openstack-manuals/doc/src/docbkx/openstack-ops/src/ch_arch_cloud_controller.xml

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xml:id="cloud_controller_design">
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<title>Cloud Controller Design</title>
<para>OpenStack is designed to be massively horizontally scalable,
which allows all services to be distributed widely. However,
to simplify this guide we have decided to discuss services of
a more central nature using the concept of a single
<emphasis>cloud controller</emphasis> (see the <xref
linkend="example_architecture"/> for more details on the
overall architecture). As described in this guide, the cloud
controller is a single node that hosts the databases, message
queue service, authentication and authorization service, image
management service, user dashboard, and <glossterm>API
endpoint</glossterm>s.</para>
<para>The cloud controller provides the central management system
for multi-node OpenStack deployments. Typically the cloud
controller manages authentication and sends messaging to all
the systems through a message queue. For our example, the
cloud controller has a collection of <code>nova-*</code>
components that represent the global state of the cloud, talks
to services such as authentication, maintains information
about the cloud in a database, communicates to all compute
nodes and storage <glossterm>worker</glossterm>s through a
queue, and provides API access. Each service running on a
designated cloud controller may be broken out into separate
nodes for scalability or availability.</para>
<section xml:id="hardware_consid">
<title>Hardware Considerations</title>
<para>A cloud controller's hardware can be the same as a
compute node, though you may want to further specify based
on the size and type of cloud that you run. It's also
possible to use virtual machines for all or some of the
services that the cloud controller manages, such as the
message queuing. In this guide, we assume that all
services are running directly on the cloud
controller.</para>
<para>To size the server correctly, and determine whether to
virtualize any part of it, you should estimate:</para>
<itemizedlist role="compact">
<listitem>
<para>The number of instances that you expect to
run</para>
</listitem>
<listitem>
<para>The number of compute nodes that you have</para>
</listitem>
<listitem>
<para>The number of users who will access the compute
or storage services</para>
</listitem>
<listitem>
<para>Whether users interact with your cloud through
the REST API or the dashboard</para>
</listitem>
<listitem>
<para>Whether users authenticate against an external
system (such as, LDAP or <glossterm>Active
Directory</glossterm>)</para>
</listitem>
<listitem>
<para>How long you expect single instances to
run</para>
</listitem>
</itemizedlist>
<?hard-pagebreak?>
<informaltable rules="all">
<thead>
<tr>
<th>Consideration</th>
<th>Ramification</th>
</tr>
</thead>
<tbody>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How many instances will run at
once?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Size your database server
accordingly, and scale out beyond one
cloud controller if many instances will
report status at the same time and
scheduling where a new instance starts up
needs computing power.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How many compute nodes will run at
once?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Ensure that your messaging queue
handles requests successfully and size
accordingly.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How many users will access the
API?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>If many users will make multiple
requests, make sure that the CPU load for
the cloud controller can handle.
it.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How many users will access the
<glossterm>dashboard</glossterm>?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>The dashboard makes many requests,
even more than the API access, so add even
more CPU if your dashboard is the main
interface for your users.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How many <code>nova-api</code>
services do you run at once for your
cloud?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>You need to size the controller
with a core per service.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>How long does a single instance
run?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Starting instances and deleting
instances is demanding on the compute node
but also demanding on the controller node
because of all the API queries and
scheduling needs.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Does your authentication system
also verify externally?</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Ensure network connectivity between
the cloud controller and external
authentication system are good and that
the cloud controller has the CPU power to
keep up with requests.</para></td>
</tr>
</tbody>
</informaltable>
</section>
<section xml:id="separate_services">
<title>Separation of Services</title>
<para>While our example contains all central services in a
single location, it is possible and indeed often a good
idea to separate services onto different physical servers.
The following is a list of deployment scenarios we've
seen, and their justifications.</para>
<informaltable rules="all">
<col width="25%"/>
<col width="75%"/>
<tbody>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Run <code>glance-*</code> servers
on the <code>swift-proxy</code>
server</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>This deployment felt the spare I/O
on the Object Storage proxy server was
sufficient, and the Image Delivery portion
of Glance benefited from being on physical
hardware and having good connectivity to
the Object Storage back-end it was
using.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Run a central dedicated database
server</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>This deployment made a central
dedicated server to provide the databases
for all services. This simplified
operations by isolating database server
updates, and allowed for the simple
creation of slave database servers for
failover.</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Run one VM per service</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>This deployment ran central
services on a set of servers running KVM.
