focused applications the future is the IPv6 protocol. IPv6 -increases the address space significantly, fixes long standing issues in -the IPv4 protocol, and will become essential for network focused -applications in the future. - -OpenStack Networking supports IPv6 when configured to take advantage of -it. To enable IPv6, create an IPv6 subnet in Networking and use IPv6 -prefixes when creating security groups. - -Asymmetric links -^^^^^^^^^^^^^^^^ - -When designing a network architecture, the traffic patterns of an -application heavily influence the allocation of total bandwidth and the -number of links that you use to send and receive traffic. Applications -that provide file storage for customers allocate bandwidth and links to -favor incoming traffic, whereas video streaming applications allocate -bandwidth and links to favor outgoing traffic. - -Performance -^^^^^^^^^^^ - -It is important to analyze the applications' tolerance for latency and -jitter when designing an environment to support network focused -applications. Certain applications, for example VoIP, are less tolerant -of latency and jitter. Where latency and jitter are concerned, certain -applications may require tuning of QoS parameters and network device -queues to ensure that they queue for transmit immediately or guarantee -minimum bandwidth. Since OpenStack currently does not support these -functions, consider carefully your selected network plug-in. - -The location of a service may also impact the application or consumer -experience. If an application serves differing content to different -users it must properly direct connections to those specific locations. -Where appropriate, use a multi-site installation for these situations. - -You can implement networking in two separate ways. Legacy networking -(nova-network) provides a flat DHCP network with a single broadcast -domain. This implementation does not support project isolation networks -or advanced plug-ins, but it is currently the only way to implement a -distributed :term:`layer-3 (L3) agent` using the multi_host configuration. -OpenStack Networking (neutron) is the official networking implementation and -provides a pluggable architecture that supports a large variety of -network methods. Some of these include a layer-2 only provider network -model, external device plug-ins, or even OpenFlow controllers. - -Networking at large scales becomes a set of boundary questions. The -determination of how large a layer-2 domain must be is based on the -amount of nodes within the domain and the amount of broadcast traffic -that passes between instances. Breaking layer-2 boundaries may require -the implementation of overlay networks and tunnels. This decision is a -balancing act between the need for a smaller overhead or a need for a -smaller domain. - -When selecting network devices, be aware that making this decision based -on the greatest port density often comes with a drawback. Aggregation -switches and routers have not all kept pace with Top of Rack switches -and may induce bottlenecks on north-south traffic. As a result, it may -be possible for massive amounts of downstream network utilization to -impact upstream network devices, impacting service to the cloud. Since -OpenStack does not currently provide a mechanism for traffic shaping or -rate limiting, it is necessary to implement these features at the -network hardware level. diff --git a/doc/arch-design-to-archive/source/network-focus-user-requirements.rst b/doc/arch-design-to-archive/source/network-focus-user-requirements.rst deleted file mode 100644 index 2865742d52..0000000000 --- a/doc/arch-design-to-archive/source/network-focus-user-requirements.rst +++ /dev/null @@ -1,71 +0,0 @@ -User requirements -~~~~~~~~~~~~~~~~~ - -Network-focused architectures vary from the general-purpose architecture -designs. Certain network-intensive applications influence these -architectures. Some of the business requirements that influence the -design include network latency through slow page loads, degraded video -streams, and low quality VoIP sessions impacts the user experience. - -Users are often not aware of how network design and architecture affects their -experiences. Both enterprise customers and end-users rely on the network for -delivery of an application. Network performance problems can result in a -negative experience for the end-user, as well as productivity and economic -loss. - -High availability issues ------------------------- - -Depending on the application and use case, network-intensive OpenStack -installations can have high availability requirements. Financial -transaction systems have a much higher requirement for high availability -than a development application. Use network availability technologies, -for example :term:`quality of service (QoS)`, to improve the network -performance of sensitive applications such as VoIP and video streaming. - -High performance systems have SLA requirements for a minimum QoS with -regard to guaranteed uptime, latency, and bandwidth. The level of the -SLA can have a significant impact on the network architecture and -requirements for redundancy in the systems. - -Risks ------ - -Network misconfigurations - Configuring incorrect IP addresses, VLANs, and routers can cause - outages to areas of the network or, in the worst-case scenario, the - entire cloud infrastructure. Automate network configurations to - minimize the opportunity for operator error as it can cause - disruptive problems. - -Capacity planning - Cloud networks require management for capacity and growth over time. - Capacity planning includes the purchase of network circuits and - hardware that can potentially have lead times measured in months or - years. - -Network tuning - Configure cloud networks to minimize link loss, packet loss, packet - storms, broadcast storms, and loops. - -Single Point Of Failure (SPOF) - Consider high availability at the physical and environmental layers. - If there is a single point of failure due to only one upstream link, - or only one power supply, an outage can become unavoidable. - -Complexity - An overly complex network design can be difficult to maintain and - troubleshoot. While device-level configuration can ease maintenance - concerns and automated tools can handle overlay networks, avoid or - document non-traditional interconnects between functions and - specialized hardware to prevent outages. - -Non-standard features - There are additional risks that arise from configuring the cloud - network to take advantage of vendor specific features. One example - is multi-link aggregation (MLAG) used to provide redundancy at the - aggregator switch level of the network. MLAG is not a standard and, - as a result, each vendor has their own proprietary implementation of - the feature. MLAG architectures are not interoperable across switch - vendors, which leads to vendor lock-in, and can cause delays or - inability when upgrading components. diff --git a/doc/arch-design-to-archive/source/network-focus.rst b/doc/arch-design-to-archive/source/network-focus.rst deleted file mode 100644 index 3b5b021dd4..0000000000 --- a/doc/arch-design-to-archive/source/network-focus.rst +++ /dev/null @@ -1,101 +0,0 @@ -=============== -Network focused -=============== - -.. toctree:: - :maxdepth: 2 - - network-focus-user-requirements.rst - network-focus-technical-considerations.rst - network-focus-operational-considerations.rst - network-focus-architecture.rst - network-focus-prescriptive-examples.rst - -All OpenStack deployments depend on network communication in order to function -properly due to its service-based nature. In some cases, however, the network -elevates beyond simple infrastructure. This chapter discusses architectures -that are more reliant or focused on network services. These architectures -depend on the network infrastructure and require network services that -perform reliably in order to satisfy user and application requirements. - -Some possible use cases include: - -Content delivery network - This includes streaming video, viewing photographs, or accessing any other - cloud-based data repository distributed to a large number of end users. - Network configuration affects latency, bandwidth, and the distribution of - instances. Therefore, it impacts video streaming. Not all video streaming - is consumer-focused. For example, multicast videos (used for media, press - conferences, corporate presentations, and web conferencing services) can - also use a content delivery network. The location of the video repository - and its relationship to end users affects content delivery. Network - throughput of the back-end systems, as well as the WAN architecture and - the cache methodology, also affect performance. - -Network management functions - Use this cloud to provide network service functions built to support the - delivery of back-end network services such as DNS, NTP, or SNMP. - -Network service offerings - Use this cloud to run customer-facing network tools to support services. - Examples include VPNs, MPLS private networks, and GRE tunnels. - -Web portals or web services - Web servers are a common application for