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Data Grids provide environment for huge, data-intensive applications that produce and process enormous data. Such environments are thus asked to manage data and schedule jobs at the same time. These two important operations have to be tightly coupled to achieve the best results. Replication techniques are widely used to increase the availability of data, improving performance of query latency and load balancing in Data Grid. Also effective resource scheduling is a challenging research issue. In this paper we propose a job scheduling policy, called Parallel Job Scheduling (PJS), and a dynamic data replication strategy, called Threshold-based Dynamic Data Replication (TDDR), to improve the data access efficiencies in a hierarchical Data Grid. The PJS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs …