Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization
Abstract In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimiza...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2020-05-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-020-01722-4 |
| _version_ | 1819056931553148928 |
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| author | Shuxiang Li Xianbing Pan |
| author_facet | Shuxiang Li Xianbing Pan |
| author_sort | Shuxiang Li |
| collection | DOAJ |
| description | Abstract In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization. |
| first_indexed | 2024-12-21T13:31:15Z |
| format | Article |
| id | doaj.art-a0fdb6944fb741a5a7b5f9fad6477d0e |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-21T13:31:15Z |
| publishDate | 2020-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-a0fdb6944fb741a5a7b5f9fad6477d0e2022-12-21T19:02:17ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992020-05-012020111210.1186/s13638-020-01722-4Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimizationShuxiang Li0Xianbing Pan1Department of Mathematics and Physics Teaching, Yitong College, Chongqing University of Posts and TelecommunicationsDepartment of Management Engineering, School of Communication, Chongqing University of Posts and TelecommunicationsAbstract In order to improve the adaptive management ability of virtual machine placement in cloud computing, an adaptive management and multi-objective optimization method for virtual machine placement in cloud computing is proposed based on particle swarm optimization (PSO). The objective optimization model of adaptive management of virtual machine placement in cloud computing is constructed by particle swarm evolution, and the global optimization control of adaptive management of virtual machine placement in cloud computing is carried out by introducing extremum perturbation operator. The global dynamic objective function of particle swarm optimization is constructed, and the global optimal solution of virtual machine in cloud computing is found by deconvolution algorithm, and the optimal position of particle swarm is searched in two-dimensional space. The multi-objective optimization problem of adaptive management of virtual machine placement is transformed into particle swarm optimization problem to realize adaptive management and multi-objective optimization of virtual machine placement in cloud computing. Simulation results show that the adaptive management of virtual machine placement in cloud computing using this method has better global optimization ability, better convergence of particle swarm optimization, and better performance of multi-objective optimization.http://link.springer.com/article/10.1186/s13638-020-01722-4Particle swarm optimizationCloud computingVirtual machine placementAdaptive managementMulti-objective optimization |
| spellingShingle | Shuxiang Li Xianbing Pan Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization EURASIP Journal on Wireless Communications and Networking Particle swarm optimization Cloud computing Virtual machine placement Adaptive management Multi-objective optimization |
| title | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| title_full | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| title_fullStr | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| title_full_unstemmed | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| title_short | Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| title_sort | adaptive management and multi objective optimization of virtual machine in cloud computing based on particle swarm optimization |
| topic | Particle swarm optimization Cloud computing Virtual machine placement Adaptive management Multi-objective optimization |
| url | http://link.springer.com/article/10.1186/s13638-020-01722-4 |
| work_keys_str_mv | AT shuxiangli adaptivemanagementandmultiobjectiveoptimizationofvirtualmachineincloudcomputingbasedonparticleswarmoptimization AT xianbingpan adaptivemanagementandmultiobjectiveoptimizationofvirtualmachineincloudcomputingbasedonparticleswarmoptimization |