Computer cluster: Difference between revisions

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{{Short description|Set of computers configured in a distributed computing system}}

{{Distinguish|data cluster|grid computing}}

{{Redirect|Cluster computing|the journal|Cluster Computing (journal)}}

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[[File:Taiwania series.jpg|thumb|[[Taiwania_(supercomputer)|Taiwania]] series uses cluster architecture.]]

A '''computer cluster''' is a set of [[computerscomputer]]s that work together so that they can be viewed as a single system. Unlike [[Grid computing|grid computer]]s, computer clusters have each [[Node (networking)|node]] set to perform the same task, controlled and scheduled by software. The newest manifestation of cluster computing is [[cloud computing]].

The components of a cluster are usually connected to each other through fast [[local area network]]s, with each [[Node (networking)|node]] (computer used as a server) running its own instance of an [[operating system]]. In most circumstances, all of the nodes use the same hardware<ref>{{cite web |url=https://stackoverflow.com/questions/9723040/what-is-the-difference-between-cloud-grid-and-cluster |title=Cluster vs grid computing |website=[[Stack Overflow]]}}</ref>{{better source needed|date=June 2017}} and the same operating system, although in some setups (e.g. using [[Open Source Cluster Application Resources]] (OSCAR)), different operating systems can be used on each computer, or different hardware.<ref name=pcauthority>{{cite web|url=http://www.pcauthority.com.au/Feature/306972,weekend-project-build-your-own-supercomputer.aspx|title=Weekend Project: Build your own supercomputer|date=29 June 2012|first=Darien|last=Graham-Smith|website=PC & Tech Authority|access-date=2 June 2017}}</ref>

Clusters are usually deployed to improve performance and availability over that of a single computer, while typically being much more cost-effective than single computers of comparable speed or availability.<ref>{{cite web|url=http://www.cc.gatech.edu/~bader/papers/ijhpca.html|title=Cluster Computing: Applications|last1=Bader|first1=David|author-link=David Bader (computer scientist)|date=May 2001|publisher=[[Georgia Institute of Technology College of Computing|Georgia Tech College of Computing]]|first2=Robert|last2=Pennington|access-date=2017-02-28|archive-url=https://web.archive.org/web/20071221011621/http://www.cc.gatech.edu/~bader/papers/ijhpca.html|archive-date=2007-12-21|url-status=dead}}</ref>

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==Benefits==

<!-- This used to be a list. Work has been done since, but it's still incomplete. -->

Clusters are primarily designed with performance in mind, but installations are based on many other factors. Fault tolerance (''the ability forof a system to continue workingoperating withdespite a malfunctioning node'') allows forenables [[horizontal scaling|scalability]], and in high-performance situations, allows for a low frequency of maintenance routines, resource consolidation (e.g., [[RAID]]), and centralized management. Advantages include enabling data recovery in the event of a disaster and providing parallel data processing and high processing capacity.<ref>{{cite web|url=http://www-03.ibm.com/systems/clusters/benefits.html|title=IBM Cluster System : Benefits|publisher=[[IBM]]|access-date=8 September 2014|archive-url=https://web.archive.org/web/20160429022854/http://www-03.ibm.com/systems/clusters/benefits.html|archive-date=29 April 2016|url-status=dead}}</ref><ref>{{cite web|url=https://technet.microsoft.com/en-us/library/cc778629(v=ws.10).aspx|title=Evaluating the Benefits of Clustering|date=28 March 2003|publisher=[[Microsoft]]|access-date=8 September 2014|archive-url=https://web.archive.org/web/20160422092651/https://technet.microsoft.com/en-us/library/cc778629%28v%3Dws.10%29.aspx|archive-date=22 April 2016|url-status=dead}}</ref>

In terms of scalability, clusters provide this in their ability to add nodes horizontally. This means that more computers may be added to the cluster, to improve its performance, redundancy and fault tolerance. This can be an inexpensive solution for a higher performing cluster compared to scaling up a single node in the cluster. This property of computer clusters can allow for larger computational loads to be executed by a larger number of lower performing computers.

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==Design and configuration==

[[File:beowulf.png|thumb|240px|left|A typical Beowulf configuration]]

One of the issues in designing a cluster is how tightly coupled the individual nodes may be. For instance, a single computer job may require frequent communication among nodes: this implies that the cluster shares a dedicated network, is densely located, and probably has homogeneous nodes. The other extreme is where a computer job uses one or few nodes, and needs little or no inter-node communication, approaching [[grid computing]].

