Machine Learning Feature Selection for Tuning Memory Page Swapping
Rick Battle · Calhoun: The Naval Postgraduate School Institutional Archive (Naval Postgraduate School) · 2013
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.