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Cluster Computing

, Volume 22, Supplement 5, pp 11119–11128 | Cite as

An efficient and improved multi-objective optimized replication management with dynamic and cost aware strategies in cloud computing data center

  • E. Bijolin EdwinEmail author
  • P. Umamaheswari
  • M. Roshni Thanka
Article

Abstract

Recent technology which focus in cloud computing with ICT based service providers for better challenges in the field of online services. This provides the computing world as an utility based scenario of sharing the given resources of different data centeres with enormous opportunities. Though replicas of a data file has increased, the performance and availability of data also increases. In this paper the different aspects on dynamic, cost-aware with the data replication method through optimization is proposed which identifies the less amount of information of data replication which is required to make sure that the data availability increases with the increase in the replication process. The multi objective optimization strategy for the cost of replication from higher-cost data centers to lower-cost data centers being implemented by the concept of Improved knapsack algorithm and considering the availability and load balancing in the replication process. Data can be managed effectively by file replication, which reduces effectively the file service time and access latency, which increases the file availability and the system get improved through load balancing. An efficient and improved multi-objective optimized replication management (EIMORM) can solve these optimal solutions by balancing among these optimization objectives. Some experiments clearly shows that the EIMORM is much more energy efficiency and the performance in the replication system of the Hadoop Distributed File System (HDFS) with the multi-objective evolutionary stated algorithm with the performance, load balancing in cloud storage clustors. Now considering the energy efficiency also considering the bandwidth of system. Hence the results taken through simulator can support the energy efficiency as the guidance in the areas of data replication.

