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.authorAkbulut, Yagmur
dc.contributor.supervisorGanti, Sudhakar
dc.contributor.supervisorCoady, Yvonne
dc.date.accessioned2014-08-20T22:41:52Z
dc.date.available2014-08-20T22:41:52Z
dc.date.copyright2014en_US
dc.date.issued2014-08-20
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractCloud 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.description.proquestcode0984en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationYagız 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.identifier.urihttp://hdl.handle.net/1828/5579
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectDynamic Resource Allocationen_US
dc.subjectCloud Computingen_US
dc.subjectMCDAen_US
dc.subjectOutranking Methodsen_US
dc.subjectSingle Synthesizing Methodsen_US
dc.subjectAHPen_US
dc.subjectQualiflexen_US
dc.subjectPrometheeen_US
dc.subjectElectreen_US
dc.subjectRegimeen_US
dc.subjectMAUTen_US
dc.subjectSmarten_US
dc.subjectOresteen_US
dc.subjectmultiple-criteria decision analysisen_US
dc.titleAutonomous Resource Allocation in Clouds: A Comprehensive Analysis of Single Synthesizing Criterion and Outranking Based Multiple Criteria Decision Analysis Methodsen_US
dc.typeThesisen_US

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