Computer cluster: Difference between revisions

Revision as of 20:38, 12 January 2022

Revision as of 14:20, 29 January 2022

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The desire to get more computing power and better reliability by orchestrating a number of low-cost [[commercial off-the-shelf]] computers has given rise to a variety of architectures and configurations.

The computer clustering approach usually (but not always) connects a number of readily available computing nodes (e.g. personal computers used as servers) via a fast [[local area network]].<ref name=nbis>{{cite conference|title=Network-Based Information Systems: First International Conference, NBIS 2007|ISBNisbn=978-3-540-74572-63|page=375|last1=Enokido|first1=Tomoya|last2=Barolli|first2=Leonhard|last3=Takizawa|first3=Makoto|date=23 August 2007}}</ref> The activities of the computing nodes are orchestrated by "clustering middleware", a software layer that sits atop the nodes and allows the users to treat the cluster as by and large one cohesive computing unit, e.g. via a [[single system image]] concept.<ref name=nbis />

Computer clustering relies on a centralized management approach which makes the nodes available as orchestrated shared servers. It is distinct from other approaches such as [[peer to peer]] or [[grid computing]] which also use many nodes, but with a far more [[distributed computing|distributed nature]].<ref name=nbis />

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===Task scheduling===

When a large multi-user cluster needs to access very large amounts of data, [[task scheduling]] becomes a challenge. In a heterogeneous CPU-GPU cluster with a complex application environment, the performance of each job depends on the characteristics of the underlying cluster. Therefore, mapping tasks onto CPU cores and GPU devices provides significant challenges.<ref name=Shira /> This is an area of ongoing research; algorithms that combine and extend [[MapReduce]] and [[Apache Hadoop|Hadoop]] have been proposed and studied.<ref name=Shira >{{cite conference|author=K. Shirahata |display-authors=etal |title=Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters|conference=Cloud Computing Technology and Science (CloudCom) |date=30 Nov – 3 Dec 2010|pages=733–740|ISBNisbn=978-1-4244-9405-7|doi=10.1109/CloudCom.2010.55}}</ref>

===Node failure management===

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== Further reading ==

* {{cite arxivarXiv|first=Mark|last=Baker |display-authors=etal |title=Cluster Computing White Paper|eprint=cs/0004014|date=11 Jan 2001}}

* {{cite book|first1=Evan|last1=Marcus|first2=Hal|last2=Stern|title=Blueprints for High Availability: Designing Resilient Distributed Systems|publisher=John Wiley & Sons|isbn=978-0-471-35601-1|date=2000-02-14|url=https://archive.org/details/blueprintsforhig00marc}}

* {{cite book|first1=Greg|last1=Pfister|title=In Search of Clusters|publisher=Prentice Hall|isbn=978-0-13-899709-0|year=1998|url=https://archive.org/details/insearchofcluste00pfis}}