Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm

Abstract

Grid computing is the principle in utilizing and sharing large-scale resources of heterogeneous computing systems to solve the complex scientific problem. Such flexible resource request could offer the opportunity to optimize several parameters, such as coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major opportunity is in optimal job scheduling, which Grid nodes need to allocate the resources for each job. This paper proposes and evaluates a new method for job scheduling in heterogeneous computing Systems. Its objectives are to minimize the average waiting time and make-span time. The minimization is proposed by using a multiple objective genetic algorithm (GA), because the job scheduling problem is NP-hard problem. Our model presents the strategies of allocating jobs to different nodes. In this preliminary tests we show how the solution founded may minimize the average waiting time and the make-span time in Grid environment. The benefits of the usage of multiple objective genetic algorithm is improving the performance of the scheduling is discussed. The simulation has been obtained using historical information to study the job scheduling in Grid environment. The experimental results have shown that the scheduling system using the multiple objective genetic algorithms can allocate jobs efficiently and effectively.