A static jobs scheduling for independent jobs in Grid Environment by using Fuzzy C-Mean and Genetic algorithms

Abstract

The concept of Grid computing is becoming a more important for the high performance computing world. 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. Specifically, we investigate the static job scheduling algorithm for independent jobs. In this paper we propose and evaluate experimentally a static scheduling for independent jobs that rely on determining job characteristics at runtime and jobs allocate to resources. We present a static job scheduling algorithm by using Fuzzy C-Mean and Genetic algorithms. Our model presents the strategies of allocating jobs to different nodes, which we have developed the model by using Fuzzy C-Mean algorithm for prediction the characteristics of jobs that run in Grid environment and Genetic algorithm for jobs allocated to large scale sharing of resources. The performance of our model in a static job scheduling have researchers will be discussed. Our model has shown that the scheduling system will allocate jobs efficiently and effectively.