Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment
Abstract To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the clou...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-10-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-024-00666-7 |
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| _version_ | 1850181997894303744 |
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| author | Jian Zhu Qian Li Shi Ying Zhihua Zheng |
| author_facet | Jian Zhu Qian Li Shi Ying Zhihua Zheng |
| author_sort | Jian Zhu |
| collection | DOAJ |
| description | Abstract To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%. |
| format | Article |
| id | doaj-art-ac0a77df02fa46b497f4ac953be6767c |
| institution | OA Journals |
| issn | 1875-6883 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-ac0a77df02fa46b497f4ac953be6767c2025-08-20T02:17:46ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-10-0117111410.1007/s44196-024-00666-7Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service EnvironmentJian Zhu0Qian Li1Shi Ying2Zhihua Zheng3School of Computer Science, Wuhan UniversitySchool of Computer and Information Engineering, Guangxi Vocational Normal UniversitySchool of Computer Science, Wuhan UniversityNature and Resources Informat Ctr Guangxi ProAbstract To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.https://doi.org/10.1007/s44196-024-00666-7Cloud service environmentDynamic adaptationParticle swarmSaaS platformParallel task schedulingLoad leveling |
| spellingShingle | Jian Zhu Qian Li Shi Ying Zhihua Zheng Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment International Journal of Computational Intelligence Systems Cloud service environment Dynamic adaptation Particle swarm SaaS platform Parallel task scheduling Load leveling |
| title | Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment |
| title_full | Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment |
| title_fullStr | Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment |
| title_full_unstemmed | Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment |
| title_short | Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment |
| title_sort | research on parallel task scheduling algorithm of saas platform based on dynamic adaptive particle swarm optimization in cloud service environment |
| topic | Cloud service environment Dynamic adaptation Particle swarm SaaS platform Parallel task scheduling Load leveling |
| url | https://doi.org/10.1007/s44196-024-00666-7 |
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