Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment
At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for sol...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10250772/ |
| _version_ | 1827807440285990912 |
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| author | Jieguang He Xiaoli Liu |
| author_facet | Jieguang He Xiaoli Liu |
| author_sort | Jieguang He |
| collection | DOAJ |
| description | At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching–Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO’s performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency. |
| first_indexed | 2024-03-11T21:57:26Z |
| format | Article |
| id | doaj.art-2bf029d65b514b99877cc54ddaabdc2e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T21:57:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2bf029d65b514b99877cc54ddaabdc2e2023-09-25T23:00:37ZengIEEEIEEE Access2169-35362023-01-011110075510076810.1109/ACCESS.2023.331473510250772Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud EnvironmentJieguang He0https://orcid.org/0000-0003-2321-1022Xiaoli Liu1https://orcid.org/0009-0003-0420-000XCollege of Computer Science, Guangdong University of Petrochemical Technology, Maoming, ChinaCollege of Computer Science, Guangdong University of Petrochemical Technology, Maoming, ChinaAt present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching–Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO’s performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.https://ieeexplore.ieee.org/document/10250772/Workflow schedulingcloud computingteaching–learning-based optimizationopposition-based learningsearch boundarypopulation information |
| spellingShingle | Jieguang He Xiaoli Liu Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment IEEE Access Workflow scheduling cloud computing teaching–learning-based optimization opposition-based learning search boundary population information |
| title | Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment |
| title_full | Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment |
| title_fullStr | Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment |
| title_full_unstemmed | Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment |
| title_short | Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment |
| title_sort | hybrid teaching x2013 learning based optimization for workflow scheduling in cloud environment |
| topic | Workflow scheduling cloud computing teaching–learning-based optimization opposition-based learning search boundary population information |
| url | https://ieeexplore.ieee.org/document/10250772/ |
| work_keys_str_mv | AT jieguanghe hybridteachingx2013learningbasedoptimizationforworkflowschedulingincloudenvironment AT xiaoliliu hybridteachingx2013learningbasedoptimizationforworkflowschedulingincloudenvironment |