Update Propagation Technique for Data Grid

  • Mohammed Radi
  • Ali Mamat
  • M. Mat Deris
  • Hamidah Ibrahim
  • Subramaniam Shamala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4707)


Data replication is a well known technique used to reduce accesses latency, improve availability, and performance in a distributed computing environment. An asynchronous replication is a commonly agreed solution for the consistency of replicas problem. Update propagation using a classical propagation schema called the radial method suffers from high overhead of the master replica while line method suffers from high delay time. This paper presents a new asynchronous replication protocol called Update Propagation Grid (UPG) which especially for a wide area distributed Data Grid. Updates reach other replicas using a propagation technique based on nodes organized into a logical structure network that enables the technique to scale well for thousands of replicas. Restructuring operation is provided to build and reconfigure the UPG dynamically. An analytical model is developed; communication cost, average load balance, and average delay time have been analyzed. The technique achieves load balancing and minimizes the delay for file replication in Data Grid.


Relay Node Data Grid Responsible Site Average Delay Time Grid Site 
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.
    Chervenak, I., Foster, C., Kesselman, C., Salisbury, C., Tuecke, S.: The data grid: Towards architecture for the distributed management and analysis of large scientific datasets. Journal of Network and Computer Applications 23, 187–200 (2001)CrossRefGoogle Scholar
  2. 2.
    Allcock, J., Bester, J., Bresnahan, A.L., Chervenak, I., Foster, C., Kesselman, S., Meder, V., Nefedova, D., Quesnal, Tuecke, S.: Data management and transfer in high performance computational grid environments. Parallel Computing Journal 28(3), 749–771 (2002)CrossRefGoogle Scholar
  3. 3.
    Gray, J., et al.: The Dangers of Replication and a Solution. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 173–182 (1996)Google Scholar
  4. 4.
    Chervenak, A., et al.: Giggle: A Framework for Constructing Scalable Replica Location Services. In: Proc. Of SC 2002 (2002)Google Scholar
  5. 5.
    Samar, H S.: Grid Data Management Pilot (GDMP): A Tool for Wide Area Replication. Applied Informatics (AI) (2001)Google Scholar
  6. 6.
    Cameron, J., Casey, L., Guy, P., Kunszt, S., Lemaitre, G., McCance, H., Stockinger, K., Stockinger, G., Andronico, W., Bell, I., Ben-Akiva, D., Bosio, R., Chytracek, A., Domenici, F., Donno, W., Hoschek, E., Laure, L., Lucio, P., Millar, L., Salconi, B., Segal, M.S.: Replica Management Services in the European Data Grid ProjectUK. e-Science All Hands Conference, Nottingham (September 2004)Google Scholar
  7. 7.
    Bosio, Bell, W.H., Cameron, D., McCance, G., Millar, A.P., et al.: EU Data Grid Data Management Services, UK e-Science All Hands Conference, Nottingham (September 2003) Google Scholar
  8. 8.
    Lamehamedi, H., Szymanski, B.: Decentralized Data Management Framework for Data Grids. Future eneration Computer Systems 23(1), 109–115 (2007)CrossRefGoogle Scholar
  9. 9.
    Guy, L., Kunszt, P., Laure, E., Stockinger, H., Stockinger, K.: Replica Management in Data Grids. Technical Report, GGF5 Working Draft, Edinburgh Scotland (July 2002)Google Scholar
  10. 10.
    Dullmann, W.H., Jaen-Martinez, J., Segal, B., Stockinger, H., Stockinger, K., Samar, A.: Models for Replica Synchronisation and Consistency in a Data Grid. hpdc. In: 10th IEEE International Symposium on High Performance Distributed Computing (HPDC-10 ’01), San Francisco, CA, USA, August 7-9, 2001, p. 67. IEEE Computer Society Press, Los Alamitos (2001)CrossRefGoogle Scholar
  11. 11.
    Venugopal, S., Buyya, R., Ramamohanarao, K.: A Taxonomy of Data Grids for Distributed Data Sharing. Management and Processing. In: ACM Computing Surveys, vol. 38(1), pp. 1–53. ACM Press, New York, USA (2006)Google Scholar
  12. 12.
    Domenici, A., Donno, F., Pucciani, G., Stockinger, H., Stockinger, K.: Replica Consistency in a Data Grid, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 534(1-2), 24–28 (2004)CrossRefGoogle Scholar
  13. 13.
    No, J., Park, C., Park, S.: Data Replication Techniques for Data-Intensive Applications. In: International Conference on Computational Science. vol. 4, pp. 1063–1070 (2006)Google Scholar
  14. 14.
    Chang, R., Chang, J.: Adaptable Replica Consistency Service for Data Grids. In: Proceedings of the Third International Conference on Information Technology: New Generations (ITNG’06) (2006)Google Scholar
  15. 15.
    Sun, Y., Xu, Z.: Grid Replication Coherence Protocol. ipdps. In: 18th International Parallel and Distributed Processing Symposium (IPDPS’04) - Workshop. vol. 13, p. 232b (2004)Google Scholar
  16. 16.
    Domenici, A., Donno, F., Pucciani, G., Stockinger, H.: Relaxed Data Consistency with CONStanza. In: CCGRID 2006, pp. 425–429 Google Scholar
  17. 17.
    Wang, Z., et al.: An efficient update propagation algorithm for P2P systems, Comput. Commun. (2006), doi:10.1016/j.comcom.2006.11.005.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mohammed Radi
    • 1
  • Ali Mamat
    • 1
  • M. Mat Deris
    • 2
  • Hamidah Ibrahim
    • 1
  • Subramaniam Shamala
    • 1
  1. 1.Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, SelangorMalaysia
  2. 2.Faculty of Information Technology and Multimedia University of Tun Hussein Onn, P.O. Box 101, 86400 Parit Raja, Batu Pahat, JohorMalaysia

Personalised recommendations