Optimizing distributed data access in grid environments by using artificial intelligence techniques

Authors:
Rodrigo F. de Mello;Jose Augusto Andrade Filho;Evgueni Dodonov;Renato Porfirio Ishii;Laurence T. Yang
Affiliations:
University of São Paulo, ICMC - Department of Computer Science, São Carlos, SP, Brazil;University of São Paulo, ICMC - Department of Computer Science, São Carlos, SP, Brazil;University of São Paulo, ICMC - Department of Computer Science, São Carlos, SP, Brazil;Federal University of Mato Grosso do Sul, Department of Computer and Statistics, MS, Brazil;St. Francis Xavier University, Antigonish, NS, Canada
Venue:
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
Year:
2007

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Abstract

This work evaluates two artificial intelligence techniques for file distribution in Grid environments. These techniques are used to access data on independent servers in parallel, in order to improve the performance and maximize the throughput rate. In this work, genetic algorithms and Hopfield neural networks are the techniques used to solve the problem. Both techniques are evaluated for efficiency and performance. Experiments were conduced in environments composed of 32, 256 and 1024 distributed nodes. The results allow to confirm the decreasing in the file access time and that Hopfield neural network offered the best performance, being possible to be applied on Grid environments.