Machine learning feature selection for tuning memory page swapping

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.

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Monterey, California: Naval Postgraduate School

Year dc:date.issued

2013

Author dc:creator

• Battle, Rick

Advisor dc:contributor.advisor

• Martell, Craig

Statement 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.

Handle dc:identifier.uri

https://hdl.handle.net/10945/37585

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/37585