Merge "Removal of passive voice from chap 4, arch guide"
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<title>Prescriptive examples</title>
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<para>Storage-focused architectures are highly dependent on the
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specific use case. Three specific example use cases are
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discussed in this section: an object store with a RESTful
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interface, compute analytics with parallel file systems, and a
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high performance database.</para>
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<para>This example describes a REST interface without a high
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performance requirement, so the presented REST interface does
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not require a high performance caching tier, and is presented
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as a traditional Object store running on traditional
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spindles.</para>
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discussed in this section:</para>
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<itemizedlist>
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<listitem>
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<para>
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An object store with a RESTful interface
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</para>
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</listitem>
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<listitem>
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<para>
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Compute analytics with parallel file systems
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</para>
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</listitem>
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<listitem>
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<para>
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High performance database
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</para>
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</listitem>
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</itemizedlist>
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<para>The example below shows a REST interface without a high
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performance requirement.</para>
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<para>Swift is a highly scalable object store that is part of the
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OpenStack project. This is a diagram to explain the example
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OpenStack project. This diagram explains the example
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architecture:
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<mediaobject>
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<imageobject>
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<imagedata contentwidth="4in"
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fileref="../figures/Storage_Object.png"
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/>
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</imageobject>
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</mediaobject></para>
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<mediaobject>
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<imageobject>
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<imagedata contentwidth="4in"
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fileref="../figures/Storage_Object.png"/>
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</imageobject>
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</mediaobject>
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</para>
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<para>The presented REST interface does not require a high performance
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caching tier, and is presented as a traditional Object store running
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on traditional spindles.</para>
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<para>This example uses the following components:</para>
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<para>Network:</para>
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<itemizedlist>
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@ -37,8 +52,8 @@
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<para>Storage hardware:</para>
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<itemizedlist>
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<listitem>
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<para>10 storage servers each with 12x4 TB disks which
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equals 480 TB total space with approximately 160 Tb of
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<para>10 storage servers each with 12x4 TB disks equalling
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480 TB total space with approximately 160 Tb of
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usable space after replicas.</para>
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</listitem>
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</itemizedlist>
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@ -58,77 +73,80 @@
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back end storage cluster</para>
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</listitem>
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</itemizedlist>
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<note><para>For some applications, it may be necessary to
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implement a 3rd-party caching layer to achieve suitable
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performance.</para></note>
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<section xml:id="compute-analytics-with-sahara">
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<title>Compute analytics with Data processing service for
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OpenStack</title>
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<para>Analytics of large data sets can be highly dependent on the
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performance of the storage system. Some clouds using storage
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systems such as HDFS have inefficiencies which can cause
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performance issues. A potential solution to this is to
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implement a storage system designed with performance in mind.
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Traditionally, parallel file systems have filled this need in
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the HPC space and could be a consideration, when applicable,
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for large scale performance-oriented systems.</para>
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<para>This example discusses an OpenStack Object Store with a high
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performance requirement. OpenStack has integration with Hadoop
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through the Data processing project (Sahara), which is leveraged
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to manage the Hadoop cluster within the cloud.
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<note>
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<para>It may be necessary to implement a 3rd-party caching layer
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for some applications to achieve suitable performance.</para>
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</note>
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<section xml:id="compute-analytics-with-sahara">
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<title>Compute analytics with Data processing service</title>
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<para>Analytics of large data sets are highly dependent on the performance
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of the storage system. Clouds using storage systems such as
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Hadoop Distributed File System (HDFS) have inefficiencies which can
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cause performance issues.
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</para>
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<para>One potential solution to this problem is the implentation of storage
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systems designed for performance. Parallel file systems have previously
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filled this need in the HPC space and as a result could be considered
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for large scale performance-orientated systems.</para>
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<para>OpenStack has integration with Hadoop to manage the Hadoop cluster
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within the cloud. This diagram shows an OpenStack store with a high
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performance requirement:
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<mediaobject>
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<imageobject>
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<imagedata contentwidth="4in"
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fileref="../figures/Storage_Hadoop3.png"
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/>
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fileref="../figures/Storage_Hadoop3.png"/>
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</imageobject>
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</mediaobject></para>
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<para>The actual hardware requirements and configuration are
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similar to those of the High Performance Database example
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below. In this case, the architecture uses Ceph's
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Swift-compatible REST interface, features that allow for
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connecting a caching pool to allow for acceleration of the
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presented pool.</para></section>
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<section xml:id="high-performance-database-with-trove">
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<title>High performance database with Database service for OpenStack</title>
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<para>Databases are a common workload that can greatly benefit
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from a high performance storage back end. Although enterprise
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storage is not a requirement, many environments have existing
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storage that can be used as back ends for an OpenStack cloud.
