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Autonomous Resource Allocation in Clouds: A Comprehensive Analysis of Single Synthesizing Criterion and Outranking Based Multiple Criteria Decision Analysis Methods

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dc.contributor.author Akbulut, Yagmur
dc.date.accessioned 2014-08-20T22:41:52Z
dc.date.available 2014-08-20T22:41:52Z
dc.date.copyright 2014 en_US
dc.date.issued 2014-08-20
dc.identifier.uri http://hdl.handle.net/1828/5579
dc.description.abstract Cloud computing is an emerging trend where clients are billed for services on a pay-per-use basis. Service level agreements define the formal negotiations between the clients and the service providers on common metrics such as processing power, memory and bandwidth. In the case of service level agreement violations, the service provider is penalised. From service provider's point of view, providing cloud services efficiently within the negotiated metrics is an important problem. Particularly, in large-scale data center settings, manual administration for resource allocation is not a feasible option. Service providers aim to maximize resource utilization in the data center, as well as, avoiding service level agreement violations. On the other hand, from the client's point of view, the cloud must continuously ensure enough resources to the changing workloads of hosted application environments and services. Therefore, an autonomous cloud manager that is capable of dynamically allocating resources in order to satisfy both the client and the service provider's requirements emerges as a necessity. In this thesis, we focus on the autonomous resource allocation in cloud computing environments. A distributed resource consolidation manager for clouds, called IMPROMPTU, was introduced in our previous studies. IMPROMPTU adopts a threshold based reactive design where each unique physical machine is coupled with an autonomous node agent that manages resource consolidation independently from the rest of the autonomous node agents. In our previous studies, IMPROMPTU demonstrated the viability of Multiple Criteria Decision Analysis (MCDA) to provide resource consolidation management that simultaneously achieves lower numbers of reconfiguration events and service level agreement violations under the management of three well-known outranking-based methods called PROMETHEE II, ELECTRE III and PAMSSEM II. The interesting question of whether more efficient single synthesizing criterion and outranking based MCDA methods exist was left open for research. This thesis addresses these limitations by analysing the capabilities of IMPROMPTU using a comprehensive set of single synthesizing criterion and outranking based MCDA methods in the context of dynamic resource allocation. The performances of PROMETHEE II, ELECTRE III, PAMSSEM II, REGIME, ORESTE, QUALIFEX, AHP and SMART are investigated by in-depth analysis of simulation results. Most importantly, the question of what denotes the properties of good MCDA methods for this problem domain is answered. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/2.5/ca/ *
dc.subject Dynamic Resource Allocation en_US
dc.subject Cloud Computing en_US
dc.subject MCDA en_US
dc.subject Outranking Methods en_US
dc.subject Single Synthesizing Methods en_US
dc.subject AHP en_US
dc.subject Qualiflex en_US
dc.subject Promethee en_US
dc.subject Electre en_US
dc.subject Regime en_US
dc.subject MAUT en_US
dc.subject Smart en_US
dc.subject Oreste en_US
dc.subject multiple-criteria decision analysis en_US
dc.title Autonomous Resource Allocation in Clouds: A Comprehensive Analysis of Single Synthesizing Criterion and Outranking Based Multiple Criteria Decision Analysis Methods en_US
dc.type Thesis en_US
dc.contributor.supervisor Ganti, Sudhakar
dc.contributor.supervisor Coady, Yvonne
dc.degree.department Department of Computer Science en_US
dc.degree.level Master of Science M.Sc. en_US
dc.rights.temp Available to the World Wide Web en_US
dc.identifier.bibliographicCitation Yagız Onat Yazır, Yagmur Akbulut, Roozbeh Farahbod, Adel Guitouni, Stephen William Neville, Sudhakar Ganti, and Yvonne Coady. Autonomous Re- source Consolidation Management in Clouds Using IMPROMPTU Extensions. In IEEE CLOUD’12, pages 614–621, 2012. en_US
dc.description.scholarlevel Graduate en_US
dc.description.proquestcode 0984 en_US


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