cloud services, and we recommend - an understanding of their network requirements. The network requires scaling - out to meet user demand and deliver web pages with a minimum latency. - Depending on the details of the portal architecture, consider the internal - east-west and north-south network bandwidth. - -High speed and high volume transactional systems - These types of applications are sensitive to network configurations. Examples - include financial systems, credit card transaction applications, and trading - and other extremely high volume systems. These systems are sensitive to - network jitter and latency. They must balance a high volume of East-West and - North-South network traffic to maximize efficiency of the data delivery. Many - of these systems must access large, high performance database back ends. - -High availability - These types of use cases are dependent on the proper sizing of the network to - maintain replication of data between sites for high availability. If one site - becomes unavailable, the extra sites can serve the displaced load until the - original site returns to service. It is important to size network capacity to - handle the desired loads. - -Big data - Clouds used for the management and collection of big data (data ingest) have - a significant demand on network resources. Big data often uses partial - replicas of the data to maintain integrity over large distributed clouds. - Other big data applications that require a large amount of network resources - are Hadoop, Cassandra, NuoDB, Riak, and other NoSQL and distributed - databases. - -Virtual desktop infrastructure (VDI) - This use case is sensitive to network congestion, latency, jitter, and other - network characteristics. Like video streaming, the user experience is - important. However, unlike video streaming, caching is not an option to - offset the network issues. VDI requires both upstream and downstream traffic - and cannot rely on caching for the delivery of the application to the end - user. - -Voice over IP (VoIP) - This is sensitive to network congestion, latency, jitter, and other network - characteristics. VoIP has a symmetrical traffic pattern and it requires - network :term:`quality of service (QoS)` for best performance. In addition, - you can implement active queue management to deliver voice and multimedia - content. Users are sensitive to latency and jitter fluctuations and can detect - them at very low levels. - -Video Conference or web conference - This is sensitive to network congestion, latency, jitter, and other network - characteristics. Video Conferencing has a symmetrical traffic pattern, but - unless the network is on an MPLS private network, it cannot use network - :term:`quality of service (QoS)` to improve performance. Similar to VoIP, - users are sensitive to network performance issues even at low levels. - -High performance computing (HPC) - This is a complex use case that requires careful consideration of the traffic - flows and usage patterns to address the needs of cloud clusters. It has high - east-west traffic patterns for distributed computing, but there can be - substantial north-south traffic depending on the specific application. - diff --git a/doc/arch-design-to-archive/source/references.rst b/doc/arch-design-to-archive/source/references.rst deleted file mode 100644 index f12f826a78..0000000000 --- a/doc/arch-design-to-archive/source/references.rst +++ /dev/null @@ -1,85 +0,0 @@ -========== -References -========== - -`Data Protection framework of the European Union -`_ -: Guidance on Data Protection laws governed by the EU. - -`Depletion of IPv4 Addresses -`_ -: describing how IPv4 addresses and the migration to IPv6 is inevitable. - -`Ethernet Switch Reliability `_ -: Research white paper on Ethernet Switch reliability. - -`Financial Industry Regulatory Authority -`_ -: Requirements of the Financial Industry Regulatory Authority in the USA. - -`Image Service property keys `_ -: Glance API property keys allows the administrator to attach custom -characteristics to images. - -`LibGuestFS Documentation `_ -: Official LibGuestFS documentation. - -`Logging and Monitoring -`_ -: Official OpenStack Operations documentation. - -`ManageIQ Cloud Management Platform `_ -: An Open Source Cloud Management Platform for managing multiple clouds. - -`N-Tron Network Availability -`_ -: Research white paper on network availability. - -`Nested KVM `_ -: Post on how to nest KVM under KVM. - -`Open Compute Project `_ -: The Open Compute Project Foundation's mission is to design -and enable the delivery of the most efficient server, -storage and data center hardware designs for scalable computing. - -`OpenStack Flavors -`_ -: Official OpenStack documentation. - -`OpenStack High Availability Guide `_ -: Information on how to provide redundancy for the OpenStack components. - -`OpenStack Hypervisor Support Matrix -`_ -: Matrix of supported hypervisors and capabilities when used with OpenStack. - -`OpenStack Object Store (Swift) Replication Reference -`_ -: Developer documentation of Swift replication. - -`OpenStack Operations Guide `_ -: The OpenStack Operations Guide provides information on setting up -and installing OpenStack. - -`OpenStack Security Guide `_ -: The OpenStack Security Guide provides information on securing -OpenStack deployments. - -`OpenStack Training Marketplace -`_ -: The OpenStack Market for training and Vendors providing training -on OpenStack. - -`PCI passthrough `_ -: The PCI API patches extend the servers/os-hypervisor to -show PCI information for instance and compute node, -and also provides a resource endpoint to show PCI information. - -`TripleO `_ -: TripleO is a program aimed at installing, upgrading and operating -OpenStack clouds using OpenStack's own cloud facilities as the foundation. diff --git a/doc/arch-design-to-archive/source/specialized-desktop-as-a-service.rst b/doc/arch-design-to-archive/source/specialized-desktop-as-a-service.rst deleted file mode 100644 index dd5e93dd97..0000000000 --- a/doc/arch-design-to-archive/source/specialized-desktop-as-a-service.rst +++ /dev/null @@ -1,47 +0,0 @@ -==================== -Desktop-as-a-Service -==================== - -Virtual Desktop Infrastructure (VDI) is a service that hosts -user desktop environments on remote servers. This application -is very sensitive to network latency and requires a high -performance compute environment. Traditionally these types of -services do not use cloud environments because few clouds -support such a demanding workload for user-facing applications. -As cloud environments become more robust, vendors are starting -to provide services that provide virtual desktops in the cloud. -OpenStack may soon provide the infrastructure for these types of deployments. - -Challenges -~~~~~~~~~~ - -Designing an infrastructure that is suitable to host virtual -desktops is a very different task to that of most virtual workloads. -For example, the design must consider: - -* Boot storms, when a high volume of logins occur in a short period of time -* The performance of the applications running on virtual desktops -* Operating systems and their compatibility with the OpenStack hypervisor - -Broker -~~~~~~ - -The connection broker determines which remote desktop host -users can access. Medium and large scale environments require a broker -since its service represents a central component of the architecture. -The broker is a complete management product, and enables automated -deployment and provisioning of remote desktop hosts. - -Possible solutions -~~~~~~~~~~~~~~~~~~ - -There are a number of commercial products currently available that -provide a broker solution. However, no native OpenStack projects -provide broker services. -Not providing a broker is also an option, but managing this manually -would not suffice for a large scale, enterprise solution. - -Diagram -~~~~~~~ - -.. figure:: figures/Specialized_VDI1.png diff --git a/doc/arch-design-to-archive/source/specialized-hardware.rst b/doc/arch-design-to-archive/source/specialized-hardware.rst deleted file mode 100644 index 68404a68d5..0000000000 --- a/doc/arch-design-to-archive/source/specialized-hardware.rst +++ /dev/null @@ -1,43 +0,0 @@ -==================== -Specialized hardware -==================== - -Certain workloads require specialized hardware devices that -have significant virtualization or sharing challenges. -Applications such as load balancers, highly parallel brute -force computing, and direct to wire networking may need -capabilities that basic OpenStack components do not provide. - -Challenges -~~~~~~~~~~ - -Some applications need access to hardware devices to either -improve performance or provide capabilities that are not -virtual CPU, RAM, network, or storage. These can be a shared -resource, such as a cryptography processor, or a dedicated -resource, such as a Graphics Processing Unit (GPU). OpenStack can -provide some of these, while others may need extra work. - -Solutions -~~~~~~~~~ - -To provide cryptography offloading to a set of instances, -you can use Image service configuration options. -For example, assign the cryptography chip to a device