Merge "[arch-guide-archive] Removing old arch guide from master"

doc-tools-check-languages.conf

@@ -33,6 +33,4 @@ declare -A SPECIAL_BOOKS=(
     ["contributor-guide"]="skip"
     ["releasenotes"]="skip"
     ["ha-guide-draft"]="skip"
-    # Skip old arch design, will be archived
-    ["arch-design-to-archive"]="skip"
 )

doc/arch-design-to-archive/setup.cfg

@@ -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

doc/arch-design-to-archive/setup.py

@@ -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)

doc/arch-design-to-archive/source/common

@@ -1 +0,0 @@
-../../common

doc/arch-design-to-archive/source/compute-focus-architecture.rst

@@ -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 <https://docs.openstack.org/ha-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.

doc/arch-design-to-archive/source/compute-focus-operational-considerations.rst

@@ -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.

doc/arch-design-to-archive/source/compute-focus-prescriptive-examples.rst

@@ -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 <cell>` 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 <https://flume.apache.org/>`__, `Elastic
-Search <https://www.elastic.co/>`__,
-`Kibana <https://www.elastic.co/products/kibana>`__, and the CERN
-developed `Lemon <http://lemon.web.cern.ch/lemon/index.shtml>`__
-project.

doc/arch-design-to-archive/source/compute-focus-technical-considerations.rst

@@ -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
-<https://github.com/noslzzp/cloud-resource-calculator/blob/master/cloud-resource-calculator.ods>`_.
-
-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 <https://docs.openstack.org/ops-guide/ops-user-facing-operations.html#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.

doc/arch-design-to-archive/source/compute-focus.rst

@@ -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.

doc/arch-design-to-archive/source/conf.py

@@ -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.
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-
-
-# -- 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 = {}
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-# Add any paths that contain custom themes here, relative to this directory.
-# html_theme_path = [openstackdocstheme.get_html_theme_path()]
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-# html_short_title = None
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-# of the sidebar.
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-# html_favicon = None
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-# Add any paths that contain custom static files (such as style sheets) here,
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-# html_domain_indices = True
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-# 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
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-# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
-# html_show_sphinx = True
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-# html_show_copyright = True
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-# If true, an OpenSearch description file will be output, and all pages will
-# contain a <link> tag referring to it.  The value of this option must be the
-# base URL from which the finished HTML is served.
-# html_use_opensearch = ''
-
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-# html_file_suffix = None
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-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',
-
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-# (source start file, name, description, authors, manual section).
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-    ('index', 'ArchDesignRst', u'Architecture Design Guide',
-     [u'OpenStack contributors'], 1)
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doc/arch-design-to-archive/source/generalpurpose-architecture.rst

@@ -1,483 +0,0 @@
-============
-Architecture
-============
-
-Hardware selection involves three key areas:
-
-* Compute
-
-* Network
-
-* Storage
-
-Hardware for a general 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<kernel-based VM (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 <https://docs.openstack.org/ha-guide/networking-ha.html>`__ 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 <https://docs.openstack.org/ha-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.

doc/arch-design-to-archive/source/generalpurpose-operational-considerations.rst

@@ -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 <Compute API (Nova 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 <https://docs.openstack.org/ops>`_.

doc/arch-design-to-archive/source/generalpurpose-prescriptive-example.rst

@@ -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.
-

doc/arch-design-to-archive/source/generalpurpose-technical-considerations.rst

@@ -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 <https://wiki.openstack.org/wiki/HypervisorSupportMatrix>`_.
-
-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 <https://docs.openstack.org/ops-guide/arch-network-design.html#network-topology>`
-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 <https://docs.openstack.org/ha-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 <https://docs.openstack.org/security-guide/>`_.

doc/arch-design-to-archive/source/generalpurpose-user-requirements.rst

@@ -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.

doc/arch-design-to-archive/source/generalpurpose.rst

@@ -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

doc/arch-design-to-archive/source/hybrid-architecture.rst

@@ -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 <http://libguestfs.org/virt-v2v>`_
-and `virt-edit <http://libguestfs.org/virt-edit.1.html>`_.
-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 <Database service
-  (trove)>` 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 <security>` 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.

