Optimalisation of a job scheduler in the grid environment by using fuzzy C-mean

Abstract

Grid computing is the principle in utilizing and sharing large-scale resources to solve complex scientific problems. Under this principle, Grid environment has problems in flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. However, the major problems include optimal job scheduling, and which grid nodes allocate the resources for each job. This paper proposes the model for optimizing jobs scheduling in Grid environment. The model presents the results of the simulation of the Grid environment of jobs allocation to different nodes. We develop the results of job characteristics to three classifications depending on jobs run time in machines, which have been obtained using the optimization of jobs scheduling. The results prove the model by using Fuzzy c-mean clustering technique for predicting the characterization of jobs and optimization of jobs scheduling in Grid environment. This prediction and optimization engine will provide Jobs scheduling base upon historical information. This paper presents the need for such a prediction and optimization engine that discusses the approach for history-based prediction and optimization. Simulation runs demonstrate that our algorithm leads to better results than the traditional algorithms for scheduling policies used in Grid environment.