An adaptive weight based particle swarm optimization scheduling algorithm on cloud computing system
Authors: 
Fan Pei, Dong Minggang, Jing ChaoAuthors Info & Claims
Pages 604 - 608
Abstract
The resource scheduling problem in cloud computing is one of the most concerned issues of researchers in related fields in recent years. With the aim of solving defects in particle swarm optimization algorithm (PSO) that it is easy to fall into local optimal solution and "premature", we propose a particle swarm optimization scheduling algorithm based on adaptive weights. By improving the inertia weight factor and using the number of iterations to control the size of the inertia weight, the algorithm convergence speed can be accelerated. Meanwhile, by updating the particle speed to update the weights of the cognitive and social coefficients in the number of iterations, the fitness function of the particles is improved to strengthen the adaptive ability of the particles and made the algorithm more in line with objective laws. A large number of simulation comparison experiments has been conducted on the Cloudsim platform. The experimental results show that the improved particle swarm optimization algorithm has better performance in adaptability, convergence speed, and capabilities of finding global optimums.
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Index Terms
- An adaptive weight based particle swarm optimization scheduling algorithm on cloud computing system
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Published In
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
Copyright © 2020 ACM.
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In-Cooperation
- Guilin: Guilin University of Technology, Guilin, China
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 March 2021
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- Research-article
- Research
- Refereed limited
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EBIMCS 2020
EBIMCS 2020: 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 5 - 6, 2020
Wuhan, China
Acceptance Rates
EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%
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