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A game-theoretic method of fair resource allocation for cloud computing services

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dc.contributor.author Wei, GY en
dc.contributor.author Vasilakos, AV en
dc.contributor.author Zheng, Y en
dc.contributor.author Xiong, NX en
dc.date.accessioned 2014-03-01T01:32:26Z
dc.date.available 2014-03-01T01:32:26Z
dc.date.issued 2010 en
dc.identifier.issn 0920-8542 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20134
dc.subject Cloud computing en
dc.subject Game theory en
dc.subject Resource allocation en
dc.subject.classification Computer Science, Hardware & Architecture en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other PARALLEL en
dc.subject.other PREDICTION en
dc.title A game-theoretic method of fair resource allocation for cloud computing services en
heal.type journalArticle en
heal.identifier.primary 10.1007/s11227-009-0318-1 en
heal.identifier.secondary http://dx.doi.org/10.1007/s11227-009-0318-1 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions. en
heal.publisher SPRINGER en
heal.journalName JOURNAL OF SUPERCOMPUTING en
dc.identifier.doi 10.1007/s11227-009-0318-1 en
dc.identifier.isi ISI:000282225700007 en
dc.identifier.volume 54 en
dc.identifier.issue 2 en
dc.identifier.spage 252 en
dc.identifier.epage 269 en


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