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. GridSim 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 %.
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References
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Khanli, L.M., Isazadeh, A., Shishavan, T.N.: PHFS: a dynamic replication method, to decrease access latency in the multi-tier data grid. Future Gener. Comput. Syst. 27(3), 233–244 (2011)
Tim, H., Abramson, D.: The griddles data replication service. In: Proceedings of the 1st International Conference on E-Science and Grid Computing, pp. 271–278 (2005)
Jadaan, O.A., Abdulal, W., Hameed, M.A.: Enhancing data selection using genetic algorithm. In: Proceedings of IEEE International Conference on Computational Intelligence and Communication Networks, pp. 434–439 (2010)
Buyya, R., Murshed, M.: GridSim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. J. Concurrency Comput. Pract. Experience 14, 1175–1220 (2002)
Lin, Y., Chen, Y., Wang, G., Deng, B.: Rigel: a scalable and lightweight replica selection service for replicated distributed file system. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGC, pp. 581–582 (2010)
Naseera, S., Murthy, K.V.M.: Performance evaluation of predictive replica selection using neural network approaches. In: Proceedings of International Conference on Intelligent Agent and Multi-Agent Systems, IAMA 2009, p. 1 (2009)
Rahman, R.M., Baker, K., Alhajj, E.: A predictive technique for replica selection in grid environment. In: Seventh IEEE International Symposium on Cluster Computing and the Grid, pp. 163–170 (2007)
Ishii, R.P., De Mello, R.F.: An online data access prediction and optimization approach for distributed systems. IEEE Trans. Parallel Distrib. Syst. 23(6), 1017–1029 (2012)
Sun, M., Sun, J., Lu, E., Yu, C.: Ant algorithm for file replica selection in data grid. In: Proceedings of First International Conference on Semantics, Knowledge and Grid, p. 64 (2005)
Holland, J.: Adaptation in Natural Artificial Systems. University of Michigan Press, Ann Arbor (1992)
Olivas, E.S., Guerrero, J.D., Martinez-Sober, M., Magdalena-Benedito, J.R., Serrano Lopez, A.J.: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques. IGI Global, Hershey (2010). doi:10.4018/978-1-60566-766-9
Kirkpatrick, S., Gelatt, C., Vecchi, M.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculation by fast computing machines. J. Chem. Phys. 21(1087), 1087–1091 (1953)
Yoshikawa, M., Yamauchi, H., Terai, H.: Hybrid architecture of genetic algorithm and simulated annealing. Eng. Lett. 16(3), EL_16_3_11 (2012)
Chervenak, A., Schuler, R., Ripeanu, M., Amer, M.A., Bharathi, S., Foster, I., Kesselman, C.: The globus replica location service: design and experience. IEEE Trans. Parallel Distrib. Syst. 20(9), 1260–1272 (2009)
Gandomkar, M., Vakilian, M., Ehsan, M.: A combination of genetic algorithm and simulated annealing for optimal DG allocation in distribution networks. In: Proceedings of Canadian Conference on Electrical and Computer Engineering, pp. 645–648 (2005)
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The authors would like to thank the Management & Principal of Sri Ramakrishna Engineering College, and the Head of the Department of Computer Science and Engineering, for their support in completing this work.
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Kingsy Grace, R., Manimegalai, R. (2016). HGASA: An Efficient Hybrid Technique for Optimizing Data Access in Dynamic Data Grid. In: Bjørner, N., Prasad, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2016. Lecture Notes in Computer Science(), vol 9581. Springer, Cham. https://doi.org/10.1007/978-3-319-28034-9_17
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DOI: https://doi.org/10.1007/978-3-319-28034-9_17
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