A Neural Net-Based Approach for CPU Utilization

Year 2017, , 263 - 272, 31.07.2017

Sibel Senan

https://doi.org/10.17671/gazibtd.331037

Cited By: 1

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.

Keywords

CPU Scheduling, Operating Systems, Neural Networks

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