Smart Replica Selection for Data Grids Using Rough Set Approximations (RSDG)

Abstract

The best replica selection problem is one of the important aspects of data management strategy of data grid infrastructure. Recently, rough set theory has emerged as a powerful tool for problems that require making optimal choice amongst a large enumerated set of options. In this paper, we propose a new replica selection strategy using a grey-based rough set approach. Here first the rough set theory is used to nominate a number of replicas, (alternatives of ideal replicas) by lower approximation of rough set theory. Next, linguistic variables are used to represent the attributes values of the resources (files) in rough set decision table to get a precise selection cause, some attribute values like security and availability need to be decided by linguistic variables (grey numbers) since the replica mangers' judgments on attribute often cannot be estimated by the exact numerical values (integer values). The best replica site is decided by grey relational analysis based on a grey number. Our results show an improved performance, compared to the previous work in this area.