Design and Performance Analysis of File Replication Strategy on Distributed File System Using GridSim

  • Nirmal Singh
  • Sarbjeet Singh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)


Distributed Computing Systems like Peer-to-Peer, Web and Cloud are becoming popular day by day and are being used in a wide variety of data intensive applications. Data replication is an important concept which increases the availability and reliability of data in these systems. This paper presents the design and performance analysis of simulated implementation of data replication strategy on a distributed file system using GridSim toolkit. The parameters taken for the performance analysis are aggregate bandwidth, successful execution rate and system byte effective rate. The results indicate that the distributed file system making use of data replication strategy performs better, with respect to the parameters mentioned above, than the distributed file system which is not making use of data replication strategy. The integration of data replication scheme with distributed file system can greatly improve the availability and reliability of data.


Distributed File System Replication Strategy Web Cloud Peerto- Peer GridSim 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Goel, S., Buyya, R.: Data Replication Strategies in Wide Area Distributed Systems,
  2. 2.
    Sun, D., Chang, G., Gao, S., Jin, L., Wang, X.: Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments. Journal of Computer Science and Technology 27(2), 256–272 (2012)CrossRefGoogle Scholar
  3. 3.
    Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop Distributed File System. In: Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), May 03-07, pp. 1–10 (2010)Google Scholar
  4. 4.
    Buyya, R., Murshed, M.: GridSim: A toolkit for the modeling and simulation of distributed resource management and scheduling for Grid Computing. Concurrency and Computation: Practice and Experience 14(13-15) (2002)Google Scholar
  5. 5.
    Borthakur, D.: HDFS Architecture, The Apache Software Foundation,
  6. 6.
    Rahman, R.M., Barker, K., Alhajj, R.: Replica placement design with static optimality and dynamic maintainability. In: Proceedings of the 6th IEEE International Symposium on Cluster Computing and the Grid, pp. 434–437 (2006)Google Scholar
  7. 7.
    Dogan, A.: A study on performance of dynamic file replication algorithms for real-time file access in data grids. Future Generation Computer Systems 25(8), 829–839 (2009)CrossRefGoogle Scholar
  8. 8.
    Lei, M., Vrbsky, S.V., Hong, X.: An on-line replication strategy to increase availability in data grids. Future Generation Computer Systems 24(2), 85–98 (2008)CrossRefMATHGoogle Scholar
  9. 9.
    Litke, A., Skoutas, D., Tserpes, K., Varvarigou, T.: Efficient task replication and management for adaptive fault tolerance in mobile grid environments. Future Generation Computer Systems 23(2), 163–178 (2007)CrossRefGoogle Scholar
  10. 10.
    Dobber, M., Van der Mei, R., Koole, G.: Dynamic load balancing and job replication in a global-scale grid environment: A comparison. IEEE Transactions on Parallel and Distributed Systems 20(2), 207–218 (2009)CrossRefGoogle Scholar
  11. 11.
    Yuan, D., Yang, Y., Liu, X., Chen, J.: A data placement strategy in scientific cloud workflows. Future Generation Computer Systems 26(8), 1200–1214 (2010)CrossRefGoogle Scholar
  12. 12.
    Rood, B., Lewis, M.J.: Grid resource availability prediction-based scheduling and task replication. Journal of Grid Computing 7(4), 479–500 (2009)CrossRefGoogle Scholar
  13. 13.
    Latip, R., Othman, M., Abdullah, A., Ibrahim, H., Sulaiman, M.N.: Quorum-based data replication in grid environment. International Journal of Computational Intelligence Systems 2(4), 386–397 (2009)Google Scholar
  14. 14.
    Wei, Q., Veeravalli, B., Gong, B., Zeng, L., Feng, D.: CDRM: A cost-effiective dynamic replication management scheme for cloud storage cluster. In: Proceedings of the 2010 IEEE International Conference on Cluster Computing, Heraklion, pp. 188–196 (2010)Google Scholar
  15. 15.
    Bonvin, N., Papaioannou, T.G., Aberer, K.: A self-organized, fault-tolerant and scalable replication scheme for cloud storage. In: Proceedings of the 1st ACM Symposium on Cloud Computing, Indianapolis, pp. 205–216 (2010)Google Scholar
  16. 16.
    Peter, B., Ishai, M., Mosharaf, C., Pradeepkumar, M., David, A.M., Ion, S.: Surviving failures in bandwidth-constrained datacenters. In: Proceedings of the ACM SIGCOMM 2012 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, August 13-17 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science and EngineeringUIET, Panjab UniversityChandigarhIndia

Personalised recommendations