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dc.contributor.advisorSaman Amarasinghe and Yuri Baskakov.en_US
dc.contributor.authorReth, Sarandethen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.date.accessioned2018-02-16T20:05:00Z
dc.date.available2018-02-16T20:05:00Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/113767
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (page 75).en_US
dc.description.abstractWorking sets are sets of pages that have been most recently accessed by virtual machines (VMs). They are often used within the memory scheduler of a hypervisor to estimate the memory demands of VMs running on the hypervisor. In order to manage the memory resources of the hypervisor efficiently, it is essential that these working set sizes be estimated accurately at any given point in time. Currently, a statistical sampling strategy is used within VMware ESX hypervisors to estimate the working set sizes of VMs. Using this technique, a small number of random pages is selected to form a sample set. Access to these sampled pages is then tracked and the percentage of sampled pages that are accessed is used to estimate the working set size of a VM. This technique, though simple, does not provide a very accurate estimation of the working set size. A more promising tool that can be used to accurately estimate the working set size of a VM is a miss ratio curve (MRC). An MRC is a curve that plots the predicted miss ratio of a VM against the total available memory given to the VM. Even though MRCs can estimate working set sizes of VMs with much better accuracy, they are still not widely used in practice because building these curves incurs too much overhead, thus affecting the overall system performance. However, a recent study has found a way to reduce the cost of building these curves, making them a promising tool that can be used to estimate working set sizes. In this thesis, I propose that MRCs be used as an alternative to the statistical sampling strategy currently employed within VMware ESX. I will demonstrate how to apply the state of the art technique found in the recent study to construct accurate MRCs without incurring too much overhead, and use these curves to track working set sizes of VMs. I will also show that these curves can estimate working set sizes of VMs with much better accuracy than the statistical sampling strategy.en_US
dc.description.statementofresponsibilityby Sarandeth Reth.en_US
dc.format.extent75 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMechanical Engineering.en_US
dc.titleTracking working set sizes of virtual machines using miss ratio curvesen_US
dc.title.alternativeTracking working set sizes of VMs using MRCsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering.en_US
dc.identifier.oclc1022281929en_US


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