A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), An...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
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Science Publications
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30677/1/A%20survey.pdf |
| _version_ | 1796971789281132544 |
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| author | Ambursa, Faruku Umar Latip, Rohaya |
| author_facet | Ambursa, Faruku Umar Latip, Rohaya |
| author_sort | Ambursa, Faruku Umar |
| collection | UPM |
| description | Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several
researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased
scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category. |
| first_indexed | 2024-03-06T08:18:17Z |
| format | Article |
| id | upm.eprints-30677 |
| institution | Universiti Putra Malaysia |
| language | English English |
| last_indexed | 2024-03-06T08:18:17Z |
| publishDate | 2013 |
| publisher | Science Publications |
| record_format | dspace |
| spelling | upm.eprints-306772015-09-21T08:39:19Z http://psasir.upm.edu.my/id/eprint/30677/ A survey : particle swarm optimization-based algorithms for grid computing scheduling systems. Ambursa, Faruku Umar Latip, Rohaya Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category. Science Publications 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30677/1/A%20survey.pdf Ambursa, Faruku Umar and Latip, Rohaya (2013) A survey : particle swarm optimization-based algorithms for grid computing scheduling systems. Journal of Computer Science, 9 (12). pp. 1669-1679. ISSN 1549-3636 http://thescipub.com/issue-jcs/9/12 10.3844/jcssp.2013.1669.1679 English |
| spellingShingle | Ambursa, Faruku Umar Latip, Rohaya A survey : particle swarm optimization-based algorithms for grid computing scheduling systems. |
| title | A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
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| title_full | A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
|
| title_fullStr | A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
|
| title_full_unstemmed | A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
|
| title_short | A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.
|
| title_sort | survey particle swarm optimization based algorithms for grid computing scheduling systems |
| url | http://psasir.upm.edu.my/id/eprint/30677/1/A%20survey.pdf |
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