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dc.contributor.advisorMartell, Craig
dc.contributor.authorBattle, Rick
dc.dateSep-13
dc.date.accessioned2013-11-20T23:35:52Z
dc.date.available2013-11-20T23:35:52Z
dc.date.issued2013-09
dc.identifier.urihttp://hdl.handle.net/10945/37585
dc.description.abstractThis thesis is an exploration of the virtual memory subsystem in the modern Linux kernel. It applies machine learning to find areas where better page-out decisions can be made. Two areas of possible improvement are identified and analyzed. The first area explored arises because pages in a computation appear repeatedly in a sequence. This is an example of temporal locality. In this instance, we can predict pages that will not be recalled again from the backing store with a precision and recall of 0.82 and 0.81, respectively, with a baseline of 0.30. The second is trying to predict when the system has made bad page-out decisions, those which lived in the backing store for less than one second before being recalled into RAM. In this case, we achieved a precision of 0.82 and a recall of 0.81 with a baseline of 0.12.en_US
dc.description.urihttp://archive.org/details/machinelearningf1094537585
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleMachine learning feature selection for tuning memory page swappingen_US
dc.typeThesisen_US
dc.contributor.secondreaderYoung, Joel
dc.contributor.departmentComputer Science
dc.subject.authorLinux Kernel, Virtual Memory, Machine Learning, Temporal Localityen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster Of Science In Computer Scienceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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