Determining dynamic time quantum in round robin scheduling algorithm using machine learning

One of the most common CPU scheduling algorithms is Round Robin (RR). In RR, a time quantum represents the maximum time that a process can have the processor, and the processor is allocated to the processes of ready queue in circular order. The size of time quantum has a significant effect on the performance of RR, so that if it is small, due to the increase in the number of context switches and the resulting overhead, the CPU utilization will decrease. In contrast, if the time quantum is large, the average response time of the processes increases, which makes the use of RR in interactive applications inefficient. The purpose of this paper is to provide an efficient method for determining dynamic time quantum using machine learning. Initially, a training set including the number of processes and the maximum, minimum, average and median of their burst time and optimal time quantum as the class has been developed. Then, by training machine learning classifiers on this set, the optimal time quantum for new examples is predicted. The experimental results show that in general, the proposed method has a better performance compared to other methods based on the performance measures of scheduling algorithms. For instance, compared to the genetic algorithm, which has the best performance among the existing methods, the average waiting time and the average number of context switches of the proposed method improved by 12 milliseconds and 1.76 units, respectively, and the average turnaround time is increased about 2 milliseconds.