A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing,...

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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2021; pp. 1 - 14
Main Author: Ababneh, Jafar
Format: Journal Article
Language:English
Published: New York Hindawi 13.09.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Attenuation
Cloud computing
Efficiency
Energy consumption
Energy costs
Mathematical problems
Optimization
Optimization algorithms
Scheduling
Task scheduling
Virtual environments
ISSN:1024-123X, 1563-5147
Online Access:Get full text
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Summary:In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2021/3517145