Abstract
This paper investigates an alternative way of efficiently matching and allocating grid resources to user jobs, in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of Genetic Algorithm, using populations based on Multisets is presented. For the proposed resource allocation method, an additional mechanism (populations based on multiset) is introduced into the genetic algorithm components, to enhance its search capability in a large problem space. A computational experiment is presented in order to show the importance of operator improvement on traditional genetic algorithms. The preliminary performance results show that the introduction of an additional operator fine-tuning is efficient in both speed and precession, and can keep up with the high job arrival rates.
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Acknowledgments
We would like to thank the corps member, Nneoma Okoroafor and Bridget Pwajok, who worked with us during the initial preparation of this paper. We also thank all the anonymous reviewers for their comments and recommendations, which have been crucial to improving the quality of this work.
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Ezugwu, A.E., Yakmut, D.I., Ochang, P.A., Buhari, S.M., Frincu, M.E., Junaidu, S.B. (2016). Multiset Genetic Algorithm Approach to Grid Resource Allocation. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_1
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DOI: https://doi.org/10.1007/978-3-319-28031-8_1
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