CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling

CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling

Aiming at the multiobjective cloud resource scheduling problem, with the goal of optimizing the total completion time and total execution cost of the task, a fuzzy cloud resource scheduling model is established using the method of fuzzy mathematics.Utilizing the advantage of the covariance matrix t...

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Bibliographic Details
Main Authors: LI Cheng-yan, SONG Yue, MA Jin-tao
Format: Article
Language:zho
Published: Harbin University of Science and Technology Publications 2022-02-01
Series:Journal of Harbin University of Science and Technology
Subjects:
cloud computing
task scheduling
particle swarm algorithm
covariance matrix adaptation evolution strategy
Online Access:https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2053
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Summary:Aiming at the multiobjective cloud resource scheduling problem, with the goal of optimizing the total completion time and total execution cost of the task, a fuzzy cloud resource scheduling model is established using the method of fuzzy mathematics.Utilizing the advantage of the covariance matrix that can solve the non-convexity problem, adopting the covariance evolution strategy to initialize the population, a hybrid intelligent optimization algorithm CMA-PSO algorithm (covariance matrix adaptation evolution strategy particle swarm optimization，CMA-PSO) is proposed to solve the fuzzy cloud resource scheduling model.The Cloudsim simulation platform was used to randomly generate cloud computing resource scheduling data, and the CMA-PSO algorithm was tested.The experimental results showed that compared with the PSO algorithm (particle swarm optimization), the optimization capability of CMA-PSO algorithm is increased by 28%, the number of iterations of CMA-PSO algorithm is increased by 20%, and it has good load balancing performance.
ISSN:1007-2683

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