HGASA: An efficient hybrid technique for optimizing data access in dynamic data grid

Abstract

Grid computing uses computers that are distributed across various geographical locations in order to provide enormous computing power and massive storage. Scientific applications produce large quantity of sharable data which requires efficient handling and management. Replica selection is one of the data management techniques in grid computing and is used for selecting data from large volumes of distributed data. Replica selection is an interesting data access problem in data grid. Genetic Algorithms (GA) and Simulated Annealing (SA) are two popularly used evolutionary algorithms which are different in nature. In this paper, a hybrid approach which combines Genetic Algorithm with Simulated Annealing, namely, HGASA, is proposed to solve replica selection problem in data grid. The proposed algorithm, HGASA, considers security, availability of file, load balance and response time to improve the performance of the grid. Grid Sim simulator is used for evaluating the performance of the proposed algorithm. The results show that the proposed algorithm, HGASA, outperforms Genetic Algorithms (GA) by 9% and Simulated Annealing (SA) by 21% and Ant Colony Optimization (ACO) by 50%.