Scheduling of scientific workflows using Discrete PSO Algorithm for Grids

Grid computing systems utilize distributive owned and geographically dispersed resources for providing a wide variety of services for various applications. It is possible that the job submission for the resource request by resource consumers can be large owing to wide area distribution of grid. Key services such as resource discovery, monitoring and scheduling are inherently more complicated in a grid environment. In this paper, we present a method of applying Particle Swarm Optimization (PSO) algorithm to the problem of Grid workload scheduling. Swapping mechanism is used to improve the performance of the Discrete Particle Swarm Optimization algorithm. Furthermore, we compare the scheduling algorithm with other scheduling mechanisms, namely, FCFS and EDF algorithm. The computational results reveal that the proposed Discrete PSO approach is more effective.