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Applying Machine Learning Techniques to Improve Linux Process Scheduling

Abstract—In this work we use Machine Learning (ML) techniques
to learn the CPU time-slice utilization behavior of known
programs in a Linux system. Learning is done by an analysis of
certain static and dynamic attributes of the processes while they
are being run. Our objective was to discover the most important
static and dynamic attributes of the processes that can help best
in prediction of CPU burst times which minimize the process
TaT (Turn-around-Time). In our experimentation we modify the
Linux Kernel scheduler (version 2.4.20-8) to allow scheduling
with customized time slices. The “Waikato Environment for
Knowledge Analysis” (Weka), an open source machine-learning
tool is used to nd the most suitable ML method to characterize
our programs. We experimentally nd that the C4.5 Decision
Tree algorithm most effectively solved the problem. We nd that
predictive scheduling could reduce TaT in the range of 1:4%
to 5:8%. This was due to a reduction in the number of context
switches needed to complete the process execution. We nd our
result interesting in the context that generally operating systems
presently never make use of a program's previous execution
history in their scheduling behavior.

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