An adaptive genetic algorithm for the grid scheduling problem

Grid computing is a service that shares computational power and data storage capacity over the Internet. The goal of grid tasks scheduling is to achieve high system throughput and to match the application need with the available computing resources. Aiming at the evolution characteristics of genetic algorithm (GA), an adaptive genetic algorithm (AGA) is presented in this paper. The AGA is used to solve the grid scheduling problem. It can keep all the advantages of the standard GA, such as implementation simplicity, low computational burden, and few control parameters, etc. A set of experiments show that the algorithm is stable and presents low variability. The preliminary results obtained in this research are auspicious. We analyze the laboratory results to show that the modified algorithm has better characteristics than standard GA and Max-Min algorithm when it was used in task scheduling.