Predictive Grid Process Scheduling Model in Computational Grid

Abstract

Importance and need of grid process scheduling have been increased in accordance with development of grid computing. In order to distribute and utilize grid processors efficiently, grid computing system needs scheduling policies that manage and schedule grid process. This paper reviews current scheduling policies and proposes an efficient scheduling model which is called the predictive process scheduling model. For efficient scheduling, this paper presents the processing time prediction algorithm to resolve problems of grid scheduling. The predictive process scheduling model predicts processing times of processors, allocates a job to a processor with minimum processing time, and minimizes overall system execution times. For performance evaluation, this paper measures turn-around time, job loss, throughput, and utilization. Empirical results show that the predictive process scheduling model operates with the less 69.5% of turn-around time and the less 76.4% of job loss and improves the more 119.6 % of throughput and the more 117.8% of utilization than those of existing scheduling models such as random scheduling and round-robin scheduling.

This research was supported by the MIC(Ministry of Information and Communication), Korea, under the ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Technology Assessment).