In a [[Beowulf cluster]], the application programs never see the computational nodes (also called slave computers) but only interact with the "Master" which is a specific computer handling the scheduling and management of the slaves.<ref name=VECPAR /> In a typical implementation the Master has two network interfaces, one that communicates with the private Beowulf network for the slaves, the other for the general purpose network of the organization.<ref name=VECPAR /> The slave computers typically have their own version of the same operating system, and local memory and disk space. However, the private slave network may also have a large and shared file server that stores global persistent data, accessed by the slaves as needed.<ref name=VECPAR />

A special purpose 144-node [[DEGIMA (computer cluster)|DEGIMA cluster]] is tuned to running astrophysical N-body simulations using the Multiple-Walk parallel tree code, rather than general purpose scientific computations.<ref name=Hamada>{{cite journal|first=Tsuyoshi|last=Hamada |display-authors=etal |year=2009|title=A novel multiple-walk parallel algorithm for the Barnes–Hut treecode on GPUs – towards cost effective, high performance N-body simulation|journal=Computer Science – Research and Development|volume=24|issue=1–2 |pages=21–31 |doi=10.1007/s00450-009-0089-1|s2cid=31071570 }}</ref>

Due to the increasing computing power of each generation of [[game console]]s, a novel use has emerged where they are repurposed into [[High-performance computing]] (HPC) clusters. workstation, which uses multiple graphics accelerator processor chips. Besides game consoles, high-end graphics cards too can be used instead. The use of graphics cards (or rather their GPU's) to do calculations for grid computing is vastly more economical than using CPU's, despite being less precise. However, when using double-precision values, they become as precise to work with as CPU's and are still much less costly (purchase cost).<ref name="pcauthority" />

Due to the increasing computing power of each generation of [[game console]]s, a novel use has emerged where they are repurposed into [[High-performance computing]] (HPC) clusters. Some examples of game console clusters are [[PlayStation 3 cluster|Sony PlayStation clusters]] and [[Microsoft]] [[Xbox (console)|Xbox]] clusters. Another example of consumer game product is the [[Nvidia Tesla Personal Supercomputer]] workstation, which uses multiple graphics accelerator processor chips. Besides game consoles, high-end graphics cards too can be used instead. The use of graphics cards (or rather their GPU's) to do calculations for grid computing is vastly more economical than using CPU's, despite being less precise. However, when using double-precision values, they become as precise to work with as CPU's and are still much less costly (purchase cost).<ref name="pcauthority" />

Computer clusters haWithhave historically run on separate physical [[computer]]s with the same [[operating system]]. With the advent of [[virtualization]], the cluster nodes may run on separate physical computers with different operating systems which are painted above with a virtual layer to look similar.<ref name=linuxjournal>{{cite web|url=http://www.linuxjournal.com/article/8812|title=Xen Virtualization and Linux Clustering, Part 1|date=12 Jan 2006|website=Linux Journal|first=Ryan|last=Mauer|access-date=2 Jun 2017}}</ref>{{citation needed|date=November 2013}}{{clarify|date=November 2013}} The cluster may also be virtualized on various configurations as maintenance takes place; an example implementation is [[Xen]] as the virtualization manager with [[Linux-HA]].<ref name="linuxjournal" />

==Data sharing and communication==

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:* [[Solaris Cluster]]

:* [[Veritas Cluster Server]]

:* [[Beowulf cluster]]

''Computer farms''

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{{Commons category|Clusters (computing)}}

* [https://web.archive.org/web/20190219183441/https://www.ieeetcsc.org/ IEEE Technical Committee on Scalable Computing (TCSC)]

* [https://archive.today/20130103192843/http://publib.boulder.ibm.com/infocenter/clresctr/vxrx/index.jsp?topic=%2Fcom/com.ibm.cluster.rsct.doc%2Frsctbooks/rsctbooks.html Reliable Scalable Cluster Technology, IBM]{{Dead link|date=July 2020 |bot=InternetArchiveBot |fix-attempted=yes }}

* [https://www.ibm.com/developerworks/wikis/display/tivoli/Tivoli+System+Automation Tivoli System Automation Wiki]

* [https://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/43438.pdf Large-scale cluster management at Google with Borg], April 2015, by Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune and John Wilkes