Job Scheduling and Data Replication in Hierarchical Data Grid

Data Grid environment is a geographically distributed that deal with date-intensive application in scientific and enterprise computing. In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth, especially when hierarchy of bandwidth appears in network. Effective job scheduling can reduce data transfer time by considering hierarchy of bandwidth and also dispatching a job to where the needed data are present. Additionally, replication of data from primary repositories to other locations can be an important optimization step to reduce the frequency of remote data access. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy, called SS (Scheduling strategy), and a dynamic data replication strategy, called DRS (Distributed Replication Strategy), to improve the data access efficiencies in a cluster grid. We study our approach and evaluate it through simulation. The results show that combination of SS and DRS has improved 17% over other combinations.