187 lines
10 KiB
XML
187 lines
10 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
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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="arch-guide-architecture-hybrid">
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<?dbhtml stop-chunking?>
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<title>Architecture</title>
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<para>Once business and application requirements have been
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defined, the first step for designing a hybrid cloud solution
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is to map out the dependencies between the expected workloads
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and the diverse cloud infrastructures that need to support
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them. By mapping the applications and the targeted cloud
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environments, you can architect a solution that enables the
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broadest compatibility between cloud platforms and minimizes
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the need to create workarounds and processes to fill
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identified gaps. Note the evaluation of the monitoring and
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orchestration APIs available on each cloud platform and the
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relative levels of support for them in the chosen Cloud
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Management Platform.</para>
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<mediaobject>
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<imageobject>
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<imagedata
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fileref="../images/Multi-Cloud_Priv-AWS4.png"
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/>
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</imageobject>
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</mediaobject>
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<section xml:id="image-portability"><title>Image portability</title>
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<para>The majority of cloud workloads currently run on instances
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using hypervisor technologies such as KVM, Xen, or ESXi. The
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challenge is that each of these hypervisors use an image
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format that is mostly, or not at all, compatible with one
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another. In a private or hybrid cloud solution, this can be
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mitigated by standardizing on the same hypervisor and instance
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image format but this is not always feasible. This is
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particularly evident if one of the clouds in the architecture
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is a public cloud that is outside of the control of the
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designers.</para>
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<para>There are conversion tools such as virt-v2v
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(http://libguestfs.org/virt-v2v/) and virt-edit
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(http://libguestfs.org/virt-edit.1.html) that can be used in
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those scenarios but they are often not suitable beyond very
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basic cloud instance specifications. An alternative is to
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build a thin operating system image as the base for new
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instances. This facilitates rapid creation of cloud instances
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using cloud orchestration or configuration management tools,
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driven by the CMP, for more specific templating. Another more
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expensive option is to use a commercial image migration tool.
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The issue of image portability is not just for a one time
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migration. If the intention is to use the multiple cloud for
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disaster recovery, application diversity or high availability,
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the images and instances are likely to be moved between the
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different cloud platforms regularly.</para></section>
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<section xml:id="upper-layer-services"><title>Upper-Layer Services</title>
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<para>Many clouds offer complementary services over and above the
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basic compute, network, and storage components. These
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additional services are often used to simplify the deployment
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and management of applications on a cloud platform.</para>
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<para>Consideration is required to be given to moving workloads
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that may have upper-layer service dependencies on the source
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cloud platform to a destination cloud platform that may not
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have a comparable service. Conversely, the user can implement
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it in a different way or by using a different technology. For
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example, moving an application that uses a NoSQL database
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service such as MongoDB that is delivered as a service on the
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source cloud, to a destination cloud that does not offer that
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service or may only use a relational database such as MySQL,
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could cause difficulties in maintaining the application
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between the platforms.</para>
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<para>There are a number of options that might be appropriate for
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the hybrid cloud use case:</para>
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<itemizedlist>
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<listitem>
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<para>Create a baseline of upper-layer services that are
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implemented across all of the cloud platforms. For
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platforms that do not support a given service, create
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a service on top of that platform and apply it to the
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workloads as they are launched on that cloud. For
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example, OpenStack, via Trove, supports MySQL as a
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service but not NoSQL databases in production. To move
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from or to run alongside on AWS a NoSQL workload would
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require recreating the NoSQL database on top of
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OpenStack and automate the process of implementing it
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using a tool such as OpenStack Orchestration
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(Heat).</para>
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</listitem>
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<listitem>
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<para>Deploy a Platform as a Service (PaaS) technology
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such as Cloud Foundry or OpenShift that abstracts the
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upper-layer services from the underlying cloud
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platform. The unit of application deployment and
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migration is the PaaS and leverages the services of
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the PaaS and only consumes the base infrastructure
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services of the cloud platform. The downside to this
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approach is that the PaaS itself then potentially
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becomes a source of lock-in.</para>
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</listitem>
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<listitem>
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<para>Use only the base infrastructure services that are
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common across all cloud platforms. Use automation
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tools to create the required upper-layer services
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which are portable across all cloud platforms. For
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example, instead of using any database services that
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are inherent in the cloud platforms, launch cloud
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instances and deploy the databases on to those
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instances using scripts or various configuration and
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application deployment tools.</para>
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</listitem>
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</itemizedlist></section>
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<section xml:id="network-services"><title>Network Services</title>
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<para>Network services functionality is a significant barrier for
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multiple cloud architectures. It could be an important factor
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to assess when choosing a CMP and cloud provider.
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Considerations are: functionality, security, scalability and
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High availability (HA). Verification and ongoing testing of
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the critical features of the cloud endpoint used by the
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architecture are important tasks.</para>
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<itemizedlist>
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<listitem>
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<para>Once the network functionality framework has been
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decided, a minimum functionality test should be
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designed to confirm that the functionality is in fact
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compatible. This will ensure testing and functionality
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persists during and after upgrades. Note that over
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time, the diverse cloud platforms are likely to
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de-synchronize if care is not taken to maintain
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compatibility. This is a particular issue with
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APIs.</para>
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</listitem>
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<listitem>
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<para>Scalability across multiple cloud providers may
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dictate which underlying network framework is chosen
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for the different cloud providers. It is important to
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have the network API functions presented and to verify
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that the desired functionality persists across all
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chosen cloud endpoint.</para>
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</listitem>
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<listitem>
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<para>High availability (HA) implementations vary in
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functionality and design. Examples of some common
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methods are Active-Hot-Standby, Active-Passive and
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Active-Active. High availability and a test framework
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need to be developed to insure that the functionality
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and limitations are well understood.</para>
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</listitem>
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<listitem>
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<para>Security considerations, such as how data is secured
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between client and endpoint and any traffic that
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traverses the multiple clouds, from eavesdropping to
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DoS activities must be addressed. Business and
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regulatory requirements dictate the security approach
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that needs to be taken.</para>
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</listitem>
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</itemizedlist></section>
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<section xml:id="data"><title>Data</title>
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<para>Replication has been the traditional method for protecting
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object store implementations. A variety of different
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implementations have existed in storage architectures.
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Examples of this are both synchronous and asynchronous
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mirroring. Most object stores and back-end storage systems have
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a method for replication that can be implemented at the
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storage subsystem layer. Object stores also have implemented
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replication techniques that can be tailored to fit a clouds
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needs. An organization must find the right balance between
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data integrity and data availability. Replication strategy may
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also influence the disaster recovery methods
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implemented.</para>
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<para>Replication across different racks, data centers and
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geographical regions has led to the increased focus of
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determining and ensuring data locality. The ability to
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guarantee data is accessed from the nearest or fastest storage
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can be necessary for applications to perform well. Examples of
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this are Hadoop running in a cloud. The user either runs with
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a native HDFS, when applicable, or on a separate parallel file
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system such as those provided by Hitachi and IBM. Special
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consideration should be taken when running embedded object
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store methods to not cause extra data replication, which can
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create unnecessary performance issues. Another example of
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ensuring data locality is by using Ceph. Ceph has a data
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container abstraction called a pool. Pools can be created with
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replicas or erasure code. Replica based pools can also have a
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rule set defined to have data written to a “local” set of
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hardware which would be the primary access and modification
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point.</para>
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</section>
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</section>
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