Using Classification Techniques to Improve Replica Selection in Data Grid

  • Hai Jin
  • Jin Huang
  • Xia Xie
  • Qin Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4276)


Data grid is developed to facilitate sharing data and resources located in different parts of the world. The major barrier to support fast data access in a data grid is the high latency of wide area networks and the Internet. Data replication is adopted to improve data access performance. When different sites hold replicas, there are significant benefits while selecting the best replica. In this paper, we propose a new replica selection strategy based on classification techniques. In this strategy the replica selection problem is regarded as a classification problem. The data transfer history is utilized to help predicting the best site holding the replica. The adoption of the switch mechanism of replica selection model avoids a waste of time for inaccurate classification results. In this paper, we study and simulate KNN and SVM methods for different file access patterns and compare results with the traditional replica catalog model. The results show that our replica selection model outperforms the traditional one for certain file access requests.


Data Grid Access Pattern Support Vector Machine Method File Index Resource Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allcock, B., Bester, J., Bresnahan, J., Chervenak, A., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D., Tuecke, S.: Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing. In: Proceedings of the 18th IEEE Symposium on Mass Storage Systems and Technologies, pp. 13–28 (2001)Google Scholar
  2. 2.
    Stockinger, H., Samar, A., Holtman, K., Allcock, B., Foster, I., Tierney, B.: File and Object Replication in Data Grids. In: Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing, pp. 76–86 (2001)Google Scholar
  3. 3.
    The GriPhyN Project,
  4. 4.
    Rahman, R., Barker, K., Alhajj, R.: Replica Selection in Grid Environment: A Data-Mining Approach. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 695–700 (2005)Google Scholar
  5. 5.
    Feng, J., Humphrey, M.: Eliminating Replica Selection - Using Multiple Replicas to Accelerate Data Transfer on Grids. In: Proceedings of the Tenth International Conference on Parallel and Distributed Systems, pp. 359–366 (2004)Google Scholar
  6. 6.
    Kavitha, R., Foster, I.: Design and Evaluation of Dynamic Replication Strategies for High Performance Data Grids. In: Proceedings of the International Conference on Computing in High Energy and Nuclear Physics, pp. 106–118 (2001)Google Scholar
  7. 7.
    Vazhkudai, S., Schopf, J., Foster, I.: Predicting the Performance of Wide Area Data Transfers. In: Proceedings of the 16th International Parallel and Distributed Processing Symposium, pp. 34–43 (2002)Google Scholar
  8. 8.
    Rahman, R., Barker, K., Alhajj, R.: Replica Placement in Data Grid: Considering Utility and Risk. In: Proceedings of the International Conference on Information Technology: Coding and Computing, pp. 354–359 (2005)Google Scholar
  9. 9.
    Chervenak, A., Deelman, E., Foster, I., Guy, L., Hoschek, W., Iamnitchi, A., Kesselman, C., Kunszt, P., Ripeanu, M., Schwartzkopf, B., Stockinger, H., Stockinger, K., Tierney, B.: Giggle: A Framework for Constructing Scalable Replica Location Services. In: Proceedings of the ACM/IEEE conference on Supercomputing, pp. 1–17 (2002)Google Scholar
  10. 10.
    Zhao, Y., Hu, Y.: Gress - A Grid Replica Selection Service. In: Proceedings of ISCA 16th International Conference on Parallel and Distributed Computing Systems (2003)Google Scholar
  11. 11.
    Cai, M., Chervenak, A., Frank, M.: A Peer-to-Peer Replica Location Service Based on a Distributed Hash Table. In: Proceedings of the ACM/IEEE Conference on Supercomputing, p. 56 (2004)Google Scholar
  12. 12.
    Aha, D., Kibler, D., Albert, M.: Instance-Based Learning Algorithms. Machine Learning 6(1), 37–66 (1991)Google Scholar
  13. 13.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Inc., San Francisco (2001)Google Scholar
  14. 14.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)Google Scholar
  15. 15.
    Muller, K., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An Introduction to Kernel-based Learning Algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)CrossRefGoogle Scholar
  16. 16.
    Sulistio, A., Poduvaly, G., Buyya, R., Tham, C.: Constructing a Grid Simulation with Differentiated Network Service Using GridSim. In: Proceedings of the 6th International Conference on Internet Computing, pp. 437–444 (2005)Google Scholar
  17. 17.
    The GridSim Project,
  18. 18.
    Bell, W., Cameron, D., Capozza, L., Millar, A., Stockinger, K., Zini, F.: OptorSim - A Grid Simulator for Studying Dynamic Data Replication Strategies. Int. J. of High Performance Computing Applications 17(4), 403–416 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hai Jin
    • 1
  • Jin Huang
    • 1
  • Xia Xie
    • 1
  • Qin Zhang
    • 1
  1. 1.Cluster and Grid Computing LabHuazhong University of Science and TechnologyWuhanChina

Personalised recommendations