Grid Scheduling using Improved Particle Swarm Optimization with Digital Pheromones

Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid computing. Grid environment is a dynamic, heterogenous and unpredictable computing system which shares different services among various users. Because of heterogenous and dynamic nature of the grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be designed to address this research problem. This paper represents the technique of particle swarm optimization with digital pheromones which hasimproved solution characteristics. The main objective of the proposed algorithm is to find a solution that generates an optimal schedule which minimizes the flowtime in grid environment.Simulations have been carried out using GridSim for the improved PSO algorithm. Experimental studies illustrates that the proposed methodology, Improved PSO with digital pheromones is more efficient and surpasses those of PSO algorithms for the grid scheduling problem.