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A bi-objective Particle Swarm Optimization for Task Scheduling in Cloud Computing Environments


Received: 20 October 2021
Revised: 21 November 2021
Accepted: 19 December 2021

Fatemeh Alizadeh, Shahram Jamali, Soheila Sadeqi

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Cloud computing is the growth of distributed computing, parallel computing, utility computing and grid computing, or defined as the commercial implementation of these computer science theories. One of the fundamental issues in cloud environment is the task scheduling which plays the key role of efficiency of the whole cloud computing facilities. Scheduling maps the user’s tasks to resources to be executed efficiently in order to benefit both the service providers and customers. Since the cloud task scheduling is an NP-hard optimization problem, many meta -heuristic algorithms have been proposed to solve it. In this paper a policy based on particle swarm optimization compared with genetic algorithm and FCFS, has been introduced. PSO is a population-based search algorithm based on the simulation of the social behavior of birds within the flock. The main goal in this research is minimizing the makespan and flowtime of a given tasks set. Proposed policy and two other algorithms have been simulated using Cloudsim toolkit package. The results showed that PSO performed better than genetic and FCFS algorithms.

Keywords: Cloud computing, task scheduling, particle swarm optimization, makespan.

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Publication Information

Journal of Practical Management Information Systems

ISSN: 2783-2910

Rose Scientific Publication
Melbourne, Australia

Director In-charge:
Seyed Mojtaba Mostafavi, Ph.D.

Dr. Tim Kulikov

Executive Manager
Mehrdad Fallahi Barzoki, Ph.D.

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