182 lines
8.3 KiB
XML
182 lines
8.3 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
|
|
<!DOCTYPE section [
|
|
<!ENTITY % openstack SYSTEM "../../common/entities/openstack.ent">
|
|
%openstack;
|
|
]>
|
|
<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="user-requirements-compute-focus">
|
|
<?dbhtml stop-chunking?>
|
|
<title>User requirements</title>
|
|
<para>Compute intensive workloads are defined by their high
|
|
utilization of CPU, RAM, or both. User requirements will
|
|
determine if a cloud must be built to accommodate anticipated
|
|
performance demands.
|
|
</para>
|
|
<variablelist>
|
|
<varlistentry>
|
|
<term>Cost</term>
|
|
<listitem>
|
|
<para>Cost is not generally a primary concern for a
|
|
compute-focused cloud, however some organizations
|
|
might be concerned with cost avoidance. Repurposing
|
|
existing resources to tackle compute-intensive tasks
|
|
instead of needing to acquire additional resources may
|
|
offer cost reduction opportunities.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>Time to market</term>
|
|
<listitem>
|
|
<para>Compute-focused clouds can be used
|
|
to deliver products more quickly, for example,
|
|
speeding up a company's software development life cycle
|
|
(SDLC) for building products and applications.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>Revenue opportunity</term>
|
|
<listitem>
|
|
<para>Companies that are interested
|
|
in building services or products that rely on the
|
|
power of the compute resources will benefit from a
|
|
compute-focused cloud. Examples include the analysis
|
|
of large data sets (via Hadoop or Cassandra) or
|
|
completing computational intensive tasks such as
|
|
rendering, scientific computation, or
|
|
simulations.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
</variablelist>
|
|
<section xml:id="legal-requirements-compute-focus">
|
|
<title>Legal requirements</title>
|
|
<para>Many jurisdictions have legislative and regulatory
|
|
requirements governing the storage and management of data in
|
|
cloud environments. Common areas of regulation include:</para>
|
|
<itemizedlist>
|
|
<listitem>
|
|
<para>Data retention policies ensuring storage of
|
|
persistent data and records management to meet data
|
|
archival requirements.</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>Data ownership policies governing the possession and
|
|
responsibility for data.</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>Data sovereignty policies governing the storage of
|
|
data in foreign countries or otherwise separate
|
|
jurisdictions.</para>
|
|
</listitem>
|
|
<listitem>
|
|
<para>Data compliance—certain types of information needs
|
|
to reside in certain locations due to regular issues—and
|
|
more important cannot reside in other locations
|
|
for the same reason.</para>
|
|
</listitem>
|
|
</itemizedlist>
|
|
<para>
|
|
Examples of such legal frameworks include the <link
|
|
xlink:href="http://ec.europa.eu/justice/data-protection/">data
|
|
protection framework</link> of the European Union and the
|
|
requirements of the <link
|
|
xlink:href="http://www.finra.org/Industry/Regulation/FINRARules/">Financial
|
|
Industry Regulatory Authority</link> in the United
|
|
States. Consult a local regulatory body for more
|
|
information.</para></section>
|
|
<section xml:id="technical-considerations-compute-focus-user">
|
|
<title>Technical considerations</title>
|
|
<para>The following are some technical requirements that need to
|
|
be incorporated into the architecture design.
|
|
</para>
|
|
<variablelist>
|
|
<varlistentry>
|
|
<term>Performance</term>
|
|
<listitem>
|
|
<para>If a primary technical concern is for
|
|
the environment to deliver high performance
|
|
capability, then a compute-focused design is an
|
|
obvious choice because it is specifically designed to
|
|
host compute-intensive workloads.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>Workload persistence</term>
|
|
<listitem>
|
|
<para>Workloads can be either
|
|
short-lived or long running. Short-lived workloads
|
|
might include continuous integration and continuous
|
|
deployment (CI-CD) jobs, where large numbers of
|
|
compute instances are created simultaneously to
|
|
perform a set of compute-intensive tasks. The results
|
|
or artifacts are then copied from the instance into
|
|
long-term storage before the instance is destroyed.
|
|
Long-running workloads, like a Hadoop or
|
|
high-performance computing (HPC) cluster, typically
|
|
ingest large data sets, perform the computational work
|
|
on those data sets, then push the results into long
|
|
term storage. Unlike short-lived workloads, when the
|
|
computational work is completed, they will remain idle
|
|
until the next job is pushed to them. Long-running
|
|
workloads are often larger and more complex, so the
|
|
effort of building them is mitigated by keeping them
|
|
active between jobs. Another example of long running
|
|
workloads is legacy applications that typically are
|
|
persistent over time.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>Storage</term>
|
|
<listitem>
|
|
<para>Workloads targeted for a compute-focused
|
|
OpenStack cloud generally do not require any
|
|
persistent block storage (although some usages of
|
|
Hadoop with HDFS may dictate the use of persistent
|
|
block storage). A shared filesystem or object store
|
|
will maintain the initial data set(s) and serve as the
|
|
destination for saving the computational results. By
|
|
avoiding the input-output (IO) overhead, workload
|
|
performance is significantly enhanced. Depending on
|
|
the size of the data set(s), it might be necessary to
|
|
scale the object store or shared file system to match
|
|
the storage demand.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>User interface</term>
|
|
<listitem>
|
|
<para>Like any other cloud architecture, a
|
|
compute-focused OpenStack cloud requires an on-demand
|
|
and self-service user interface. End users must be
|
|
able to provision computing power, storage, networks
|
|
and software simply and flexibly. This includes
|
|
scaling the infrastructure up to a substantial level
|
|
without disrupting host operations.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
<varlistentry>
|
|
<term>Security</term>
|
|
<listitem>
|
|
<para>Security is going to be highly dependent
|
|
on the business requirements. For example, a
|
|
computationally intense drug discovery application
|
|
will obviously have much higher security requirements
|
|
than a cloud that is designed for processing market
|
|
data for a retailer. As a general start, the security
|
|
recommendations and guidelines provided in the
|
|
OpenStack Security Guide are applicable.</para>
|
|
</listitem>
|
|
</varlistentry>
|
|
</variablelist>
|
|
</section>
|
|
<section xml:id="operational-considerations-compute-focus-user">
|
|
<title>Operational considerations</title>
|
|
<para>The compute intensive cloud from the operational perspective
|
|
is similar to the requirements for the general-purpose cloud.
|
|
More details on operational requirements can be found in the
|
|
general-purpose design section.</para>
|
|
</section>
|
|
</section>
|