Comparative Analysis of Job Scheduling for Grid Environment

Abstract

Grid computing is a continuous growing technology that alleviates the executions of large-scale resource intensive applications on geographically distributed computing resources. For a computational grid environment, there are number of scheduling policies available to address the scheduling and load balancing problem. Scheduling techniques applied in grid systems are primarily based on the concept of queuing systems, and deals with the allocation of job to computing node. The scheduler, that schedules the incoming job can be based on global vs. local i.e. what information will be used to make a load balancing decision, centralized vs. de-centralized i.e. where load balancing decisions are made, and static vs. dynamic i.e. when the distribution of load is made. The primary objective of all load balancing algorithm is minimization of the makespan value, maximum load balanced and to gain more desirable performance. In this paper we  present the various load balancing strategies of job scheduling for grid computing environment. We also analyze the efficiency and limitations of the various approaches.