Adaptive QOS Guided Ant Algorithm for Data Intensive Grid Scheduling
Grid computing is rapidly growing in the distributed heterogeneous environment for utilizing and sharing large scale resources to solve complex scientific problems. Scheduling is the most critical task to achieve high performance in both computation and data grids. To utilize the grid efficiently, a good job scheduling algorithm is required. In the communication environment, the performance of accessing distributed and huge amount of data depends on the availability of network bandwidth. The proposed algorithm is based on the general adaptive scheduling heuristic and employs a QoS guided component which emphasizes more on communication capability. The algorithm fully utilizes high quality resources and dynamically reduces the total job execution time when the numbers of jobs and congestion rates are varied.