Resource management in grid computing using enhanced ant colony optimization

Efficient resource management is needed to overcome stagnation problem in grid computing. Scheduling of jobs is one of the activities of resource management. This activity is complicated due to the distributed and heterogeneous nature of the resources. An enhanced ant colony optimization algorithm for job and resource scheduling is proposed in this paper. The proposed algorithm focuses on global pheromone update and the use of grid resource table to store all information about jobs, resources and pheromone value. Simulation approach has been used to test the performance of the algorithm. The credibility of the proposed algorithm is compared with other approaches and results produced showed that the algorithm can balance the load of the resources