node in the guest. -The OpenStack Command Line Reference contains further information on -configuring this solution in the section `Image service property keys -`_. -A challenge, however, is this option allows all guests using the -configured images to access the hypervisor cryptography device. - -If you require direct access to a specific device, PCI pass-through -enables you to dedicate the device to a single instance per hypervisor. -You must define a flavor that has the PCI device specifically in order -to properly schedule instances. -More information regarding PCI pass-through, including instructions for -implementing and using it, is available at -`https://wiki.openstack.org/wiki/Pci_passthrough `_. - -.. figure:: figures/Specialized_Hardware2.png - :width: 100% diff --git a/doc/arch-design-to-archive/source/specialized-multi-hypervisor.rst b/doc/arch-design-to-archive/source/specialized-multi-hypervisor.rst deleted file mode 100644 index d31b04604e..0000000000 --- a/doc/arch-design-to-archive/source/specialized-multi-hypervisor.rst +++ /dev/null @@ -1,78 +0,0 @@ -======================== -Multi-hypervisor example -======================== - -A financial company requires its applications migrated -from a traditional, virtualized environment to an API driven, -orchestrated environment. The new environment needs -multiple hypervisors since many of the company's applications -have strict hypervisor requirements. - -Currently, the company's vSphere environment runs 20 VMware -ESXi hypervisors. These hypervisors support 300 instances of -various sizes. Approximately 50 of these instances must run -on ESXi. The remaining 250 or so have more flexible requirements. - -The financial company decides to manage the -overall system with a common OpenStack platform. - -.. figure:: figures/Compute_NSX.png - :width: 100% - -Architecture planning teams decided to run a host aggregate -containing KVM hypervisors for the general purpose instances. -A separate host aggregate targets instances requiring ESXi. - -Images in the OpenStack Image service have particular -hypervisor metadata attached. When a user requests a -certain image, the instance spawns on the relevant aggregate. - -Images for ESXi use the VMDK format. You can convert -QEMU disk images to VMDK, VMFS Flat Disks. These disk images -can also be thin, thick, zeroed-thick, and eager-zeroed-thick. -After exporting a VMFS thin disk from VMFS to the -OpenStack Image service (a non-VMFS location), it becomes a -preallocated flat disk. This impacts the transfer time from the -OpenStack Image service to the data store since transfers require -moving the full preallocated flat disk rather than the thin disk. - -The VMware host aggregate compute nodes communicate with -vCenter rather than spawning directly on a hypervisor. -The vCenter then requests scheduling for the instance to run on -an ESXi hypervisor. - -This functionality requires that VMware Distributed Resource -Scheduler (DRS) is enabled on a cluster and set to **Fully Automated**. -The vSphere requires shared storage because the DRS uses vMotion -which is a service that relies on shared storage. - -This solution to the company's migration uses shared storage -to provide Block Storage capabilities to the KVM instances while -also providing vSphere storage. The new environment provides this -storage functionality using a dedicated data network. The -compute hosts should have dedicated NICs to support the -dedicated data network. vSphere supports OpenStack Block Storage. This -support gives storage from a VMFS datastore to an instance. For the -financial company, Block Storage in their new architecture supports -both hypervisors. - -OpenStack Networking provides network connectivity in this new -architecture, with the VMware NSX plug-in driver configured. legacy -networking (nova-network) supports both hypervisors in this new -architecture example, but has limitations. Specifically, vSphere -with legacy networking does not support security groups. The new -architecture uses VMware NSX as a part of the design. When users launch an -instance within either of the host aggregates, VMware NSX ensures the -instance attaches to the appropriate network overlay-based logical networks. - -The architecture planning teams also consider OpenStack Compute integration. -When running vSphere in an OpenStack environment, nova-compute -communications with vCenter appear as a single large hypervisor. -This hypervisor represents the entire ESXi cluster. Multiple nova-compute -instances can represent multiple ESXi clusters. They can connect to -multiple vCenter servers. If the process running nova-compute -crashes it cuts the connection to the vCenter server. -Any ESXi clusters will stop running, and you will not be able to -provision further instances on the vCenter, even if you enable high -availability. You must monitor the nova-compute service connected -to vSphere carefully for any disruptions as a result of this failure point. diff --git a/doc/arch-design-to-archive/source/specialized-networking.rst b/doc/arch-design-to-archive/source/specialized-networking.rst deleted file mode 100644 index 84a116dd81..0000000000 --- a/doc/arch-design-to-archive/source/specialized-networking.rst +++ /dev/null @@ -1,32 +0,0 @@ -============================== -Specialized networking example -============================== - -Some applications that interact with a network require -specialized connectivity. Applications such as a looking glass -require the ability to connect to a BGP peer, or route participant -applications may need to join a network at a layer2 level. - -Challenges -~~~~~~~~~~ - -Connecting specialized network applications to their required -resources alters the design of an OpenStack installation. -Installations that rely on overlay networks are unable to -support a routing participant, and may also block layer-2 listeners. - -Possible solutions -~~~~~~~~~~~~~~~~~~ - -Deploying an OpenStack installation using OpenStack Networking with a -provider network allows direct layer-2 connectivity to an -upstream networking device. -This design provides the layer-2 connectivity required to communicate -via Intermediate System-to-Intermediate System (ISIS) protocol or -to pass packets controlled by an OpenFlow controller. -Using the multiple layer-2 plug-in with an agent such as -:term:`Open vSwitch` allows a private connection through a VLAN -directly to a specific port in a layer-3 device. -This allows a BGP point-to-point link to join the autonomous system. -Avoid using layer-3 plug-ins as they divide the broadcast -domain and prevent router adjacencies from forming. diff --git a/doc/arch-design-to-archive/source/specialized-openstack-on-openstack.rst b/doc/arch-design-to-archive/source/specialized-openstack-on-openstack.rst deleted file mode 100644 index 50552f702c..0000000000 --- a/doc/arch-design-to-archive/source/specialized-openstack-on-openstack.rst +++ /dev/null @@ -1,71 +0,0 @@ -====================== -OpenStack on OpenStack -====================== - -In some cases, users may run OpenStack nested on top -of another OpenStack cloud. This scenario describes how to -manage and provision complete OpenStack environments on instances -supported by hypervisors and servers, which an underlying OpenStack -environment controls. - -Public cloud providers can use this technique to manage the -upgrade and maintenance process on complete OpenStack environments. -Developers and those testing OpenStack can also use this -technique to provision their own OpenStack environments on -available OpenStack Compute resources, whether public or private. - -Challenges -~~~~~~~~~~ - -The network aspect of deploying a nested cloud is the most -complicated aspect of this architecture. -You must expose VLANs to the physical ports on which the underlying -cloud runs because the bare metal cloud owns all the hardware. -You must also expose them to the nested levels as well. -Alternatively, you can use the network overlay technologies on the -OpenStack environment running on the host OpenStack environment to -provide the required software defined networking for the deployment. - -Hypervisor -~~~~~~~~~~ - -In this example architecture, consider which -approach you should take to provide a nested -hypervisor in OpenStack. This decision influences which -operating systems you use for the deployment of the nested -OpenStack deployments. - -Possible solutions: deployment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Deployment of a full stack can be challenging but you can mitigate -this difficulty by creating a Heat template to deploy the -entire stack, or a configuration management system. After creating -the Heat template, you can automate the deployment of additional stacks. - -The OpenStack-on-OpenStack project (:term:`TripleO`) -addresses this issue. Currently, however, the project does -not completely cover nested stacks. For more information, see -https://wiki.openstack.org/wiki/TripleO. - -Possible solutions: hypervisor -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -In the case of running TripleO, the underlying OpenStack -cloud deploys the compute nodes