AN EXPERIMENTAL STUDY ON GENETIC ALGORITHMS FOR RESOURCE ALLOCATION ON GRID SYSTEMS
-
- FATOS XHAFA
- Polytechnic University of Catalonia, Department of Languages and Informatics Systems, C/Jordi Girona 1-3, 08034 Barcelona, Spain
-
- LEONARD BAROLLI
- Department of Information and Communication Engineering, Fukuoka Institute of Technology (FIT), 3-30-1 Wajiro-higashi Higashi-ku, Fukuoka 811-0295, Japan
-
- ARJAN DURRESI
- Department of Computer and Information Science, Purdue University School of Science, Indianapolis 723 W. Michigan St., SL 280M Indianapolis, IN 46202, USA
説明
<jats:p> Computational Grid (CG) is an emerging paradigm in which geographically distributed resources are logically unified as a computational unit. A challenging problem in such systems is the allocation of jobs to resources that minimizes both makespan and flowtime parameters. In this paper, we present an experimental study on Genetic Algorithms (GAs) for scheduling independents jobs to Grid resources based on two replacement strategies: Steady-State GA (SSGA) and Struggle GA (SGA). SSGA distinguishes for its accentuated convergence of the population that rapidly reaches good solutions though it is soon stagnated. The SGA is based on struggle replacement and adaptively maintains diverse population, reducing thus convergence rapidity. The experimental results, based on a benchmark simulation model, showed that SGA outperforms SSGA for moderate size instances. On the other hand, the time needed by the SGA to reach makespan values obtained by the SSGA rapidly increases as more jobs and machines are added to the Grid. Thus, for larger size instances, SGA is not able to improve the results of the SSGA. Finally, we also report and analyze flowtime values for the considered benchmark. </jats:p>
収録刊行物
-
- Journal of Interconnection Networks
-
Journal of Interconnection Networks 08 (04), 427-443, 2007-12
World Scientific Pub Co Pte Lt