Prakhar Ojha, Siddhartha R Thota, Vani M and Mohit P Tahilianni, National Institute of Technology - Karnataka, India

An operating system scheduler is expected to not allow processor stay idle if there is any process ready or waiting for its execution. This problem gains more importance as the numbers of processes always outnumber the processors by large margins. It is in this regard that schedulers are provided with the ability to preempt a running process, by following any scheduling algorithm, and give us an illusion of simultaneous running of several processes. A process which is allowed to utilize CPU resources for a fixed quantum of time (termed as timeslice for preemption) and is then preempted for another waiting process. Each of these 'process preemption' leads to considerable overhead of CPU cycles which are valuable resource for runtime execution. In this work we try to utilize the historical performances of a scheduler and predict the nature of current running process, thereby trying to reduce the number of preemptions. We propose a machine-learning module to predict a better performing timeslice which is calculated based on static knowledge base and adaptive reinforcement learning based suggestive module. Results for an "adaptive timeslice parameter" for preemption show good saving on CPU cycles and efficient throughput time.

Reinforcement Learning, Online Machine Learning, Operating System, Scheduler, Preemption