A Neural Net-Based Approach for CPU Utilization

Abstract

CPU scheduling is an important subject to maximize CPU utilization in the context of operating systems. Multiprogramming operating systems need CPU scheduling for organization of processes to be executed. The order of process execution is determined by a CPU scheduling policy in use. The utilization of CPU depends on the selection of scheduling algorithms. There are several scheduling policies in the literature such as First-Come, First-Served scheduling, Shortest-Job-First scheduling, Last-Come, First-Served scheduling, Priority scheduling. On the other hand, there are some criteria (waiting time, throughput number, turnaround time, response time) to measure the eficiency of these policies. It is important that we choose the scheduling policy which has the minimum waiting time as this is crucial stage of utilizing CPU efficiently. This paper explores an alternative, neural network approach to build a CPU scheduling model to obtain the waiting time measure. In this paper, we will show that neural networks can be used to model scheduling policies and can predict the waiting time of processes. Three learning algorithms and three different neuron numbers in the hidden layer of the network are studied to boost the eficiency of neural network model for waiting time prediction. A comparison between Neural-Network Based Model and First-Come, First-Served scheduling, Shortest-Job-First scheduling, Last-Come, First-Served scheduling are provided. The results reveal the effectiveness of neural networks in predicting waiting times, and thus suggest that it can be useful and practical addition to the framework of operating systems.