doc/arch-design-to-archive/source/hybrid-operational-considerations.rst

@@ -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.

doc/arch-design-to-archive/source/hybrid-prescriptive-examples.rst

@@ -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
-<https://blueprints.launchpad.net/cinder/+spec/cinder-backup-volume-metadata-support>`_.
-
-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.

doc/arch-design-to-archive/source/hybrid-technical-considerations.rst

@@ -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.

doc/arch-design-to-archive/source/hybrid-user-requirements.rst

@@ -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.

doc/arch-design-to-archive/source/hybrid.rst

@@ -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 <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.

doc/arch-design-to-archive/source/index.rst

@@ -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

doc/arch-design-to-archive/source/introduction-how-this-book-is-organized.rst

@@ -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<generalpurpose>`: Uses common components that
-   address 80% of common use cases.
-
-*  :doc:`Compute focused<compute-focus>`: For compute intensive workloads
-   such as high performance computing (HPC).
-
-*  :doc:`Storage focused<storage-focus>`: For storage intensive workloads
-   such as data analytics with parallel file systems.
-
-*  :doc:`Network focused<network-focus>`: For high performance and
-   reliable networking, such as a :term:`content delivery network (CDN)`.
-
-*  :doc:`Multi-site<multi-site>`: For applications that require multiple
-   site deployments for geographical, reliability or data locality
-   reasons.
-
-*  :doc:`Hybrid cloud<hybrid>`: Uses multiple disparate clouds connected
-   either for failover, hybrid cloud bursting, or availability.
-
-*  :doc:`Massively scalable<massively-scalable>`: For cloud service
-   providers or other large installations.
-
-*  :doc:`Specialized cases<specialized>`: Architectures that have not
-   previously been covered in the defined use cases.

doc/arch-design-to-archive/source/introduction-how-this-book-was-written.rst

@@ -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 <http://twitter.com/hui_kenneth>`__
-
-*  Alexandra Settle (Rackspace)
-   `@dewsday <http://twitter.com/dewsday>`__
-
-*  Anthony Veiga (Comcast) `@daaelar <http://twitter.com/daaelar>`__
-
-*  Beth Cohen (Verizon) `@bfcohen <http://twitter.com/bfcohen>`__
-
-*  Kevin Jackson (Rackspace)
-   `@itarchitectkev <http://twitter.com/itarchitectkev>`__
-
-*  Maish Saidel-Keesing (Cisco)
-   `@maishsk <http://twitter.com/maishsk>`__
-
-*  Nick Chase (Mirantis) `@NickChase <http://twitter.com/NickChase>`__
-
-*  Scott Lowe (VMware) `@scott\_lowe <http://twitter.com/scott_lowe>`__
-
-*  Sean Collins (Comcast) `@sc68cal <http://twitter.com/sc68cal>`__
-
-*  Sean Winn (Cloudscaling)
-   `@seanmwinn <http://twitter.com/seanmwinn>`__
-
-*  Sebastian Gutierrez (Red Hat) `@gutseb <http://twitter.com/gutseb>`__
-
-*  Stephen Gordon (Red Hat) `@xsgordon <http://twitter.com/xsgordon>`__
-
-*  Vinny Valdez (Red Hat)
-   `@VinnyValdez <http://twitter.com/VinnyValdez>`__

doc/arch-design-to-archive/source/introduction-intended-audience.rst

@@ -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 <https://docs.openstack.org/ops-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.

doc/arch-design-to-archive/source/introduction-methodology.rst

@@ -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.

doc/arch-design-to-archive/source/introduction.rst

@@ -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.

doc/arch-design-to-archive/source/legal-security-requirements.rst

@@ -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 <http://ec.europa.eu/justice/data-protection/>`_
-of the European Union and the requirements of the
-`Financial Industry Regulatory Authority
-<http://www.finra.org/Industry/Regulation/FINRARules/>`_
-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 <https://docs.openstack.org/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.