A dedicated VM was created for each
service (nova-scheduler, rabbitmq,
database etc). This assisted the
deployment with scaling as they could tune
the resources given to each virtual
machine based on the load they received
(something that was not well understood
during installation).</para></td>
</tr>
<tr>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>Use an external load
balancer</para></td>
<td xmlns:db="http://docbook.org/ns/docbook"
><para>This deployment had an expensive
hardware load balancer in their
organisation. They ran multiple
<code>nova-api</code> and
<code>swift-proxy</code> servers on
different physical servers and used the
load balancer to switch between
them.</para></td>
</tr>
</tbody>
</informaltable>
<para>One choice that always comes up is whether to virtualize
or not. Some services, such as nova-compute, swift-proxy
and swift-object servers, should not be virtualized.
However, control servers can often be happily virtualized
- the performance penalty can usually be offset by simply
running more of the service.</para>
</section>
<section xml:id="database">
<title>Database</title>
<para>Most OpenStack Compute central services, and currently
also the nova-compute nodes, use the database for stateful
information. Loss of this ability leads to errors. As a
result, we recommend that you cluster your databases in
some way to make them failure tolerant.</para>
</section>
<section xml:id="message_queue">
<title>Message Queue</title>
<para>Most OpenStack Compute services communicate with each
other using the Message Queue. In general, if the message
queue fails or becomes inaccessible, the cluster grinds to
a halt and ends up in a "read only" state, with
information stuck at the point where the last message was
sent. Accordingly, we recommend that you cluster the
message queue - and RabbitMQ has in-build abilities to do
this.</para>
</section>
<section xml:id="api">
<title>Application Programming Interface
(API)</title>
<para>All public access, whether direct, through a command
line client, or through the web-based dashboard, uses the
API service. Find the API reference at <link
xlink:href="http://api.openstack.org/"
>http://api.openstack.org/</link>.</para>
<para>You must choose whether you want to support the Amazon
EC2 compatibility APIs, or just the OpenStack APIs. One
issue you might encounter when running both APIs is an
inconsistent experience when referring to images and
instances. For example, the EC2 API refers to instances
using IDs that contain hexadecimal whereas the OpenStack
API uses names and digits. Similarly, the EC2 API tends to
rely on DNS aliases for contacting virtual machines, as
opposed to OpenStack which typically lists IP addresses.
If OpenStack is not setup in the right way, it is simple
to have scenarios where users are unable to contact their
instances due to only having an incorrect DNS alias.
Despite this, EC2 compatibility can assist users migrating
to your cloud.</para>
<para>Like databases and message queues, having more than one
<glossterm>API server</glossterm> is a good thing.
Traditional HTTP load balancing techniques can be used to
achieve a highly available <code>nova-api</code>
service.</para>
</section>
<section xml:id="extensions">
<title>Extensions</title>
<para>The <link xlink:title="API Specifications"
xlink:href="http://docs.openstack.org/api/api-specs.html"
>API Specifications</link>
(http://docs.openstack.org/api/api-specs.html) define the
core actions, capabilities, and media-types of the
OpenStack API. A client can always depend on the
availability of this core API and implementers are always
required to support it in its entirety. Requiring strict
adherence to the core API allows clients to rely upon a
minimal level of functionality when interacting with
multiple implementations of the same API.</para>
<para>The OpenStack Compute API is extensible. An extension
adds capabilities to an API beyond those defined in the
core. The introduction of new features, MIME types,
actions, states, headers, parameters, and resources can
all be accomplished by means of extensions to the core
API. This allows the introduction of new features in the
API without requiring a version change and allows the
introduction of vendor-specific niche
functionality.</para>
</section>
<section xml:id="scheduler">
<title>Scheduler</title>
<para>Fitting various sized virtual machines (different
<emphasis>flavors</emphasis>) into different sized
physical nova-compute nodes is a challenging problem -
researched generically in Computer Science as a packing
problem. There are various techniques for handling this
problem, one of which is to have flavor sizes scale
linearly, be a proportional fraction of your physical node
capacity, though solving this problem is out of the scope
of this book. To support your scheduling choices,
OpenStack Compute provides several different types of
scheduling drivers, a full discussion of which is found in
