openstack-manuals/doc/arch-design/massively_scalable/section_operational_considerations_massively_scalable.xml
Thomas Kaergel b0337e125c replace 'metrics' with 'meters' wherever it makes sense
Replaced 'metrics' with 'meters' in all contexts, excluding the
metric system or  where the term is used as name.

Change-Id: If4c32dfe92c28a2079a485a6aec1d61c7b9999a1
Closes-Bug: #1446518
2015-06-16 13:49:56 +02:00

103 lines
5.9 KiB
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<?xml version="1.0" encoding="UTF-8"?>
<section xmlns="http://docbook.org/ns/docbook"
xmlns:xi="http://www.w3.org/2001/XInclude"
xmlns:xlink="http://www.w3.org/1999/xlink"
version="5.0"
xml:id="operational-considerations-massive-scale">
<?dbhtml stop-chunking?>
<title>Operational considerations</title>
<para>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 increase 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 impossible for
staff to give each system individual care.</para>
<para>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.</para>
<para>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 then quickly 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.</para>
<section xml:id="the-bleeding-edge">
<title>The bleeding edge</title>
<para>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.</para>
<para>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.</para>
<para>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.</para>
<para>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.</para></section>
<section xml:id="growth-and-capacity-planning">
<title>Growth and capacity planning</title>
<para>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 tenants 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.</para></section>
<section xml:id="skills-and-training">
<title>Skills and training</title>
<para>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: <link
xlink:href="http://www.openstack.org/marketplace/training/">http://www.openstack.org/marketplace/training/</link>.
</para>
</section>
</section>