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2021 | OriginalPaper | Chapter

A QoS Aware Binary Salp Swarm Algorithm for Effective Task Scheduling in Cloud Computing

Authors : Richa Jain, Neelam Sharma

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

Day by day task scheduling becomes a more challenging issue as the user’s demand increases in cloud computing. It is a tedious task to deliver resources according to the user’s request with satisfying quality of service (QoS) requirement for both user and service provider. Many researchers have proved that meta-heuristic algorithms give better results for this problem. It inspired us to adopt a recently proposed Salp Swarm Algorithm to optimize request–resource mapping in cloud computing. This proposed QoS aware Binary Salp Swarm algorithm (QBSSA) has been inspired by the nature of salp during the searching and navigating for food in the sea. In this paper, QBSSA is simulated and compared with other most popular meta-heuristic algorithms, i.e., Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO). From the simulation results, it is proved that QBSSA outperforms others in terms of makespan and resource utilization, throughput, and average waiting time.

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Title: A QoS Aware Binary Salp Swarm Algorithm for Effective Task Scheduling in Cloud Computing
Authors: Richa Jain
Neelam Sharma
Publisher: Springer Singapore
Book: Progress in Advanced Computing and Intelligent Engineering
Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-6353-9_43

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