doc/arch-design-to-archive/source/massively-scalable-operational-considerations.rst

@@ -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
-<https://www.openstack.org/marketplace/training/>`_.

doc/arch-design-to-archive/source/massively-scalable-technical-considerations.rst

@@ -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 <cell>` 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.

doc/arch-design-to-archive/source/massively-scalable-user-requirements.rst

@@ -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 <http://www.opencompute.org>`_)
-  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.

doc/arch-design-to-archive/source/massively-scalable.rst

@@ -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.

doc/arch-design-to-archive/source/multi-site-architecture.rst

@@ -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.

doc/arch-design-to-archive/source/multi-site-operational-considerations.rst

@@ -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 <https://docs.openstack.org/ops-guide/ops-logging-monitoring.html>`__
-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 <https://docs.openstack.org/ops-guide/ops-upgrades.html>`__
-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 <https://docs.openstack.org/ops-guide/ops-projects-users.html>`__
-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.

doc/arch-design-to-archive/source/multi-site-prescriptive-examples.rst

@@ -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**

doc/arch-design-to-archive/source/multi-site-technical-considerations.rst

@@ -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
-<https://docs.openstack.org/developer/swift/replication_network.html#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.

doc/arch-design-to-archive/source/multi-site-user-requirements.rst

@@ -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 <https://docs.openstack.org/ha-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.

doc/arch-design-to-archive/source/multi-site.rst

@@ -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.

doc/arch-design-to-archive/source/network-focus-architecture.rst

@@ -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<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<Layer-2 network>` 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.

doc/arch-design-to-archive/source/network-focus-operational-considerations.rst

@@ -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.

doc/arch-design-to-archive/source/network-focus-prescriptive-examples.rst

@@ -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<north-south traffic>` and
-:term:`east-west<east-west traffic>` 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.

doc/arch-design-to-archive/source/network-focus-technical-considerations.rst

@@ -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<Address Resolution Protocol (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<Border Gateway Protocol (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
-<http://media.beldensolutions.com/garrettcom/techsupport/papers/ethernet_switch_reliability.pdf>`_
-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
-<http://www.internetsociety.org/deploy360/blog/2014/05/goodbye-ipv4-iana-starts-allocating-final-address-blocks/>`_.
-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.

doc/arch-design-to-archive/source/network-focus-user-requirements.rst

@@ -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.