as bare-metal. You then deploy -OpenStack on these Compute bare-metal servers with the -appropriate hypervisor, such as KVM. - -In the case of running smaller OpenStack clouds for testing -purposes, where performance is not a critical factor, you can use -QEMU instead. It is also possible to run a KVM hypervisor in an instance -(see `davejingtian.org -`_), -though this is not a supported configuration, and could be a -complex solution for such a use case. - -Diagram -~~~~~~~ - -.. figure:: figures/Specialized_OOO.png - :width: 100% diff --git a/doc/arch-design-to-archive/source/specialized-software-defined-networking.rst b/doc/arch-design-to-archive/source/specialized-software-defined-networking.rst deleted file mode 100644 index 336afd034c..0000000000 --- a/doc/arch-design-to-archive/source/specialized-software-defined-networking.rst +++ /dev/null @@ -1,46 +0,0 @@ -=========================== -Software-defined networking -=========================== - -Software-defined networking (SDN) is the separation of the data -plane and control plane. SDN is a popular method of -managing and controlling packet flows within networks. -SDN uses overlays or directly controlled layer-2 devices to -determine flow paths, and as such presents challenges to a -cloud environment. Some designers may wish to run their -controllers within an OpenStack installation. Others may wish -to have their installations participate in an SDN-controlled network. - -Challenges -~~~~~~~~~~ - -SDN is a relatively new concept that is not yet standardized, -so SDN systems come in a variety of different implementations. -Because of this, a truly prescriptive architecture is not feasible. -Instead, examine the differences between an existing and a planned -OpenStack design and determine where potential conflicts and gaps exist. - -Possible solutions -~~~~~~~~~~~~~~~~~~ - -If an SDN implementation requires layer-2 access because it -directly manipulates switches, we do not recommend running an -overlay network or a layer-3 agent. -If the controller resides within an OpenStack installation, -it may be necessary to build an ML2 plug-in and schedule the -controller instances to connect to project VLANs that they can -talk directly to the switch hardware. -Alternatively, depending on the external device support, -use a tunnel that terminates at the switch hardware itself. - -Diagram -------- - -OpenStack hosted SDN controller: - -.. figure:: figures/Specialized_SDN_hosted.png - -OpenStack participating in an SDN controller network: - -.. figure:: figures/Specialized_SDN_external.png - diff --git a/doc/arch-design-to-archive/source/specialized.rst b/doc/arch-design-to-archive/source/specialized.rst deleted file mode 100644 index f41e99445f..0000000000 --- a/doc/arch-design-to-archive/source/specialized.rst +++ /dev/null @@ -1,39 +0,0 @@ -================= -Specialized cases -================= - -.. toctree:: - :maxdepth: 2 - - specialized-multi-hypervisor.rst - specialized-networking.rst - specialized-software-defined-networking.rst - specialized-desktop-as-a-service.rst - specialized-openstack-on-openstack.rst - specialized-hardware.rst - -Although most OpenStack architecture designs fall into one -of the seven major scenarios outlined in other sections -(compute focused, network focused, storage focused, general -purpose, multi-site, hybrid cloud, and massively scalable), -there are a few use cases that do not fit into these categories. -This section discusses these specialized cases and provide some -additional details and design considerations for each use case: - -* :doc:`Specialized networking `: - describes running networking-oriented software that may involve reading - packets directly from the wire or participating in routing protocols. -* :doc:`Software-defined networking (SDN) - `: - describes both running an SDN controller from within OpenStack - as well as participating in a software-defined network. -* :doc:`Desktop-as-a-Service `: - describes running a virtualized desktop environment in a cloud - (:term:`Desktop-as-a-Service`). - This applies to private and public clouds. -* :doc:`OpenStack on OpenStack `: - describes building a multi-tiered cloud by running OpenStack - on top of an OpenStack installation. -* :doc:`Specialized hardware `: - describes the use of specialized hardware devices from within - the OpenStack environment. diff --git a/doc/arch-design-to-archive/source/storage-focus-architecture.rst b/doc/arch-design-to-archive/source/storage-focus-architecture.rst deleted file mode 100644 index 1c2213dddf..0000000000 --- a/doc/arch-design-to-archive/source/storage-focus-architecture.rst +++ /dev/null @@ -1,440 +0,0 @@ -Architecture -~~~~~~~~~~~~ - -Consider the following factors when selecting storage hardware: - -* Cost - -* Performance - -* Reliability - -Storage-focused OpenStack clouds must address I/O intensive workloads. -These workloads are not CPU intensive, nor are they consistently network -intensive. The network may be heavily utilized to transfer storage, but -they are not otherwise network intensive. - -The selection of storage hardware determines the overall performance and -scalability of a storage-focused OpenStack design architecture. Several -factors impact the design process, including: - -Cost - The cost of components affects which storage architecture and - hardware you choose. - -Performance - The latency of storage I/O requests indicates performance. - Performance requirements affect which solution you choose. - -Scalability - Scalability refers to how the storage solution performs as it - expands to its maximum size. Storage solutions that perform well in - small configurations but have degraded performance in large - configurations are not scalable. A solution that performs well at - maximum expansion is scalable. Large deployments require a storage - solution that performs well as it expands. - -Latency is a key consideration in a storage-focused OpenStack cloud. -Using solid-state disks (SSDs) to minimize latency and, to reduce CPU -delays caused by waiting for the storage, increases performance. Use -RAID controller cards in compute hosts to improve the performance of the -underlying disk subsystem. - -Depending on the storage architecture, you can adopt a scale-out -solution, or use a highly expandable and scalable centralized storage -array. If a centralized storage array is the right fit for your -requirements, then the array vendor determines the hardware selection. -It is possible to build a storage array using commodity hardware with -Open Source software, but requires people with expertise to build such a -system. - -On the other hand, a scale-out storage solution that uses -direct-attached storage (DAS) in the servers may be an appropriate -choice. This requires configuration of the server hardware to support -the storage solution. - -Considerations affecting storage architecture (and corresponding storage -hardware) of a Storage-focused OpenStack cloud include: - -Connectivity - Based on the selected storage solution, ensure the connectivity - matches the storage solution requirements. We recommended confirming - that the network characteristics minimize latency to boost the - overall performance of the design. - -Latency - Determine if the use case has consistent or highly variable latency. - -Throughput - Ensure that the storage solution throughput is optimized for your - application requirements. - -Server hardware - Use of DAS impacts the server hardware choice and affects host - density, instance density, power density, OS-hypervisor, and - management tools. - -Compute (server) hardware selection ------------------------------------ - -Four opposing factors determine the compute (server) hardware selection: - -Server density - A measure of how many servers can fit into a given measure of - physical space, such as a rack unit [U]. - -Resource capacity - The number of CPU cores, how much RAM, or how much storage a given - server delivers. - -Expandability - The number of additional resources you can add to a server before it - reaches capacity. - -Cost - The relative cost of the hardware weighed against the level of - design effort needed to build the system. - -You must weigh the dimensions against each other to determine the best -design for the desired purpose. For example, increasing server density -can mean sacrificing resource capacity or expandability. Increasing -resource capacity and expandability can increase cost but decrease -server density. Decreasing cost often means decreasing supportability, -server density, resource capacity, and expandability. - -Compute capacity (CPU cores and RAM capacity) is a secondary -consideration for selecting server hardware. As a result, the required -server hardware must supply adequate CPU sockets, additional CPU cores, -and more RAM; network connectivity and storage capacity are not as -critical. The hardware needs to provide enough network connectivity and -storage capacity to meet the user requirements, however they are not the -primary consideration. - -Some server hardware form factors are better suited to storage-focused -designs than others. The