Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11428862_150 | International Conference on Computational Science (3) |
Keywords | Field | DocType |
non-dedicated cluster configuration,load balancing,cost effective scenario,high adaptable genetic algorithm,different operating system,cluster configuration,common option,classifier system,genetic machine,machine algorithm,genetics,load balance,cost effectiveness,machine learning | Learning machine,Computer science,Workload,Load balancing (computing),Algorithm,Software,Artificial intelligence,Classifier (linguistics),Machine learning,Software development,Genetic algorithm,Distributed computing | Conference |
Volume | ISSN | ISBN |
3516 | 0302-9743 | 3-540-26044-7 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. A. R. Dantas | 1 | 64 | 11.07 |
| A. R. Pinto | 2 | 42 | 11.69 |