doc/arch-design-to-archive/source/network-focus.rst

@@ -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.
-

doc/arch-design-to-archive/source/references.rst

@@ -1,85 +0,0 @@
-==========
-References
-==========
-
-`Data Protection framework of the European Union
-<http://ec.europa.eu/justice/data-protection/>`_
-: Guidance on Data Protection laws governed by the EU.
-
-`Depletion of IPv4 Addresses
-<http://www.internetsociety.org/deploy360/blog/2014/05/
-goodbye-ipv4-iana-starts-allocating-final-address-blocks/>`_
-: describing how IPv4 addresses and the migration to IPv6 is inevitable.
-
-`Ethernet Switch Reliability <http://www.garrettcom.com/
-techsupport/papers/ethernet_switch_reliability.pdf>`_
-: Research white paper on Ethernet Switch reliability.
-
-`Financial Industry Regulatory Authority
-<http://www.finra.org/Industry/Regulation/FINRARules/>`_
-: Requirements of the Financial Industry Regulatory Authority in the USA.
-
-`Image Service property keys <https://docs.openstack.org/
-cli-reference/glance.html#image-service-property-keys>`_
-: Glance API property keys allows the administrator to attach custom
-characteristics to images.
-
-`LibGuestFS Documentation <http://libguestfs.org>`_
-: Official LibGuestFS documentation.
-
-`Logging and Monitoring
-<https://docs.openstack.org/ops-guide/ops-logging-monitoring.html>`_
-: Official OpenStack Operations documentation.
-
-`ManageIQ Cloud Management Platform <http://manageiq.org/>`_
-: An Open Source Cloud Management Platform for managing multiple clouds.
-
-`N-Tron Network Availability
-<https://www.scribd.com/doc/298973976/Network-Availability>`_
-: Research white paper on network availability.
-
-`Nested KVM <http://davejingtian.org/2014/03/30/nested-kvm-just-for-fun>`_
-: Post on how to nest KVM under KVM.
-
-`Open Compute Project <http://www.opencompute.org/>`_
-: 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
-<https://docs.openstack.org/ops-guide/ops-user-facing-operations.html#flavors>`_
-: Official OpenStack documentation.
-
-`OpenStack High Availability Guide <https://docs.openstack.org/ha-guide/>`_
-: Information on how to provide redundancy for the OpenStack components.
-
-`OpenStack Hypervisor Support Matrix
-<https://wiki.openstack.org/wiki/HypervisorSupportMatrix>`_
-: Matrix of supported hypervisors and capabilities when used with OpenStack.
-
-`OpenStack Object Store (Swift) Replication Reference
-<https://docs.openstack.org/developer/swift/replication_network.html>`_
-: Developer documentation of Swift replication.
-
-`OpenStack Operations Guide <https://docs.openstack.org/ops-guide/>`_
-: The OpenStack Operations Guide provides information on setting up
-and installing OpenStack.
-
-`OpenStack Security Guide <https://docs.openstack.org/security-guide/>`_
-: The OpenStack Security Guide provides information on securing
-OpenStack deployments.
-
-`OpenStack Training Marketplace
-<https://www.openstack.org/marketplace/training>`_
-: The OpenStack Market for training and Vendors providing training
-on OpenStack.
-
-`PCI passthrough <https://wiki.openstack.org/wiki/
-Pci_passthrough#How_to_check_PCI_status_with_PCI_api_paches>`_
-: 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 <https://wiki.openstack.org/wiki/TripleO>`_
-: TripleO is a program aimed at installing, upgrading and operating
-OpenStack clouds using OpenStack's own cloud facilities as the foundation.

doc/arch-design-to-archive/source/specialized-desktop-as-a-service.rst

@@ -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

doc/arch-design-to-archive/source/specialized-hardware.rst

@@ -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
-<https://docs.openstack.org/cli-reference/glance.html#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 <https://wiki.openstack.org/
-wiki/Pci_passthrough#How_to_check_PCI_status_with_PCI_api_patches>`_.
-
-.. figure:: figures/Specialized_Hardware2.png
-   :width: 100%

doc/arch-design-to-archive/source/specialized-multi-hypervisor.rst

@@ -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.

doc/arch-design-to-archive/source/specialized-networking.rst

@@ -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.

doc/arch-design-to-archive/source/specialized-openstack-on-openstack.rst

@@ -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
-<http://davejingtian.org/2014/03/30/nested-kvm-just-for-fun/>`_),
-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%

doc/arch-design-to-archive/source/specialized-software-defined-networking.rst

@@ -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
-

doc/arch-design-to-archive/source/specialized.rst

@@ -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 <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)
-  <specialized-software-defined-networking>`:
-  describes both running an SDN controller from within OpenStack
-  as well as participating in a software-defined network.
-* :doc:`Desktop-as-a-Service <specialized-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 <specialized-openstack-on-openstack>`:
-  describes building a multi-tiered cloud by running OpenStack
-  on top of an OpenStack installation.
-* :doc:`Specialized hardware <specialized-hardware>`:
-  describes the use of specialized hardware devices from within
-  the OpenStack environment.

doc/arch-design-to-archive/source/storage-focus-architecture.rst

@@ -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 <https://docs.openstack.org/ha-guide/networking-ha.html>`_ 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.

doc/arch-design-to-archive/source/storage-focus-operational-considerations.rst

@@ -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.

doc/arch-design-to-archive/source/storage-focus-prescriptive-examples.rst

@@ -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.

doc/arch-design-to-archive/source/storage-focus-technical-considerations.rst

@@ -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 <Input/output Operations Per Second (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.

doc/arch-design-to-archive/source/storage-focus.rst

@@ -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.