Title: Swarm intelligence-based task scheduling algorithm for load balancing in cloud system

Authors: D. Komalavalli; T. Padma

Addresses: Bharathiar University, Coimbatore, India ' Department of Master of Computer Applications, Sona College of Technology, Salem, India

Abstract: As on miniature devices to military applications, the cloud computing plays a vital role. Building efficient cloud management systems will lead to improve extraordinary features in the applications of cloud services. In cloud atmosphere, enormous tasks are performed simultaneously; an effectual task scheduling is very important role to get better performance of the cloud system. An assortment of cloud-based task scheduling algorithms is offered that schedule the user's task to resources for implementation. The innovation of cloud computing, conventional scheduling algorithms cannot gratify the cloud's requirements, the researchers are frustrating to modify conventional algorithms that can accomplish the cloud needs similar to rapid elasticity, resource pooling and on-demand self-service. Also, the priority becomes an important task when dealing with critical functionality systems. Real world invocations are needed to make an efficient selection in cloud services through a collection of functionally equivalent services. This research aims to detect a novel method to predict the system functionality without consuming more time and less expensive for implementation. Investigation on swarm intelligence-based task scheduling is presented. This will improve the power consumption by reducing overloads when more services opt for a single load. Experiments were carried out to test the effectiveness of the method.

Keywords: task scheduling; particle swarm optimisation; PSO; bat algorithm; swarm intelligence; cloud computing; load balancing; low power.

DOI: 10.1504/IJENM.2021.112669

International Journal of Enterprise Network Management, 2021 Vol.12 No.1, pp.1 - 16

Received: 08 Jul 2019
Accepted: 29 Nov 2019

Published online: 26 Jan 2021 *

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