AN EXPERIMENTAL STUDY ON GENETIC ALGORITHMS FOR RESOURCE ALLOCATION ON GRID SYSTEMS
Fatos Xhafa, Leonard Barolli, Arjan Durresi · Journal of Interconnection Networks · 2007
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.