Improved time quantum length estimation for round robin scheduling algorithm using neural network

Abstract

In most cases, the quantum time length is taken to be fix in all applications that use Round Robin (RR) scheduling algorithm. Many attempts aim to determination of the optimal length of the quantum that results in a small average turnaround time, but the unknown nature of the tasks in the ready queue make the problem more complicated: Considering a large quantum length makes the RR algorithm behave like a First Come First Served (FIFO) scheduling algorithm, and a small quantum length cause high number of contexts switching. In this paper we propose a RR scheduling algorithm based on Neural Network Models for predicting the optimal quantum length which lead to a minimum average turnaround time. The quantum length depends on tasks burst times available in the ready queue. Rather than conventional traditional methods using fixed quantum length, this one giving better results by minimizing the average turnaround time for almost any set of jobs in the ready queue