A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid

Abstract

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 waiting in queue, file locations, and disk read speed of storage drive at data sources. The main idea of TDDR strategy is using a threshold value to determine if the requested replica needs to be copied to the node. The TDDR determines this threshold dynamically based on data request arrival rates and available storage capacities. Then, in order to overcome the problem of limited storage space in each node, we design an efficient replica replacement strategy, which is developed as a two stages process. First, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. Results from the simulation show that our proposed algorithms have better performance in comparison with other algorithms in terms of Mean Job Time, Number of Intercommunications, Number of Replications, Computing Resource Usage, and Effective Network Usage.