Abstract
Computational grid provides a platform for exploiting various computational resources over wide area networks. One of the concerns in implementing computational grid environment is how to effectively map tasks onto resources in order to gain high utilization in the highly heterogeneous environment of the grid. In this paper, three algorithms for task mapping based on learning automata are introduced. To show the effectiveness of the proposed algorithms, computer simulations have been conducted. The results of experiments show that the proposed algorithms outperform two best existing mapping algorithms when the heterogeneity of the environment is very high.
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Ghanbari, S., Meybodi, M.R. (2005). Learning Automata Based Algorithms for Mapping of a Class of Independent Tasks over Highly Heterogeneous Grids. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds) Advances in Grid Computing - EGC 2005. EGC 2005. Lecture Notes in Computer Science, vol 3470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508380_69
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DOI: https://doi.org/10.1007/11508380_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26918-2
Online ISBN: 978-3-540-32036-4
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