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Analysis of Load Balancing Techniques in Grid

  • R. Venkatesan
  • M. Blessy Rathna Solomi
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

Grid environment is the collection of independent systems which provide integrated computing facility. In a Grid infrastructure, some systems may be idle, while others are heavily loaded. This leads to an imbalance in load which results in under-utilization of resources, reduced throughput, and high response time. Several load balancing strategies are proposed to avoid the load imbalance. In this paper, the various load balancing models are discussed. The four load balancing models explored in this paper are graph-based, tree-based, agent-based and learning-based. Several load balancing techniques are described and discussed under appropriate category.

Keywords

Grid computing Load balancing Machine learning Tree-based model Graph partitioning Software agents 

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References

  1. 1.
    Yagoubi, B., Slimani, Y.: Dynamic Load Balancing Strategy for Grid Computing. World Academy of Science, Engineering and Technology (2006)Google Scholar
  2. 2.
    Al-Azzoni, I., Down, D.G.: Decentralized Load Balancing for Heterogeneous Grids. In: Proc. of the 2009 Computation World: Future Computing, Service Computation, Cognitive Adaptive, Content, Patterns, pp. 545–550 (2009)Google Scholar
  3. 3.
    Van Driessche, R., Roose, D.: An Improved Spectral Bisection Algorithm and its Application to Dynamic Load Balancing. Parallel Computing 21, 29–48 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Catalyurek, U.V., Boman, E.G., Devine, K.D., Bozdag, D., Heaphy, R.T., Reisen, L.A.: A Repartitioning Hypergraph Model for Dynamic Load Balancing. Journal of Parallel Distributed Computing 69, 711–724 (2009)CrossRefGoogle Scholar
  5. 5.
    Meyerhenke, H., Gehweiler, J.: On Dynamic Graph Partitioning and Graph Clustering using Diffusion. In: Dagstuhl Seminar Proceedings (2010)Google Scholar
  6. 6.
    Yagoubi, B., Slimani, Y.: Load Balancing Strategy in Grid Environment. Journal of Information Technology and Applications 1(4), 285–296 (2007)Google Scholar
  7. 7.
    Mitchell, W.F.: A Refinement-tree Based Partitioning Method for Dynamic Load Balancing with Adaptively Refined Grids. Journal of Parallel and Distributed Computing 67(4), 417–429 (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    van Nieuwpoort, R.V., Kielmann, T., Bal, H.E.: Efficient Load Balancing for Wide-Area Divide-and-Conquer Applications. In: Proc. PPoPP 2001, Snowbird, UT (2001)Google Scholar
  9. 9.
    Cao, J., Spooner, D.P., Jarvis, S.A., Saini, S., Nudd, G.R.: Agent-Based Grid Load Balancing Using Performance-Driven Task Scheduling. In: Proc. of 17th IEEE International Parallel and Distributed Processing Symposium (2003)Google Scholar
  10. 10.
    Montresor, A., Meling, H., Babaoglu, O.: Messor: Load-Balancing through a Swarm of Autonomous Agents. In: Proc. of the 1st International Conference on Agents and Peer-to-Peer Computing (July 2002)Google Scholar
  11. 11.
    Cao, J.: Self-Organizing Agents for Grid Load Balancing. In: Proceedings of 5th IEEE/ACM International Workshop on Grid Computing, pp. 388–395 (November 2004)Google Scholar
  12. 12.
    Zhu, X., Goldberg, A.B.: Introduction to Semi-Supervised Learning. In: Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers (2009)Google Scholar
  13. 13.
    Meraji, S., Zhang, W., Tropper, C.: A Multi-State Q-Learning Approach for the Dynamic Load Balancing of Time Warp. In: IEEE Workshop on Principles (2010)Google Scholar
  14. 14.
    Revar, A., Andhariya, M., Sutariya, D.: Load Balancing in Grid Environment using Machine Learning – Innovative Approach. International Journal of Computer Applications 8(10) (October 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • R. Venkatesan
    • 1
  • M. Blessy Rathna Solomi
    • 2
  1. 1.Department of Information TechnologyKarunya UniversityIndia
  2. 2.Department of Computer Science and EngineeringKarunya UniversityIndia

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