following is a list of these form factors: - -* Most blade servers support dual-socket multi-core CPUs. Choose either - full width or full height blades to avoid the limit. High density - blade servers support up to 16 servers in only 10 rack units using - half height or half width blades. - - .. warning:: - - This decreases density by 50% (only 8 servers in 10 U) if a full - width or full height option is used. - -* 1U rack-mounted servers have the ability to offer greater server - density than a blade server solution, but are often limited to - dual-socket, multi-core CPU configurations. - - .. note:: - - Due to cooling requirements, it is rare to see 1U rack-mounted - servers with more than 2 CPU sockets. - - To obtain greater than dual-socket support in a 1U rack-mount form - factor, customers need to buy their systems from Original Design - Manufacturers (ODMs) or second-tier manufacturers. - -.. warning:: - - This may cause issues for organizations that have preferred - vendor policies or concerns with support and hardware warranties - of non-tier 1 vendors. - -* 2U rack-mounted servers provide quad-socket, multi-core CPU support - but with a corresponding decrease in server density (half the density - offered by 1U rack-mounted servers). - -* Larger rack-mounted servers, such as 4U servers, often provide even - greater CPU capacity. Commonly supporting four or even eight CPU - sockets. These servers have greater expandability but such servers - have much lower server density and usually greater hardware cost. - -* Rack-mounted servers that support multiple independent servers in a - single 2U or 3U enclosure, "sled servers", deliver increased density - as compared to a typical 1U-2U rack-mounted servers. - -Other factors that influence server hardware selection for a -storage-focused OpenStack design architecture include: - -Instance density - In this architecture, instance density and CPU-RAM oversubscription - are lower. You require more hosts to support the anticipated scale, - especially if the design uses dual-socket hardware designs. - -Host density - Another option to address the higher host count is to use a - quad-socket platform. Taking this approach decreases host density - which also increases rack count. This configuration affects the - number of power connections and also impacts network and cooling - requirements. - -Power and cooling density - The power and cooling density requirements might be lower than with - blade, sled, or 1U server designs due to lower host density (by - using 2U, 3U or even 4U server designs). For data centers with older - infrastructure, this might be a desirable feature. - -Storage-focused OpenStack design architecture server hardware selection -should focus on a "scale-up" versus "scale-out" solution. The -determination of which is the best solution (a smaller number of larger -hosts or a larger number of smaller hosts), depends on a combination of -factors including cost, power, cooling, physical rack and floor space, -support-warranty, and manageability. - -Networking hardware selection ------------------------------ - -Key considerations for the selection of networking hardware include: - -Port count - The user requires networking hardware that has the requisite port - count. - -Port density - The physical space required to provide the requisite port count - affects the network design. A switch that provides 48 10 GbE ports - in 1U has a much higher port density than a switch that provides 24 - 10 GbE ports in 2U. On a general scale, a higher port density leaves - more rack space for compute or storage components which is - preferred. It is also important to consider fault domains and power - density. Finally, higher density switches are more expensive, - therefore it is important not to over design the network. - -Port speed - The networking hardware must support the proposed network speed, for - example: 1 GbE, 10 GbE, or 40 GbE (or even 100 GbE). - -Redundancy - User requirements for high availability and cost considerations - influence the required level of network hardware redundancy. Achieve - network redundancy by adding redundant power supplies or paired - switches. - - .. note:: - - If this is a requirement, the hardware must support this - configuration. User requirements determine if a completely - redundant network infrastructure is required. - -Power requirements - Ensure that the physical data center provides the necessary power - for the selected network hardware. This is not an issue for top of - rack (ToR) switches, but may be an issue for spine switches in a - leaf and spine fabric, or end of row (EoR) switches. - -Protocol support - It is possible to gain more performance out of a single storage - system by using specialized network technologies such as RDMA, SRP, - iSER and SCST. The specifics for using these technologies is beyond - the scope of this book. - -Software selection ------------------- - -Factors that influence the software selection for a storage-focused -OpenStack architecture design include: - -* Operating system (OS) and hypervisor - -* OpenStack components - -* Supplemental software - -Design decisions made in each of these areas impacts the rest of the -OpenStack architecture design. - -Operating system and hypervisor -------------------------------- - -Operating system (OS) and hypervisor have a significant impact on the -overall design and also affect server hardware selection. Ensure the -selected operating system and hypervisor combination support the storage -hardware and work with the networking hardware selection and topology. - -Operating system and hypervisor selection affect the following areas: - -Cost - Selecting a commercially supported hypervisor, such as Microsoft - Hyper-V, results in a different cost model than a - community-supported open source hypervisor like Kinstance or Xen. - Similarly, choosing Ubuntu over Red Hat (or vice versa) impacts cost - due to support contracts. However, business or application - requirements might dictate a specific or commercially supported - hypervisor. - -Supportability - Staff must have training with the chosen hypervisor. Consider the - cost of training when choosing a solution. The support of a - commercial product such as Red Hat, SUSE, or Windows, is the - responsibility of the OS vendor. If an open source platform is - chosen, the support comes from in-house resources. - -Management tools - Ubuntu and Kinstance use different management tools than VMware - vSphere. Although both OS and hypervisor combinations are supported - by OpenStack, there are varying impacts to the rest of the design as - a result of the selection of one combination versus the other. - -Scale and performance - Ensure the selected OS and hypervisor combination meet the - appropriate scale and performance requirements needed for this - storage focused OpenStack cloud. The chosen architecture must meet - the targeted instance-host ratios with the selected OS-hypervisor - combination. - -Security - Ensure the design can accommodate the regular periodic installation - of application security patches while maintaining the required - workloads. The frequency of security patches for the proposed - OS-hypervisor combination impacts performance and the patch - installation process could affect maintenance windows. - -Supported features - Selecting the OS-hypervisor combination often determines the - required features of OpenStack. Certain features are only available - with specific OSes or hypervisors. For example, if certain features - are not available, you might need to modify the design to meet user - requirements. - -Interoperability - The OS-hypervisor combination should be chosen based on the - interoperability with one another, and other OS-hyervisor - combinations. Operational and troubleshooting tools for one - OS-hypervisor combination may differ from the tools used for another - OS-hypervisor combination. As a result, the design must address if - the two sets of tools need to interoperate. - -OpenStack components --------------------- - -The OpenStack components you choose can have a significant impact on the -overall design. While there are certain components that are always -present (Compute and Image service, for example), there are other -services that may not be required. As an example, a certain design may -not require the Orchestration service. Omitting Orchestration would not -typically have a significant impact on the overall design, however, if -the architecture uses a replacement for OpenStack Object Storage for its -storage component, this could potentially have significant impacts on -the rest of the design. - -A storage-focused design might require the ability to use Orchestration -to launch instances with Block Storage volumes to perform -storage-intensive processing. - -A storage-focused OpenStack design architecture uses the following -components: - -* OpenStack Identity (keystone) - -* OpenStack dashboard (horizon) - -* OpenStack Compute (nova) (including the use of multiple hypervisor - drivers) - -* OpenStack Object Storage (swift) (or another object storage solution) - -* OpenStack