RPLB: A Replica Placement Algorithm in Data Grid with Load Balancing

RPLB: A Replica Placement Algorithm in Data Grid with Load Balancing

Kingsy Rajaretnam, Manimegalai Rajkumar, and Ranjith Venkatesan

Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, India

Abstract: Data grid is an infrastructure built based on internet which facilitates sharing and management of geographically distributed data resources. Data sharing in data grids is enhanced through dynamic data replication methodologies to reduce access latencies and bandwidth consumption. Replica placement is to create and place duplicate copies of the most needed file in beneficial locations in the data grid network. To reduce the make span i.e., total job execution time, storage consumption and effective network usage in data grids, a new method for replica placement is introduced. In this proposed method, all the nodes in the same region are grouped together and replica is placed in the highest degree and highest frequency node in the region. The node to place replica should be load balanced in terms of access and storage. The proposed dynamic Replica Placement algorithm with Load Balancing (RPLB) is tested using OptorSim simulator, which is developed by European Data Grid Projects. In this paper, two variants of the proposed algorithm RPLB, namely RPLBfrequency and RPLBdegree are also presented. The comparative analysis of all the three proposed algorithms is also presented in this paper. A Graphical User Interface (GUI) is designed as an interface to OptorSim to get all values for grid configuration file, job configuration file and parameters configuration file. Simulation results reveal that the performance of the proposed methodology is better in terms of makespan, storage consumption and replication count when compared to the existing algorithms in the literature.

Keywords: Replica placement, load balancing, effective network usage, data grid, data replication.

 

Received June 17, 2013; accepted April 28, 2014

Read 1926 times Last modified on Wednesday, 06 March 2019 03:34
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