{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/37585"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/37585","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"Machine learning feature selection for tuning memory page swapping","abstract":"This 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.","abstract_html":"This 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.","abstract_has_math":false,"creators":["Battle, Rick"],"institution":"Monterey, California: Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Computer Science","school":null,"contributors":[],"advisors":["Martell, Craig"],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-09","date_published":"2013-09","updated_at":"2026-07-27T20:26:16Z","subjects":[],"languages":[],"rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. 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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."]},{"key":"dc:title","label":"Title","values":["Machine learning feature selection for tuning memory page swapping"]}]}],"canonical_facts":{"dc:contributor.advisor":["Martell, Craig"],"dc:contributor.department":["Computer Science"],"dc:creator":["Battle, Rick"],"dc:date":["Sep-13"],"dc:date.accessioned":["2013-11-20T23:35:52Z"],"dc:date.available":["2013-11-20T23:35:52Z"],"dc:date.issued":["2013-09"],"dc:description.abstract":["This 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."],"dc:identifier.uri":["https://hdl.handle.net/10945/37585"],"dc:publisher":["Monterey, California: Naval Postgraduate School"],"dc:rights":["This 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."],"dc:title":["Machine learning feature selection for tuning memory page swapping"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:26:16Z"}