Enhanced ant colony optimization for grid load balancing

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are assigned to the same resources which lead to the resources having high workland and longer processing time. This paper proposes an Enhanced Ant Colony Optimization (EACO) algorithm for dynamic schedulling and load balancing in a grid computer system. The proposed algorithm can determine the best resource to process a job in order to balance the load among resources in a grid environment. Three new mechanisms are used in organizing the work of an ant colony which are initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The initial pheromone value is calculated based on the estimated transmission time and execution time of a given job. Global pheromone update is performed to reduce the pheromone value of resources. A simulation environment was developed to test the performance of the algorithm against another ant based algorithm in terms of resource utilization and to determine how different values of evaporation rate resource utilization. From the experiments, the best evaporation rate value will be determined for a specific number of jobs and resources