A genetic machine learning algorithm for load balancing in cluster configurations

Authors:
M. A. R. Dantas;A. R. Pinto
Affiliations:
Department of Informatics and Statistics, Federal University of Santa Catarina, Florianopolis, Brazil;Department of Informatics and Statistics, Federal University of Santa Catarina, Florianopolis, Brazil
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Year:
2005

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Abstract

Cluster configurations are a cost effective scenarios which are becoming common options to enhance several classes of applications in many organizations. In this article, we present a research work to enhance the load balancing, on dedicated and non-dedicated cluster configurations, based on a genetic machine learning algorithm. Classifier systems are learning machine algorithms, based on high adaptable genetic algorithms. We developed a software package which was designed to test the proposed scheme in a master-slave Cow and Now environment. Experimental results, from two different operating systems, indicate the enhanced capability of our load balancing approach to adapt in cluster configurations.