Parallel Asynchronous Particle Swarm Optimization For Job Scheduling In Grid Environment

Abstract

Grid computing is a new, large and powerful self managing virtual computer out of large collection of connected heterogeneous systems sharing various combination of resources and it is the combination of computer resources from multiple administrative domains applied to achieve a goal, it is used to solve scientific, technical or business problem that requires a great number of processing cycles and needs large amounts of data. One primary issue associated with the efficient utilization of heterogeneous resources in a grid environment is task scheduling. Task Scheduling is an important issue of current implementation of grid computing. The demand for scheduling is to achieve high performance computing. If large number of tasks is computed on the geographically distributed resources, a reasonable scheduling algorithm must be adopted in order to get the minimum completion time. Typically, it is difficult to find an optimal resource allocation for specific job that minimizes the schedule length of jobs. So the scheduling problem is defined as NP-complete problem and it is not trivial. Heuristic algorithms are used to solve the task scheduling problem in the grid environment and may provide high performance or high throughput computing or both. In this paper, a parallel asynchronous particle swarm optimization algorithm is proposed for job scheduling. The proposed scheduler allocates the best suitable resources to each task with minimal makespan and execution time. The experimental results are compared which shows that the algorithm produces better results when compared with the existing ant colony algorithm.