CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling
Aiming at the multiobjective 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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Harbin University of Science and Technology Publications
2022-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2053 |
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| author | LI Cheng-yan SONG Yue MA Jin-tao |
| author_facet | LI Cheng-yan SONG Yue MA Jin-tao |
| author_sort | LI Cheng-yan |
| collection | DOAJ |
| description | Aiming at the multiobjective 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. |
| format | Article |
| id | doaj-art-a128479780b64e0d8f561a35dcaae2c9 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-a128479780b64e0d8f561a35dcaae2c92025-08-20T03:15:56ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832022-02-012701313910.15938/j.jhust.2022.01.005CMA-PSO Algorithm for Fuzzy Cloud Resource SchedulingLI Cheng-yan0SONG Yue1MA Jin-tao2School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,ChinaAiming at the multiobjective 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.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2053cloud computingtask schedulingparticle swarm algorithmcovariance matrix adaptation evolution strategy |
| spellingShingle | LI Cheng-yan SONG Yue MA Jin-tao CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling Journal of Harbin University of Science and Technology cloud computing task scheduling particle swarm algorithm covariance matrix adaptation evolution strategy |
| title | CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling |
| title_full | CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling |
| title_fullStr | CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling |
| title_full_unstemmed | CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling |
| title_short | CMA-PSO Algorithm for Fuzzy Cloud Resource Scheduling |
| title_sort | cma pso algorithm for fuzzy cloud resource scheduling |
| topic | cloud computing task scheduling particle swarm algorithm covariance matrix adaptation evolution strategy |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2053 |
| work_keys_str_mv | AT lichengyan cmapsoalgorithmforfuzzycloudresourcescheduling AT songyue cmapsoalgorithmforfuzzycloudresourcescheduling AT majintao cmapsoalgorithmforfuzzycloudresourcescheduling |