Title: Energy efficient task scheduling using adaptive PSO for cloud computing

Authors: Rama Rani; Ritu Garg

Addresses: Computer Engineering Department, National Institute of Technology, Kurukshetra, India ' Computer Engineering Department, National Institute of Technology, Kurukshetra, India

Abstract: Cloud computing is an important research domain where all computational resources are networked globally and shared to users easily. Cloud service provider (CSP) wants the eco-friendly solution to resolve these issues. To enhance the performance of cloud computing resources, task scheduling is of prime concern. Further, the growth of cloud computing resources leads to a large amount of energy consumption and carbon footprints. Thus, this paper aims to reduce the makespan along with energy consumption for independent tasks. For this purpose, we proposed energy efficient adaptive particle swarm optimisation (EE-APSO) algorithm for independent tasks scheduling decision. Each particle represents a potential solution, and small position value (SPV) rule is used to change continuous particle position vector to discrete particle position vector. PSO is made adaptive by varying acceleration coefficients and inertia weight. We also introduced mutation operation to avoid the PSO algorithm getting stuck in local minima and explore the whole search space efficiently. Result analysis demonstrated that our proposed algorithm EE-APSO using SPV rule gives better results than min-min, max-min and genetic algorithm (GA) in terms of makespan and energy consumption.

Keywords: cloud computing; independent task scheduling; particle swarm optimisation; PSO; energy consumption; makespan.

DOI: 10.1504/IJRIS.2021.114630

International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.2, pp.50 - 58

Received: 07 Aug 2019
Accepted: 18 Sep 2019

Published online: 29 Apr 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article