Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm

The optimization of task scheduling in cloud computing is built with the purpose of improving its working efficiency. Aiming at resolving the deficiencies during the method deployment, supporting algorithms are therefore introduced. This paper proposes a particle swarm optimization algorithm with th...

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Bibliographic Details
Published in:Cluster computing Vol. 22; no. Suppl 2; pp. 2761 - 2769
Main Authors: Chen, Xuan, Long, Dan
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2019
Springer Nature B.V
Subjects:
Algorithms
Ant colony optimization
Cloud computing
Computer Communication Networks
Computer Science
Decomposition
Employment
Genetic algorithms
Normal distribution
Operating Systems
Optimization
Particle swarm optimization
Processor Architectures
Scheduling
Task analysis
Task scheduling
Task scheduling
Cloud computing
Ant colony optimization
Particle swarm algorithm
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:The optimization of task scheduling in cloud computing is built with the purpose of improving its working efficiency. Aiming at resolving the deficiencies during the method deployment, supporting algorithms are therefore introduced. This paper proposes a particle swarm optimization algorithm with the combination of based on ant colony optimization, which proposes the parameter determination into particle swarm algorithm. The integrated algorithm is capable of keeping particles in the fitness level at a certain concentration and guarantee the diversity of population. Further, the global best solution with high accurate converge can be exactly gained with the adjustment of learning factor. After the implementation of proposed method in task scheduling, the scheme for optimizing task scheduling shows better working performance in fitness, cost as well as running period, which presents a more reliable and efficient idea of optimal task scheduling.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-017-1479-y