A Review on Meta-heuristic Independent Task Scheduling Algorithms in Cloud Computing

Anup Gade, M. Nirupama Bhat and Nita Thakare
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Anup Gade: VFSTR Deemed to Be University
M. Nirupama Bhat: VFSTR Deemed to Be University
Nita Thakare: Priyadarshini College of Engineering

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1165-1180 from Springer

Abstract: Abstract Cloud computing has gained status of red carpet in recent years. The only rationale behind achieving this huge applause for cloud is its accessibility in requisite personalized form without harming its effectiveness. Efficiency of cloud computing has became the outcome of scheduling algorithms applied to maintained its potential, high end hardware involved and networks that support this huge infrastructure. This article is focusing on tasks scheduling in cloud computing particularly when tasks are of independent nature. Various techniques are available for minimizing scheduling time of tasks still optimization has scope in this regards. Task scheduling is usually considered as NP-hard problem and meta-heuristic algorithms are treated as one of the best solution in dealing with this kind of problem. There are plenty of meta-heuristic techniques presented as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Language Championship Algorithm (LCA), Artificial Bee Colony (ABC) to mentioned a few. Comprehensive study and comparative analysis of these diverse types of algorithm in the region of user’s view and service provider’s view is articulated here. This article is focusing on tasks scheduling in cloud computing typically when tasks are of independent nature.

Keywords: Task scheduling; Independent tasks scheduling; Meta-heuristic; PSO; ACO; LCA; GA; Cloud computing (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_118

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DOI: 10.1007/978-3-030-41862-5_118

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