Keywords

Cloud data center Data replication cost Load balancing Availability 

References

  1. 1.
    Chang, R.S., Chang, H.P.: A dynamic data replication strategy using access weights in data grids. J. Supercomput. 45(3), 277–295 (2008)CrossRefGoogle Scholar
  2. 2.
    Choi, S.C., Youn, H.Y.: Dynamic hybrid replication effectively combining tree and grid topology. J. Supercomput. 59, 1289–1311 (2012)CrossRefGoogle Scholar
  3. 3.
    Dogan, A.: A study on performance of dynamic file replication algorithms for real-time file access in data grids. Future Gener. Comput. Syst. 25(8), 829–839 (2009)CrossRefGoogle Scholar
  4. 4.
    Ghemawat, S., Gobioff, H., Leung, S.: The Google file system. In: ACM Symposium on Operating Systems Principles, vol. 37, pp. 29–43 (2003)Google Scholar
  5. 5.
    Gill, N.K., Singh, S.: A dynamic, cost-aware, optimised data replication strategy for heterogeneous cloud data centers. Future Gener. Comput. Syst. 65, 10–32 (2016). SpringerGoogle Scholar
  6. 6.
    Kusic, D., Kephart, J.O., Hanson, J.E., Kandasamy, N., Jiang, G.: Power and performance management of virtualized computing environments via lookahead control. Clust. Comput. 12(1), 1–15 (2009)CrossRefGoogle Scholar
  7. 7.
    Lamehamedi, H., Shentu, Z., Szymanski, B.: Simulation of dynamic data replication strategies in data grids. In: Proceedings of 12th Heterogeneous Computing Workshop (HCW2003) Nice, France, April 2003, IEEE Computer Science Press, Los Alamitos, CA, (2003)Google Scholar
  8. 8.
    Lei, M., Vrbsky, S.V., Hong, X.: An on-line replication strategy to increase availability in data grids. Future Gener. Comput. Syst. 24(2), 85–98 (2008)CrossRefGoogle Scholar
  9. 9.
    Li, W.H., Yang, Y., Yuan, D.: A novel cost-effective dynamic data replication strategy for reliability in cloud data center. In: IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, (2011)Google Scholar
  10. 10.
    Lin, Y.F., Wu, J.J., Liu, P.: A list-based strategy for optimal replica placement in data grid systems. In: 37th International Conference on Parallel Processing, pp. 198–205 (2008)Google Scholar
  11. 11.
    Long, S.Q., Zhao, Y.L., Chen, W.: MORM: a multi-objective optimized replication management strategy for cloud storage cluster. J. Syst. Archit. 60, 234–244 (2014)CrossRefGoogle Scholar
  12. 12.
    Nguyen, T., Cutway, A., Shi, W.: Differentiated replication strategy in data centers, In: 7th IFIP International Conference on Network and Parallel Computing, Springer, Berlin Heidelberg, pp. 277–288 (2010)Google Scholar
  13. 13.
    Nukarapu, D.T., Tang, B., Wang, L.Q., Lu, S.Y.: Data replication in data intensive scientific applications with performance guarantee. IEEE Trans. Parallel Distrib. Syst. 22(8), 1299–1306 (2011)CrossRefGoogle Scholar
  14. 14.
    Pérez, J.M., García-Carballeira, F., Carretero, J., Calderón, A., Fernández, J.: Branchreplication scheme: a new model for data replication in large scale data grids. Future Gener. Comput. Syst. 26, 12–20 (2010)CrossRefGoogle Scholar
  15. 15.
    Phan, D.H., Suzuki, J., Carroll, R.: Evolutionary multi-objective optimization for green clouds. In: GECCO’12, 7–11 July Philadelphia, Pennsylvania, USA (2012)Google Scholar
  16. 16.
    Qu, Y., Xiong, N.: RFH: A resilient, fault-tolerant and high-efficient replication algorithm for distributed cloud storage. in: 41st International Conference on Parallel Processing, pp. 520–529 (2012)Google Scholar
  17. 17.
    Rahman, R.M., Barker, K., Alhajj, R.: Replica placement in data grid: considering utility and risk. In: International Conference on Information Technology: Coding and Computing, pp. 354–359 (2005)Google Scholar
  18. 18.
    Rahman, R.M., Barker, K., Alhajj, R.: Replica placement design with static optimality and dynamic maintainability. Sixth IEEE International Symposium on Cluster Computing and the Grid, CCGRID 06, 434–437 (2006)Google Scholar
  19. 19.
    Ranganathan, K., Foster, I.: Identifying dynamic replication strategies for a high-performance data grid. In: International Workshop on Grid Computing, Springer, London, pp. 75–86 (2001)Google Scholar
  20. 20.
    Sankar, S.P., Hariharan, N., Varatharajan, R.: A novel method to increase the coupling efficiency of laser to single mode fibre. Wirel. Pers. Commun. 87, 419–430 (2016)CrossRefGoogle Scholar
  21. 21.
    Shorfuzzaman, M., Graham, P., Eskicioglu, R.; Popularity-driven dynamic replica placement in hierarchical data grids. In: Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies, IEEE, pp. 524–531 (2008)Google Scholar
  22. 22.
    Shvachko, K., Hairong, K., Radia, S., Chansler, R.: The Hadoop distributed file system. In: Proceedings of 26th Symposium on Mass Storage Systems and Technologies, Incline Village, NV, USA, May 3–7, pp. 1–10 (2010)Google Scholar
  23. 23.
    Sun, D.-W., Chang, G.-R., Gao, S., Jin, L.-Z., Wang, X.-W.: Modeling a dynamic data replication strategy to increase system availability in cloud computing environments. J. Comput. Sci. Technol. 27, 256–272 (2012)CrossRefGoogle Scholar
  24. 24.
    Sun, D.-W., Chang, G.-R., Miao, C., Jin, L.-Z., Wang, X.-W.: Analyzing modelling and evaluating dynamic adaptive fault tolerance strategies in cloud computing environments. J. Supercomput. 66, 193–228 (2013)CrossRefGoogle Scholar
  25. 25.
    Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-0977-2
  26. 26.
    Vijayakumar, K., Arun, C.: Automated risk identification using NLP in cloud based development environments. J. Ambient Intell. Hum. Comput. (2017).  https://doi.org/10.1007/s12652-017-0503-7
  27. 27.
    Vijayakumar, K., Arun, C.: Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Clust. Comput. (2017).  https://doi.org/10.1007/s10586-017-1176-x
  28. 28.
    Wei, Q., Veeravalli, B., Gong, B., Zeng, L., Feng, D.: CDRM: a cost-effective dynamic replication management scheme for cloud storage cluster. In: Proceedings of the 2010 IEEE International Conference on Cluster Computing, Heraklion, Crete, Greece, September 20–24, pp. 188–196 (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Sciences TechnologyKarunya UniversityCoimbatoreIndia
  2. 2.Department of Computer Science & Engineering, College of EngineeringAnna UniversityChennaiIndia

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