Block Storage (cinder) - -* OpenStack Image service (glance) - -* OpenStack Networking (neutron) or legacy networking (nova-network) - -Excluding certain OpenStack components may limit or constrain the -functionality of other components. If a design opts to include -Orchestration but exclude Telemetry, then the design cannot take -advantage of Orchestration's auto scaling functionality (which relies on -information from Telemetry). Due to the fact that you can use -Orchestration to spin up a large number of instances to perform the -compute-intensive processing, we strongly recommend including -Orchestration in a compute-focused architecture design. - -Networking software -------------------- - -OpenStack Networking (neutron) provides a wide variety of networking -services for instances. There are many additional networking software -packages that may be useful to manage the OpenStack components -themselves. Some examples include HAProxy, Keepalived, and various -routing daemons (like Quagga). The OpenStack High Availability Guide -describes some of these software packages, HAProxy in particular. See -the `Network controller cluster stack -chapter `_ of -the OpenStack High Availability Guide. - -Management software -------------------- - -Management software includes software for providing: - -* Clustering - -* Logging - -* Monitoring - -* Alerting - -.. important:: - - The factors for determining which software packages in this category - to select is outside the scope of this design guide. - -The availability design requirements determine the selection of -Clustering Software, such as Corosync or Pacemaker. The availability of -the cloud infrastructure and the complexity of supporting the -configuration after deployment determines the impact of including these -software packages. The OpenStack High Availability Guide provides more -details on the installation and configuration of Corosync and Pacemaker. - -Operational considerations determine the requirements for logging, -monitoring, and alerting. Each of these sub-categories includes options. -For example, in the logging sub-category you could select Logstash, -Splunk, Log Insight, or another log aggregation-consolidation tool. -Store logs in a centralized location to facilitate performing analytics -against the data. Log data analytics engines can also provide automation -and issue notification, by providing a mechanism to both alert and -automatically attempt to remediate some of the more commonly known -issues. - -If you require any of these software packages, the design must account -for the additional resource consumption. Some other potential design -impacts include: - -* OS-Hypervisor combination: Ensure that the selected logging, - monitoring, or alerting tools support the proposed OS-hypervisor - combination. - -* Network hardware: The network hardware selection needs to be - supported by the logging, monitoring, and alerting software. - -Database software ------------------ - -Most OpenStack components require access to back-end database services -to store state and configuration information. Choose an appropriate -back-end database which satisfies the availability and fault tolerance -requirements of the OpenStack services. - -MySQL is the default database for OpenStack, but other compatible -databases are available. - -.. note:: - - Telemetry uses MongoDB. - -The chosen high availability database solution changes according to the -selected database. MySQL, for example, provides several options. Use a -replication technology such as Galera for active-active clustering. For -active-passive use some form of shared storage. Each of these potential -solutions has an impact on the design: - -* Solutions that employ Galera/MariaDB require at least three MySQL - nodes. - -* MongoDB has its own design considerations for high availability. - -* OpenStack design, generally, does not include shared storage. - However, for some high availability designs, certain components might - require it depending on the specific implementation. diff --git a/doc/arch-design-to-archive/source/storage-focus-operational-considerations.rst b/doc/arch-design-to-archive/source/storage-focus-operational-considerations.rst deleted file mode 100644 index a687d80838..0000000000 --- a/doc/arch-design-to-archive/source/storage-focus-operational-considerations.rst +++ /dev/null @@ -1,252 +0,0 @@ -Operational Considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Several operational factors affect the design choices for a general -purpose cloud. Operations staff receive tasks regarding the maintenance -of cloud environments for larger installations, including: - -Maintenance tasks - The storage solution should take into account storage maintenance - and the impact on underlying workloads. - -Reliability and availability - Reliability and availability depend on wide area network - availability and on the level of precautions taken by the service - provider. - -Flexibility - Organizations need to have the flexibility to choose between - off-premise and on-premise cloud storage options. This relies on - relevant decision criteria with potential cost savings. For example, - continuity of operations, disaster recovery, security, records - retention laws, regulations, and policies. - -Monitoring and alerting services are vital in cloud environments with -high demands on storage resources. These services provide a real-time -view into the health and performance of the storage systems. An -integrated management console, or other dashboards capable of -visualizing SNMP data, is helpful when discovering and resolving issues -that arise within the storage cluster. - -A storage-focused cloud design should include: - -* Monitoring of physical hardware resources. - -* Monitoring of environmental resources such as temperature and - humidity. - -* Monitoring of storage resources such as available storage, memory, - and CPU. - -* Monitoring of advanced storage performance data to ensure that - storage systems are performing as expected. - -* Monitoring of network resources for service disruptions which would - affect access to storage. - -* Centralized log collection. - -* Log analytics capabilities. - -* Ticketing system (or integration with a ticketing system) to track - issues. - -* Alerting and notification of responsible teams or automated systems - which remediate problems with storage as they arise. - -* Network Operations Center (NOC) staffed and always available to - resolve issues. - -Application awareness ---------------------- - -Well-designed applications should be aware of underlying storage -subsystems in order to use cloud storage solutions effectively. - -If natively available replication is not available, operations personnel -must be able to modify the application so that they can provide their -own replication service. In the event that replication is unavailable, -operations personnel can design applications to react such that they can -provide their own replication services. An application designed to -detect underlying storage systems can function in a wide variety of -infrastructures, and still have the same basic behavior regardless of -the differences in the underlying infrastructure. - -Fault tolerance and availability --------------------------------- - -Designing for fault tolerance and availability of storage systems in an -OpenStack cloud is vastly different when comparing the Block Storage and -Object Storage services. - -Block Storage fault tolerance and availability -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Configure Block Storage resource nodes with advanced RAID controllers -and high performance disks to provide fault tolerance at the hardware -level. - -Deploy high performing storage solutions such as SSD disk drives or -flash storage systems for applications requiring extreme performance out -of Block Storage devices. - -In environments that place extreme demands on Block Storage, we -recommend using multiple storage pools. In this case, each pool of -devices should have a similar hardware design and disk configuration -across all hardware nodes in that pool. This allows for a design that -provides applications with access to a wide variety of Block Storage -pools, each with their own redundancy, availability, and performance -characteristics. When deploying multiple pools of storage it is also -important to consider the impact on the Block Storage scheduler which is -responsible for provisioning storage across resource nodes. Ensuring -that applications can schedule volumes in multiple regions, each with -their own network, power, and cooling infrastructure, can give projects -the ability to build fault tolerant applications that are distributed -across multiple availability zones. - -In addition to the Block Storage resource nodes, it is important to -design for high availability and redundancy of the APIs, and related -services that are responsible for provisioning and providing access to -storage. We recommend designing a layer of hardware or software load -balancers in order to achieve high availability of the appropriate REST -API services to provide