Hierarchical Replication Strategy for Adaptive Scoring Job Scheduling in Grid Computing

Grid technology, which together a number of personal computer clusters with high speed networks, can reach the same computing power as a supercomputer does, also with a minimum cost. However, heterogeneous system is called as grid. Scheduling independent tasks on grid is more difficult. In order to utilize the power of grid completely, we demand an efficient job scheduling algorithm to execute jobs to resources in a grid. The Data Grid provides massive aggregated computing resources and distributed storage space to deal with data-intensive applications. Due to the limitation of available resources in the grid as well as construction of huge volumes of data, efficient usage of the Grid resources becomes a significant challenge. In previous work develop the Adaptive Scoring Job Scheduling algorithm (ASJS) for the grid environment. In that algorithm is not suitable for replication technique. Data replication is a key optimization technique for reducing access latency and managing large data by storing data in a wise manner. Effective scheduling in the Grid can reduce the amount of data transferred between nodes by submitting a job to a node where most of the requested data files are available. The proposed system uses dynamic data replication strategy, called Effective Hierarchical Replication (EHR) that improves file access time. This strategy is an enhanced version of the Dynamic Hierarchical Replication strategy. It uses an economic model for file deletion when there is not enough space for the replica node. So our proposed system finds the replicate detection of files with different cluster structure representation of the input files. We combine the replica strategy with ASJS algorithm for efficiently decrease the completion time of submitted jobs, which may consist of computing-intensive jobs and data-intensive jobs.