An adaptive symbiotic organisms search for constrained task scheduling in cloud computing.
Metaheuristic algorithms have been effective in obtaining near-optimal solutions for NP-Complete problems like task scheduling. However, most of these algorithms still suffer from inadequate balance between local and global search when seeking a global solution, which often results in sub-optimal so...
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| Format: | Article |
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Springer Science and Business Media Deutschland GmbH
2023
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| _version_ | 1811132295987331072 |
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| author | Abdullahi, Mohammed Ngadi, Md. Asri Dishing, Salihu Idi Abdulhamid, Shafi’i Muhammad |
| author_facet | Abdullahi, Mohammed Ngadi, Md. Asri Dishing, Salihu Idi Abdulhamid, Shafi’i Muhammad |
| author_sort | Abdullahi, Mohammed |
| collection | ePrints |
| description | Metaheuristic algorithms have been effective in obtaining near-optimal solutions for NP-Complete problems like task scheduling. However, most of these algorithms still suffer from inadequate balance between local and global search when seeking a global solution, which often results in sub-optimal solutions. In this paper, an adaptive benefit factors based symbiotic organisms search (ABFSOS) is proposed, that adaptively tune SOS control parameters to strike a balance between local and global search procedures for faster convergence speed. Moreover, an adaptive constrained handling strategy is integrated into the proposed algorithm to effectively tune the values of the penalty function, thereby avoiding infeasible solutions and premature convergence. The performance of the proposed constrained multi-objective ABFSOS (CMABFSOS) was evaluated using large instances of both standard, and synthetic workloads, on a standard toolkit simulator (CloudSim). The non-dominated solutions obtained by the proposed CMABFSOS algorithm outperforms the compared algorithms (EMS-C, and ECMSMOO) for all the workload instances. The proposed CMABFSOS algorithm obtained significant improvement of hypervolume (convergence and diversity) over the compared algorithms for the workload instances. The performance improvement of CMABFSOS over EMS-C ranges from 17.02 to 47.73% across the workloads, while the performance improvement over ECMSMOO is between 19.98 to 52.18%. |
| first_indexed | 2024-09-24T00:01:03Z |
| format | Article |
| id | utm.eprints-106233 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-09-24T00:01:03Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | utm.eprints-1062332024-06-20T02:13:45Z http://eprints.utm.my/106233/ An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. Abdullahi, Mohammed Ngadi, Md. Asri Dishing, Salihu Idi Abdulhamid, Shafi’i Muhammad QA75 Electronic computers. Computer science Metaheuristic algorithms have been effective in obtaining near-optimal solutions for NP-Complete problems like task scheduling. However, most of these algorithms still suffer from inadequate balance between local and global search when seeking a global solution, which often results in sub-optimal solutions. In this paper, an adaptive benefit factors based symbiotic organisms search (ABFSOS) is proposed, that adaptively tune SOS control parameters to strike a balance between local and global search procedures for faster convergence speed. Moreover, an adaptive constrained handling strategy is integrated into the proposed algorithm to effectively tune the values of the penalty function, thereby avoiding infeasible solutions and premature convergence. The performance of the proposed constrained multi-objective ABFSOS (CMABFSOS) was evaluated using large instances of both standard, and synthetic workloads, on a standard toolkit simulator (CloudSim). The non-dominated solutions obtained by the proposed CMABFSOS algorithm outperforms the compared algorithms (EMS-C, and ECMSMOO) for all the workload instances. The proposed CMABFSOS algorithm obtained significant improvement of hypervolume (convergence and diversity) over the compared algorithms for the workload instances. The performance improvement of CMABFSOS over EMS-C ranges from 17.02 to 47.73% across the workloads, while the performance improvement over ECMSMOO is between 19.98 to 52.18%. Springer Science and Business Media Deutschland GmbH 2023-07 Article PeerReviewed Abdullahi, Mohammed and Ngadi, Md. Asri and Dishing, Salihu Idi and Abdulhamid, Shafi’i Muhammad (2023) An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. Journal of Ambient Intelligence and Humanized Computing, 14 (7). pp. 8839-8850. ISSN 1868-5137 http://dx.doi.org/10.1007/s12652-021-03632-9 DOI: 10.1007/s12652-021-03632-9 |
| spellingShingle | QA75 Electronic computers. Computer science Abdullahi, Mohammed Ngadi, Md. Asri Dishing, Salihu Idi Abdulhamid, Shafi’i Muhammad An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title | An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title_full | An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title_fullStr | An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title_full_unstemmed | An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title_short | An adaptive symbiotic organisms search for constrained task scheduling in cloud computing. |
| title_sort | adaptive symbiotic organisms search for constrained task scheduling in cloud computing |
| topic | QA75 Electronic computers. Computer science |
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