Agent Based Priority Heuristic for Job Scheduling on Computational Grids

A grid is an infrastructure for resource sharing. At present, many scientific applications require high computing power in processing, which can only be achieved by using the computational grid. For the selection and allocation of grid resources to current and future applications, grid job scheduling is playing a very vital role for computational grids. They constitute the building blocks for making grids available to the society. The efficient and effective scheduling policies, when assigning different jobs to specific resources, are very important for a grid to process high computing intensive applications. This paper presents an agent based job scheduling algorithm for efficient and effective execution of user jobs. This paper also includes the comparative performance analysis of our proposed job scheduling algorithm along with other well known job scheduling algorithms considering the quality of service parameters like waiting time, turnaround time, response time, total completion time, bounded slowdown time and stretch time. We also conducted the QoS based evaluation of the scheduling algorithms on an experimental computational grid using real workload traces. Experimental evaluation confirmed that the proposed grid scheduling algorithms posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs.