176 lines
8.0 KiB
XML
176 lines
8.0 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE section [
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<!ENTITY % openstack SYSTEM "../../common/entities/openstack.ent">
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%openstack;
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]>
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<section xmlns="http://docbook.org/ns/docbook"
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xmlns:xi="http://www.w3.org/2001/XInclude"
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xmlns:xlink="http://www.w3.org/1999/xlink"
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version="5.0"
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xml:id="user-requirements-compute-focus">
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<?dbhtml stop-chunking?>
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<title>User requirements</title>
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<para>High utilization of CPU, RAM, or both defines compute
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intensive workloads. User requirements determine the performance
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demands for the cloud.
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</para>
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<variablelist>
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<varlistentry>
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<term>Cost</term>
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<listitem>
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<para>Cost is not generally a primary concern for a
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compute-focused cloud, however some organizations
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might be concerned with cost avoidance. Repurposing
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existing resources to tackle compute-intensive tasks
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instead of acquiring additional resources may
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offer cost reduction opportunities.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Time to market</term>
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<listitem>
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<para>Compute-focused clouds can deliver products more quickly,
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for example by speeding up a company's software development
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life cycle (SDLC) for building products and applications.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Revenue opportunity</term>
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<listitem>
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<para>Companies that want to build services or products that
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rely on the power of compute resources benefit from a
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compute-focused cloud. Examples include the analysis
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of large data sets (via Hadoop or Cassandra) or
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completing computational intensive tasks such as
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rendering, scientific computation, or
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simulations.</para>
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</listitem>
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</varlistentry>
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</variablelist>
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<section xml:id="legal-requirements-compute-focus">
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<title>Legal requirements</title>
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<para>Many jurisdictions have legislative and regulatory
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requirements governing the storage and management of data in
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cloud environments. Common areas of regulation include:</para>
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<itemizedlist>
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<listitem>
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<para>Data retention policies ensuring storage of
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persistent data and records management to meet data
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archival requirements.</para>
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</listitem>
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<listitem>
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<para>Data ownership policies governing the possession and
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responsibility for data.</para>
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</listitem>
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<listitem>
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<para>Data sovereignty policies governing the storage of
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data in foreign countries or otherwise separate
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jurisdictions.</para>
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</listitem>
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<listitem>
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<para>Data compliance: certain types of information need
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to reside in certain locations due to regular issues and,
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more importantly, cannot reside in other locations
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for the same reason.</para>
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</listitem>
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</itemizedlist>
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<para>
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Examples of such legal frameworks include the <link
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xlink:href="http://ec.europa.eu/justice/data-protection/">data
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protection framework</link> of the European Union and the
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requirements of the <link
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xlink:href="http://www.finra.org/Industry/Regulation/FINRARules/">Financial
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Industry Regulatory Authority</link> in the United
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States. Consult a local regulatory body for more
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information.</para></section>
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<section xml:id="technical-considerations-compute-focus-user">
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<title>Technical considerations</title>
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<para>The following are some technical requirements you must consider
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in the architecture design:
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</para>
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<variablelist>
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<varlistentry>
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<term>Performance</term>
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<listitem>
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<para>If a primary technical concern is to deliver high performance
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capability, then a compute-focused design is an
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obvious choice because it is specifically designed to
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host compute-intensive workloads.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Workload persistence</term>
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<listitem>
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<para>Workloads can be either
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short-lived or long-running. Short-lived workloads
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can include continuous integration and continuous
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deployment (CI-CD) jobs, which create large numbers of
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compute instances simultaneously to
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perform a set of compute-intensive tasks. The environment then
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copies the results or artifacts from each instance into
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long-term storage before destroying the instance.
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Long-running workloads, like a Hadoop or
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high-performance computing (HPC) cluster, typically
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ingest large data sets, perform the computational work
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on those data sets, then push the results into long-term
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storage. When the computational work finishes, the instances
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remain idle until they receive another job. Environments
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for long-running workloads are often larger and more complex,
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but you can offset the cost of building them by keeping them
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active between jobs. Another example of long-running
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workloads is legacy applications that are
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persistent over time.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Storage</term>
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<listitem>
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<para>Workloads targeted for a compute-focused
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OpenStack cloud generally do not require any
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persistent block storage, although some uses of
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Hadoop with HDFS may require persistent
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block storage. A shared filesystem or object store
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maintains the initial data sets and serves as the
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destination for saving the computational results. By
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avoiding the input-output (IO) overhead, you can significantly
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enhance workload performance. Depending on
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the size of the data sets, it may be necessary to
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scale the object store or shared file system to match
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the storage demand.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>User interface</term>
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<listitem>
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<para>Like any other cloud architecture, a
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compute-focused OpenStack cloud requires an on-demand
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and self-service user interface. End users must be
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able to provision computing power, storage, networks,
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and software simply and flexibly. This includes
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scaling the infrastructure up to a substantial level
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without disrupting host operations.</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Security</term>
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<listitem>
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<para>Security is highly dependent
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on business requirements. For example, a
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computationally intense drug discovery application
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has much higher security requirements
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than a cloud for processing market
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data for a retailer. As a general rule, the security
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recommendations and guidelines provided in the
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OpenStack Security Guide are applicable.</para>
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</listitem>
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</varlistentry>
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</variablelist>
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</section>
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<section xml:id="operational-considerations-compute-focus-user">
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<title>Operational considerations</title>
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<para>From an operational perspective, a compute intensive cloud
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is similar to a general-purpose cloud. See the general-purpose
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design section for more details on operational requirements.</para>
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</section>
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</section>
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