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dc.contributor.advisorKörpeoğlu, İbrahim
dc.contributor.authorUrul, Gökalp
dc.date.accessioned2018-01-19T10:57:56Z
dc.date.available2018-01-19T10:57:56Z
dc.date.copyright2018-01
dc.date.issued2018-01
dc.date.submitted2018-01-18
dc.identifier.urihttp://hdl.handle.net/11693/35741
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2018.en_US
dc.descriptionIncludes bibliographical references (leaves 51-54).en_US
dc.description.abstractWith tremendous increase in Internet capacity and services, the demand for cloud computing has also grown enormously. This enormous demand for cloud based data storage and processing forces cloud providers to optimize their platforms and facilities. Reducing energy consumption while maintaining service level agreements (SLAs) is one of the most important issues in this optimization effort. Dynamic virtual machine allocation and migration is one of the techniques to achieve this goal. This technique requires constant measurement and prediction of usage of machine resources to trigger migrations at right times. In this thesis, we present a dynamic virtual machine allocation and migration method utilizing CPU usage prediction to improve energy efficiency while maintaining agreed quality of service levels in cloud datacenters. Our proposed method, called LRAPS, tries to estimate short-term CPU utilization of hosts based on their utilization history. This estimation is then used to detect overloaded and underloaded hosts as part of live migration process. If a host is overloaded, some of the VMs running on that host are migrated to other hosts to avoid SLA violations; if a host is underloaded, all of the VMs in that host are tried to be migrated to other machines so that the host can be powered off. We did extensive simulation experiments using CloudSim to evaluate the efficiency and effectiveness of our proposed method. Our simulation experiments show that our method is feasible to apply and can signi cantly reduce power consumption and SLA violations in cloud systems.en_US
dc.description.statementofresponsibilityby Gökalp Ural.en_US
dc.format.extentx, 54 leaves : charts ; 30 cmen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClouden_US
dc.subjectVirtual machineen_US
dc.subjectResource allocationen_US
dc.subjectLocal regressionen_US
dc.subjectCross validationen_US
dc.subjectDynamic allocationen_US
dc.titleEnergy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacentersen_US
dc.title.alternativeİşlemci kullanım tahminiyle enerji verimli dinamik sanal makine yerleştirmesien_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB157376


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