Task scheduling on computational grids using Gravitational Search Algorithm
Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfull...
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| Format: | Article |
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Springer
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35599/ |
| _version_ | 1848848100264771584 |
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| author | Zarrabi, Amirreza Samsudin, Khairulmizam |
| author_facet | Zarrabi, Amirreza Samsudin, Khairulmizam |
| author_sort | Zarrabi, Amirreza |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules. |
| first_indexed | 2025-11-15T09:29:08Z |
| format | Article |
| id | upm-35599 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:29:08Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-355992016-01-18T02:04:21Z http://psasir.upm.edu.my/id/eprint/35599/ Task scheduling on computational grids using Gravitational Search Algorithm Zarrabi, Amirreza Samsudin, Khairulmizam Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules. Springer 2014-09 Article PeerReviewed Zarrabi, Amirreza and Samsudin, Khairulmizam (2014) Task scheduling on computational grids using Gravitational Search Algorithm. Cluster Computing, 17 (3). pp. 1001-1011. ISSN 1386-7857; ESSN: 1573-7543 http://link.springer.com/article/10.1007%2Fs10586-013-0338-8 10.1007/s10586-013-0338-8 |
| spellingShingle | Zarrabi, Amirreza Samsudin, Khairulmizam Task scheduling on computational grids using Gravitational Search Algorithm |
| title | Task scheduling on computational grids using Gravitational Search Algorithm |
| title_full | Task scheduling on computational grids using Gravitational Search Algorithm |
| title_fullStr | Task scheduling on computational grids using Gravitational Search Algorithm |
| title_full_unstemmed | Task scheduling on computational grids using Gravitational Search Algorithm |
| title_short | Task scheduling on computational grids using Gravitational Search Algorithm |
| title_sort | task scheduling on computational grids using gravitational search algorithm |
| url | http://psasir.upm.edu.my/id/eprint/35599/ http://psasir.upm.edu.my/id/eprint/35599/ http://psasir.upm.edu.my/id/eprint/35599/ |