Open Access

ARTICLE

ATS: A Novel Time-Sharing CPU Scheduling Algorithm Based on Features Similarities

Samih M. Mostafa^1,*, Sahar Ahmed Idris², Manjit Kaur³

1 Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt
2 College of Industrial Engineering, King Khalid University, Abha, Saudi Arabia
3 School of Engineering and Applied Sciences, Bennett University, Greater Noida, India

* Corresponding Author: Samih M. Mostafa. Email: .e.g.

(This article belongs to the Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 70(3), 6271-6288. https://doi.org/10.32604/cmc.2022.021978

Received 22 July 2021; Accepted 23 August 2021; Issue published 11 October 2021

Abstract

Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable Time Slice (ATS). It reduces the time cost by taking the advantage of the low overhead of RR algorithm. In addition, ATS favors short processes allowing them to run longer time than given to long processes. The specific characteristics of each process are; its CPU execution time, weight, time slice, and number of context switches. ATS clusters the processes in groups depending on these characteristics. The traditional RR assigns fixed time slice for each process. On the other hand, dynamic variants of RR assign time slice for each process differs from other processes. The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster. The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes. The datasets used in the comparison vary in their features. The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost.

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