Multi-objective cloud workflow scheduling algorithm based on grid variance
Multi-objective cloud workflow scheduling algorithm based on grid variance and the strategy of bad particles self-learning were presented.Firstly,the characteristics of task scheduling was token into consideration,and particle encoding was discredited.Secondly,the strategy of mapping Pareto optimal...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2019-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019035/ |
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| _version_ | 1841530462953013248 |
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| author | Xiaoan BAO Yundi CAO Na ZHANG Junyan QIAN Jianwen CAO |
| author_facet | Xiaoan BAO Yundi CAO Na ZHANG Junyan QIAN Jianwen CAO |
| author_sort | Xiaoan BAO |
| collection | DOAJ |
| description | Multi-objective cloud workflow scheduling algorithm based on grid variance and the strategy of bad particles self-learning were presented.Firstly,the characteristics of task scheduling was token into consideration,and particle encoding was discredited.Secondly,the strategy of mapping Pareto optimal workflow scheduling set to self-adaptive grid coordinate system,and calculating the grid distribution value of each Pareto optimal solution was used.Thirdly,grid variance was adopted to evaluate the diversity of current Pareto front and dynamically adjust evolution strategies.Finally,the concept of being dominated times was introduced into bad particles self-learning strategy for filtering out bad particles in population.The simulation experiment shows that workflow scheduling solution set by this algorithm is better than the MOPSO algorithm on both IGD and S performance indexes,and the optimal value is superior to the ε-FDPSO and NSGA-Ⅱ algorithm. |
| format | Article |
| id | doaj-art-9c8885c4de834955b69f422533d8d527 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2019-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-9c8885c4de834955b69f422533d8d5272025-01-15T03:03:18ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012019-02-013511359590944Multi-objective cloud workflow scheduling algorithm based on grid varianceXiaoan BAOYundi CAONa ZHANGJunyan QIANJianwen CAOMulti-objective cloud workflow scheduling algorithm based on grid variance and the strategy of bad particles self-learning were presented.Firstly,the characteristics of task scheduling was token into consideration,and particle encoding was discredited.Secondly,the strategy of mapping Pareto optimal workflow scheduling set to self-adaptive grid coordinate system,and calculating the grid distribution value of each Pareto optimal solution was used.Thirdly,grid variance was adopted to evaluate the diversity of current Pareto front and dynamically adjust evolution strategies.Finally,the concept of being dominated times was introduced into bad particles self-learning strategy for filtering out bad particles in population.The simulation experiment shows that workflow scheduling solution set by this algorithm is better than the MOPSO algorithm on both IGD and S performance indexes,and the optimal value is superior to the ε-FDPSO and NSGA-Ⅱ algorithm.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019035/cloud workflow schedulingmulti-objective optimizationparticle swarm algorithmgrid variancedynamic adjustment |
| spellingShingle | Xiaoan BAO Yundi CAO Na ZHANG Junyan QIAN Jianwen CAO Multi-objective cloud workflow scheduling algorithm based on grid variance Dianxin kexue cloud workflow scheduling multi-objective optimization particle swarm algorithm grid variance dynamic adjustment |
| title | Multi-objective cloud workflow scheduling algorithm based on grid variance |
| title_full | Multi-objective cloud workflow scheduling algorithm based on grid variance |
| title_fullStr | Multi-objective cloud workflow scheduling algorithm based on grid variance |
| title_full_unstemmed | Multi-objective cloud workflow scheduling algorithm based on grid variance |
| title_short | Multi-objective cloud workflow scheduling algorithm based on grid variance |
| title_sort | multi objective cloud workflow scheduling algorithm based on grid variance |
| topic | cloud workflow scheduling multi-objective optimization particle swarm algorithm grid variance dynamic adjustment |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019035/ |
| work_keys_str_mv | AT xiaoanbao multiobjectivecloudworkflowschedulingalgorithmbasedongridvariance AT yundicao multiobjectivecloudworkflowschedulingalgorithmbasedongridvariance AT nazhang multiobjectivecloudworkflowschedulingalgorithmbasedongridvariance AT junyanqian multiobjectivecloudworkflowschedulingalgorithmbasedongridvariance AT jianwencao multiobjectivecloudworkflowschedulingalgorithmbasedongridvariance |