Scheduling jobs in computational grid using hybrid ACS and GA approach
Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable tim...
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my.uum.repo.130892016-04-28T01:44:51Z http://repo.uum.edu.my/13089/ Scheduling jobs in computational grid using hybrid ACS and GA approach Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana QA76 Computer software Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time.This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem.The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task.The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime.Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan.However, for flowtime, ant system and genetic algorithm perform better. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/13089/1/ComComAp%20-%20Mustafa.pdf Alobaedy, Mustafa Muwafak and Ku-Mahamud, Ku Ruhana (2014) Scheduling jobs in computational grid using hybrid ACS and GA approach. In: Computing, Communications & Applications Conference - (ComComAp 2014), October 20—22, 2014, Beijing, China. http://comcomap.net/2014/ |
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QA76 Computer software Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana Scheduling jobs in computational grid using hybrid ACS and GA approach |
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Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time.This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem.The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task.The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime.Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan.However, for flowtime, ant system and genetic algorithm perform better. |
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Conference or Workshop Item |
author |
Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
author_facet |
Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
author_sort |
Alobaedy, Mustafa Muwafak |
title |
Scheduling jobs in computational grid using hybrid ACS and GA approach |
title_short |
Scheduling jobs in computational grid using hybrid ACS and GA approach |
title_full |
Scheduling jobs in computational grid using hybrid ACS and GA approach |
title_fullStr |
Scheduling jobs in computational grid using hybrid ACS and GA approach |
title_full_unstemmed |
Scheduling jobs in computational grid using hybrid ACS and GA approach |
title_sort |
scheduling jobs in computational grid using hybrid acs and ga approach |
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2014 |
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http://repo.uum.edu.my/13089/1/ComComAp%20-%20Mustafa.pdf http://repo.uum.edu.my/13089/ http://comcomap.net/2014/ |
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1644281082966704128 |
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13.145126 |