Task scheduling in grid computing environments

Abstract

A grid computing environment is a parallel and distributed system that brings together various computing capacities to solve large computation problems. Task scheduling is a critical issue for grid computing, which maps tasks onto a parallel and distributed system for achieving good performance in terms of minimizing the overall execution time. This paper presents a genetic algorithm to solve this problem for improving the existing genetic algorithm with two main ideas: a new initialization strategy is introduced to generate the first population of chromosomes and the good characteristics of found solutions are preserved for new generations. Our proposed algorithm is implemented and evaluated using a set of well-known applications in our specific-defined system environment. The experimental results show that the proposed algorithm outperforms other algorithms within several parameter settings.