the <link xlink:title="API Specifications"
xlink:href="http://docs.openstack.org/folsom/openstack-compute/admin/content/ch_scheduling.html"
>reference manual</link>
(http://docs.openstack.org/folsom/openstack-compute/admin/content/ch_scheduling.html).</para>
<para>For availability purposes, or for very large or
high-schedule frequency installations, you should consider
running multiple nova-scheduler services. No special load
balancing is required, as the nova-scheduler communicates
entirely using the message queue.</para>
</section>
<section xml:id="images">
<title>Images</title>
<para>The OpenStack Image Catalog and Delivery service
consists of two parts - <code>glance-api</code> and
<code>glance-registry</code>. The former is
responsible for the delivery of images and the compute
node uses it to download images from the back-end. The
latter maintains the metadata information associated with
virtual machine images and requires a database.</para>
<para>The <code>glance-api</code> part is an abstraction layer
that allows a choice of back-end. Currently, it
supports:</para>
<itemizedlist role="compact">
<listitem>
<para>OpenStack Object Storage. Allows you to store
images as objects.</para>
</listitem>
<listitem>
<para>File system. Uses any traditional file system to
store the images as files.</para>
</listitem>
<listitem>
<para>S3. Allows you to fetch images from Amazon S3. </para>
</listitem>
<listitem>
<para>HTTP. Allows you to fetch images from a web
server. You cannot write images by using this
mode.</para>
</listitem>
</itemizedlist>
<para>If you have an OpenStack Object Storage service, we recommend using this as a scalable
place to store your images. You can also use a file system with sufficient performance
or Amazon S3 - unless you do not need the ability to upload new images through
OpenStack.</para>
</section>
<section xml:id="dashboard">
<title>Dashboard</title>
<para>The OpenStack Dashboard is implemented as a Python web
application that runs in <glossterm>Apache</glossterm>
<code>httpd</code>. Therefore, you may treat it the same
as any other web application, provided it can reach the
API servers (including their admin endpoints) over the
network.</para>
</section>
<?hard-pagebreak?>
<section xml:id="authentication">
<title>Authentication and Authorization</title>
<para>The concepts supporting OpenStack's authentication and
authorization are derived from well understood and widely
used systems of a similar nature. Users have credentials
they can use to authenticate, and they can be a member of
one or more groups (known as projects or tenants
interchangeably). For example a cloud administrator might
be able to list all instances in the cloud, whereas a user
can only see those in their current group. Resources
quotas, such as the number of cores that can be used, disk
space, and so on, are associated with a project.</para>
<para>The OpenStack Identity Service (Keystone) is the point
that provides the authentication decisions and user
attribute information, which is then used by the other
OpenStack services to perform authorization. Policy is set
in the policy.json file. For information on how to
configure these, see <xref
linkend="projects_users"/>.</para>
<para>The Identity Service supports different plugins for
back-end authentication decisions, and storing
information. These range from pure storage choices to
external systems and currently include:</para>
<itemizedlist role="compact">
<listitem>
<para>In-memory Key-Value Store</para>
</listitem>
<listitem>
<para>SQL database</para>
</listitem>
<listitem>
<para>PAM</para>
</listitem>
<listitem>
<para>LDAP</para>
</listitem>
</itemizedlist>
<para>Many deployments use the SQL database, however LDAP is
also a popular choice for those with existing
authentication infrastructure that needs to be
integrated.</para>
</section>
<?hard-pagebreak?>
<section xml:id="network_consid">
<title>Network Considerations</title>
<para>Because the cloud controller handles so many different
services, it must be able to handle the amount of traffic
that hits it. For example, if you choose to host the
OpenStack Imaging Service on the cloud controller, the
cloud controller should be able to support the
transferring of the images at an acceptable speed.</para>
<para>As another example, if you choose to use single-host
networking where the cloud controller is the network
gateway for all instances, then the Cloud Controller must
support the total amount of traffic that travels between
your cloud and the public Internet. </para>
<para>It is recommended to use a fast NIC, such as 10 GB. You
can also choose to use two 10gb NICs and bond them
together. While you might not be able to get a full bonded
20gb speed, different transmission streams use different
NICs. For example, if the Cloud Controller transfers two
images, each image uses a different NIC and gets a full 10
GB of bandwidth.</para>
</section>
</section>