Intelligent Water Drop Algorithm Powered by Tabu Search to Achieve near Optimal Solution for Grid Scheduling

Grid computing is a network of computer resources where every resources are shared, turning a computer network into a powerful super computers. In which, Grid Scheduling is a non linear multi-objective problem. In this paper, intelligent water drop algorithm is hybridized with Tabu Search algorithm to solve scheduling problem in computational grid. The proposed algorithm named EIWD-TS is a meta-heuristic algorithm based on swarm intelligence. The optimization objective of this research is to find the near optimal solution considering multiple objectives namely makespan, slowdown ratio, failure rate and resource utilization of grid scheduling. The result of the proposed model of this paper is tested with PSA (Parameter Sweep Application) dataset and the results are compared with Risky-MinMin (RMM), Preemptive-MinMin (PMM), Particle Swarm Optimization (PSO) and IWD. Experimental evaluation shows that the EIWD-TS algorithm has good convergence property and better in quality of solution than other algorithms reported in recent literature.