Paper Info
Title
A Load Balancing Approach Based on a Genetic Machine Learning Algorithm
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. Our approach is characterized by an on time assignment scheme using a classifier system. 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 (Cluster of Workstation) and Now (Network of Workstation) 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
10.1109/HPCS.2005.8
HPCS
Keywords
Field
DocType
non-dedicated cluster configuration,load balancing,time assignment scheme,high adaptable genetic algorithm,proposed scheme,cluster configuration,common option,genetic machine learning algorithm,genetic machine,classifier system,machine algorithm,genetics,master slave,operating systems,cost effectiveness,genetic algorithms,resource allocation,learning artificial intelligence,machine learning,clustering algorithms,load balance,software testing,parallel processing,workstations
Learning machine,Computer science,Parallel processing,Software,Artificial intelligence,Classifier (linguistics),Genetic algorithm,Distributed computing,Load balancing (computing),Parallel computing,Algorithm,Workstation,Resource allocation,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-2343-9
2
0.50
References 
Authors
8
2
Name
Order
Citations
PageRank
M. A. R. Dantas16411.07
A. R. Pinto24211.69