A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling |
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Authors: | Sergio Nesmachnow Héctor CancelaEnrique Alba |
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Affiliation: | a Universidad de la República, Herrera y Reissig 565, Montevideo, Uruguay b Universidad de Málaga, Spain |
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Abstract: | This work presents a novel parallel micro evolutionary algorithm for scheduling tasks in distributed heterogeneous computing and grid environments. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made in order to develop an efficient method to provide good schedules in reduced execution times. The parallel micro evolutionary algorithm is implemented using MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study of traditional methods and evolutionary algorithms shows that the parallel micro evolutionary algorithm achieves a high problem solving efficacy, outperforming previous results already reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances. |
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Keywords: | Parallel evolutionary algorithms Scheduling Heterogeneous computing Grid |
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