uninterrupted service. In some cases, it may -also be necessary to deploy an additional layer of load balancing to -provide access to back-end database services responsible for servicing -and storing the state of Block Storage volumes. We also recommend -designing a highly available database solution to store the Block -Storage databases. Leverage highly available database solutions such as -Galera and MariaDB to help keep database services online for -uninterrupted access, so that projects can manage Block Storage volumes. - -In a cloud with extreme demands on Block Storage, the network -architecture should take into account the amount of East-West bandwidth -required for instances to make use of the available storage resources. -The selected network devices should support jumbo frames for -transferring large blocks of data. In some cases, it may be necessary to -create an additional back-end storage network dedicated to providing -connectivity between instances and Block Storage resources so that there -is no contention of network resources. - -Object Storage fault tolerance and availability -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -While consistency and partition tolerance are both inherent features of -the Object Storage service, it is important to design the overall -storage architecture to ensure that the implemented system meets those -goals. The OpenStack Object Storage service places a specific number of -data replicas as objects on resource nodes. These replicas are -distributed throughout the cluster based on a consistent hash ring which -exists on all nodes in the cluster. - -Design the Object Storage system with a sufficient number of zones to -provide quorum for the number of replicas defined. For example, with -three replicas configured in the Swift cluster, the recommended number -of zones to configure within the Object Storage cluster in order to -achieve quorum is five. While it is possible to deploy a solution with -fewer zones, the implied risk of doing so is that some data may not be -available and API requests to certain objects stored in the cluster -might fail. For this reason, ensure you properly account for the number -of zones in the Object Storage cluster. - -Each Object Storage zone should be self-contained within its own -availability zone. Each availability zone should have independent access -to network, power and cooling infrastructure to ensure uninterrupted -access to data. In addition, a pool of Object Storage proxy servers -providing access to data stored on the object nodes should service each -availability zone. Object proxies in each region should leverage local -read and write affinity so that local storage resources facilitate -access to objects wherever possible. We recommend deploying upstream -load balancing to ensure that proxy services are distributed across the -multiple zones and, in some cases, it may be necessary to make use of -third-party solutions to aid with geographical distribution of services. - -A zone within an Object Storage cluster is a logical division. Any of -the following may represent a zone: - -* A disk within a single node - -* One zone per node - -* Zone per collection of nodes - -* Multiple racks - -* Multiple DCs - -Selecting the proper zone design is crucial for allowing the Object -Storage cluster to scale while providing an available and redundant -storage system. It may be necessary to configure storage policies that -have different requirements with regards to replicas, retention and -other factors that could heavily affect the design of storage in a -specific zone. - -Scaling storage services ------------------------- - -Adding storage capacity and bandwidth is a very different process when -comparing the Block and Object Storage services. While adding Block -Storage capacity is a relatively simple process, adding capacity and -bandwidth to the Object Storage systems is a complex task that requires -careful planning and consideration during the design phase. - -Scaling Block Storage -^^^^^^^^^^^^^^^^^^^^^ - -You can upgrade Block Storage pools to add storage capacity without -interrupting the overall Block Storage service. Add nodes to the pool by -installing and configuring the appropriate hardware and software and -then allowing that node to report in to the proper storage pool via the -message bus. This is because Block Storage nodes report into the -scheduler service advertising their availability. After the node is -online and available, projects can make use of those storage resources -instantly. - -In some cases, the demand on Block Storage from instances may exhaust -the available network bandwidth. As a result, design network -infrastructure that services Block Storage resources in such a way that -you can add capacity and bandwidth easily. This often involves the use -of dynamic routing protocols or advanced networking solutions to add -capacity to downstream devices easily. Both the front-end and back-end -storage network designs should encompass the ability to quickly and -easily add capacity and bandwidth. - -Scaling Object Storage -^^^^^^^^^^^^^^^^^^^^^^ - -Adding back-end storage capacity to an Object Storage cluster requires -careful planning and consideration. In the design phase, it is important -to determine the maximum partition power required by the Object Storage -service, which determines the maximum number of partitions which can -exist. Object Storage distributes data among all available storage, but -a partition cannot span more than one disk, although a disk can have -multiple partitions. - -For example, a system that starts with a single disk and a partition -power of 3 can have 8 (2^3) partitions. Adding a second disk means that -each has 4 partitions. The one-disk-per-partition limit means that this -system can never have more than 8 partitions, limiting its scalability. -However, a system that starts with a single disk and a partition power -of 10 can have up to 1024 (2^10) partitions. - -As you add back-end storage capacity to the system, the partition maps -redistribute data amongst the storage nodes. In some cases, this -replication consists of extremely large data sets. In these cases, we -recommend using back-end replication links that do not contend with -projects' access to data. - -As more projects begin to access data within the cluster and their data -sets grow, it is necessary to add front-end bandwidth to service data -access requests. Adding front-end bandwidth to an Object Storage cluster -requires careful planning and design of the Object Storage proxies that -projects use to gain access to the data, along with the high availability -solutions that enable easy scaling of the proxy layer. We recommend -designing a front-end load balancing layer that projects and consumers -use to gain access to data stored within the cluster. This load -balancing layer may be distributed across zones, regions or even across -geographic boundaries, which may also require that the design encompass -geo-location solutions. - -In some cases, you must add bandwidth and capacity to the network -resources servicing requests between proxy servers and storage nodes. -For this reason, the network architecture used for access to storage -nodes and proxy servers should make use of a design which is scalable. diff --git a/doc/arch-design-to-archive/source/storage-focus-prescriptive-examples.rst b/doc/arch-design-to-archive/source/storage-focus-prescriptive-examples.rst deleted file mode 100644 index 4c3d0d6b59..0000000000 --- a/doc/arch-design-to-archive/source/storage-focus-prescriptive-examples.rst +++ /dev/null @@ -1,142 +0,0 @@ -Prescriptive Examples -~~~~~~~~~~~~~~~~~~~~~ - -Storage-focused architecture depends on specific use cases. This section -discusses three example use cases: - -* An object store with a RESTful interface - -* Compute analytics with parallel file systems - -* High performance database - -The example below shows a REST interface without a high performance -requirement. - -Swift is a highly scalable object store that is part of the OpenStack -project. This diagram explains the example architecture: - -.. figure:: figures/Storage_Object.png - -The example REST interface, presented as a traditional Object store -running on traditional spindles, does not require a high performance -caching tier. - -This example uses the following components: - -Network: - -* 10 GbE horizontally scalable spine leaf back-end storage and front - end network. - -Storage hardware: - -* 10 storage servers each with 12x4 TB disks equaling 480 TB total - space with approximately 160 TB of usable space after replicas. - -Proxy: - -* 3x proxies - -* 2x10 GbE bonded front end - -* 2x10 GbE back-end bonds - -* Approximately 60 Gb of total bandwidth to the back-end storage - cluster - -.. note:: - - It may be necessary to implement a 3rd-party caching layer for some - applications to achieve suitable performance. - -Compute analytics with Data processing service ----------------------------------------------- - -Analytics of large data sets are dependent on the performance of the -storage system. Clouds using storage systems