An Enhanced Adaptive Scoring Job Scheduling Algorithm with Replication Strategy in Grid Environment

Grid computing is a form of distributed computing that involves coordinating and sharing data storage and network resource. The goal of grid job scheduling is to achieve high system throughput and match the job to the appropriate available computing resource. The complexity of scheduling problem increases with heterogeneous nature of grid and is highly difficult to schedule effectively. Existing algorithm does not adapt to the dynamic grid environment. In order to utilize the power of grid completely and to assign job to the resource dynamically an efficient algorithm called Adaptive Scoring Job Scheduling (ASJS) was introduced. However the bandwidth and storage capacity occupied by data intensive and computational intensive job is high and each time the user have to specify whether the job is computational intensive or data intensive. . Due to this problem the jobs are not completed in time. To provide a solution to that problem Enhanced Adaptive Scoring Job scheduling algorithm is introduced. The jobs are identified whether it is data intensive or computational intensive and based on that the jobs are scheduled. The jobs are allocated by computing Cluster Score (CS). The jobs that are submitted by the user is divided into sub tasks and replicated. By using this strategy the job occupies lower storage capacity and bandwidth. Due to the dynamic nature of grid environment, each time the status of the resources changes and each time the Cluster Score (CS) is computed and the jobs are replicated and allocated to the most appropriate resources.