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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Dantas, M. A. R., & Pinto, A. R. (2005). A genetic machine learning algorithm for load balancing in cluster configurations. In Lecture Notes in Computer Science (Vol. 3516, pp. 971–974). Springer Verlag. https://doi.org/10.1007/11428862_150
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