such as Hadoop Distributed -File System (HDFS) have inefficiencies which can cause performance -issues. - -One potential solution to this problem is the implementation of storage -systems designed for performance. Parallel file systems have previously -filled this need in the HPC space and are suitable for large scale -performance-orientated systems. - -OpenStack has integration with Hadoop to manage the Hadoop cluster -within the cloud. The following diagram shows an OpenStack store with a -high performance requirement: - -.. figure:: figures/Storage_Hadoop3.png - -The hardware requirements and configuration are similar to those of the -High Performance Database example below. In this case, the architecture -uses Ceph's Swift-compatible REST interface, features that allow for -connecting a caching pool to allow for acceleration of the presented -pool. - -High performance database with Database service ------------------------------------------------ - -Databases are a common workload that benefit from high performance -storage back ends. Although enterprise storage is not a requirement, -many environments have existing storage that OpenStack cloud can use as -back ends. You can create a storage pool to provide block devices with -OpenStack Block Storage for instances as well as object interfaces. In -this example, the database I-O requirements are high and demand storage -presented from a fast SSD pool. - -A storage system presents a LUN backed by a set of SSDs using a -traditional storage array with OpenStack Block Storage integration or a -storage platform such as Ceph or Gluster. - -This system can provide additional performance. For example, in the -database example below, a portion of the SSD pool can act as a block -device to the Database server. In the high performance analytics -example, the inline SSD cache layer accelerates the REST interface. - -.. figure:: figures/Storage_Database_+_Object5.png - -In this example, Ceph presents a Swift-compatible REST interface, as -well as a block level storage from a distributed storage cluster. It is -highly flexible and has features that enable reduced cost of operations -such as self healing and auto balancing. Using erasure coded pools are a -suitable way of maximizing the amount of usable space. - -.. note:: - - There are special considerations around erasure coded pools. For - example, higher computational requirements and limitations on the - operations allowed on an object; erasure coded pools do not support - partial writes. - -Using Ceph as an applicable example, a potential architecture would have -the following requirements: - -Network: - -* 10 GbE horizontally scalable spine leaf back-end storage and - front-end network - -Storage hardware: - -* 5 storage servers for caching layer 24x1 TB SSD - -* 10 storage servers each with 12x4 TB disks which equals 480 TB total - space with about approximately 160 TB of usable space after 3 - replicas - -REST proxy: - -* 3x proxies - -* 2x10 GbE bonded front end - -* 2x10 GbE back-end bonds - -* Approximately 60 Gb of total bandwidth to the back-end storage - cluster - -Using an SSD cache layer, you can present block devices directly to -hypervisors or instances. The REST interface can also use the SSD cache -systems as an inline cache. diff --git a/doc/arch-design-to-archive/source/storage-focus-technical-considerations.rst b/doc/arch-design-to-archive/source/storage-focus-technical-considerations.rst deleted file mode 100644 index 55bb640b8c..0000000000 --- a/doc/arch-design-to-archive/source/storage-focus-technical-considerations.rst +++ /dev/null @@ -1,62 +0,0 @@ -Technical considerations -~~~~~~~~~~~~~~~~~~~~~~~~ - -Some of the key technical considerations that are critical to a -storage-focused OpenStack design architecture include: - -Input-Output requirements - Input-Output performance requirements require researching and - modeling before deciding on a final storage framework. Running - benchmarks for Input-Output performance provides a baseline for - expected performance levels. If these tests include details, then - the resulting data can help model behavior and results during - different workloads. Running scripted smaller benchmarks during the - lifecycle of the architecture helps record the system health at - different points in time. The data from these scripted benchmarks - assist in future scoping and gaining a deeper understanding of an - organization's needs. - -Scale - Scaling storage solutions in a storage-focused OpenStack - architecture design is driven by initial requirements, including - :term:`IOPS `, capacity, - bandwidth, and future needs. Planning capacity based on projected - needs over the course of a budget cycle is important for a design. - The architecture should balance cost and capacity, while also allowing - flexibility to implement new technologies and methods as they become - available. - -Security - Designing security around data has multiple points of focus that - vary depending on SLAs, legal requirements, industry regulations, - and certifications needed for systems or people. Consider compliance - with HIPPA, ISO9000, and SOX based on the type of data. For certain - organizations, multiple levels of access control are important. - -OpenStack compatibility - Interoperability and integration with OpenStack can be paramount in - deciding on a storage hardware and storage management platform. - Interoperability and integration includes factors such as OpenStack - Block Storage interoperability, OpenStack Object Storage - compatibility, and hypervisor compatibility (which affects the - ability to use storage for ephemeral instance storage). - -Storage management - You must address a range of storage management-related - considerations in the design of a storage-focused OpenStack cloud. - These considerations include, but are not limited to, backup - strategy (and restore strategy, since a backup that cannot be - restored is useless), data valuation-hierarchical storage - management, retention strategy, data placement, and workflow - automation. - -Data grids - Data grids are helpful when answering questions around data - valuation. Data grids improve decision making through correlation of - access patterns, ownership, and business-unit revenue with other - metadata values to deliver actionable information about data. - -When building a storage-focused OpenStack architecture, strive to build -a flexible design based on an industry standard core. One way of -accomplishing this might be through the use of different back ends -serving different use cases. diff --git a/doc/arch-design-to-archive/source/storage-focus.rst b/doc/arch-design-to-archive/source/storage-focus.rst deleted file mode 100644 index 19210b5788..0000000000 --- a/doc/arch-design-to-archive/source/storage-focus.rst +++ /dev/null @@ -1,61 +0,0 @@ -=============== -Storage focused -=============== - -.. toctree:: - :maxdepth: 2 - - storage-focus-technical-considerations.rst - storage-focus-operational-considerations.rst - storage-focus-architecture.rst - storage-focus-prescriptive-examples.rst - -Cloud storage is a model of data storage that stores digital data in -logical pools and physical storage that spans across multiple servers -and locations. Cloud storage commonly refers to a hosted object storage -service, however the term also includes other types of data storage that -are available as a service, for example block storage. - -Cloud storage runs on virtualized infrastructure and resembles broader -cloud computing in terms of accessible interfaces, elasticity, -scalability, multi-tenancy, and metered resources. You can use cloud -storage services from an off-premises service or deploy on-premises. - -Cloud storage consists of many distributed, synonymous resources, which -are often referred to as integrated storage clouds. Cloud storage is -highly fault tolerant through redundancy and the distribution of data. -It is highly durable through the creation of versioned copies, and can -be consistent with regard to data replicas. - -At large scale, management of data operations is a resource intensive -process for an organization. Hierarchical storage management (HSM) -systems and data grids help annotate and report a baseline data -valuation to make intelligent decisions and automate data decisions. HSM -enables automated tiering and movement, as well as orchestration of data -operations. A data grid is an architecture, or set of services evolving -technology, that brings together sets of services enabling users to -manage large data sets. - -Example applications deployed with cloud storage characteristics: - -* Active archive, backups and hierarchical storage management. - -* General content storage and synchronization. An example of this is - private dropbox. - -* Data analytics with parallel file systems. - -* Unstructured data store for services. For example, social media - back-end storage. - -* Persistent block storage. - -* Operating system and application image store. - -* Media streaming. - -* Databases. - -* Content distribution. - -* Cloud storage peering.

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