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As shown in the following diagram, a storage pool can be
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carved up to provide block devices with OpenStack Block
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Storage to instances as well as an object interface. In this
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example the database I-O requirements were high and demanded
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storage presented from a fast SSD pool.</para>
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<para>A storage system is used to present a LUN that is backed by
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a set of SSDs using a traditional storage array with OpenStack
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Block Storage integration or a storage platform such as Ceph
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or Gluster.</para>
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<para>This kind of system can also provide additional performance
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in other situations. For example, in the database example
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below, a portion of the SSD pool can act as a block device to
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the Database server. In the high performance analytics
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example, the REST interface would be accelerated by the inline
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SSD cache layer.</para>
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</mediaobject>
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</para>
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<para>The hardware requirements and configuration are
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similar to those of the High Performance Database example
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below. In this case, the architecture uses Ceph's
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Swift-compatible REST interface, features that allow for
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connecting a caching pool to allow for acceleration of the
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presented pool.
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</para>
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</section>
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<section xml:id="high-performance-database-with-trove">
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<title>High performance database with Database service</title>
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<para>Databases are a common workload that benefit from high performance
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storage back ends. Although enterprise storage is not a requirement,
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many environments have existing storage that can be used as back ends for
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OpenStack cloud. A storage pool can be created to provide block devices
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with OpenStack Block Storage for instances as well as object interfaces.
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In this example, the database I-O requirements were high and demanded
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storage presented from a fast SSD pool.</para>
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<para>A storage system is used to present a LUN that is backed by
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a set of SSDs using a traditional storage array with OpenStack
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Block Storage integration or a storage platform such as Ceph
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or Gluster.</para>
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<para>This system can provide additional performance. For example,
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in the database example below, a portion of the SSD pool can act
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as a block device to the Database server. In the high performance analytics
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example, the REST interface would be accelerated by the inline
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SSD cache layer.</para>
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<mediaobject>
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<imageobject>
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<imagedata contentwidth="4in"
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fileref="../figures/Storage_Database_+_Object5.png"
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/>
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fileref="../figures/Storage_Database_+_Object5.png"/>
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</imageobject>
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</mediaobject>
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<para>Ceph was selected to present a Swift-compatible REST
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interface, as well as a block level storage from a distributed
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storage cluster. It is highly flexible and has features that
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allow to reduce cost of operations such as self healing and
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auto balancing. Erasure coded pools are used to maximize the
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amount of usable space. Note that there are special
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considerations around erasure coded pools, for example, higher
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computational requirements and limitations on the operations
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allowed on an object. For example, partial writes are not
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supported in an erasure coded pool.</para>
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<para>A potential architecture for Ceph, as it relates to the
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examples above, would entail the following:</para>
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auto balancing. Using erasure coded pools are a suitable way of
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maximizing the amount of usable space.</para>
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<note>
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<para>There are special considerations around erasure coded pools.
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For example, higher computational requirements and limitations on
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the operations allowed on an object; partial writes are not
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supported in an erasure coded pool.
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</para>
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</note>
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<para>Using Ceph as an applicable example, a potential architecture
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would have the following requirements:</para>
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<para>Network:</para>
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<itemizedlist>
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<listitem>
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@ -164,8 +182,9 @@
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back end storage cluster</para>
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</listitem>
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</itemizedlist>
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<para>The SSD cache layer is used to present block devices
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<para>Using an SSD cache layer, you can present block devices
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directly to Hypervisors or instances. The SSD cache systems
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can also be used as an inline cache for the REST interface.
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</para></section>
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</para>
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</section>
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</section>
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