Estimation Based Load Balancing Algorithm for Data-Intensive Heterogeneous Grid Environments

  • Ruchir Shah
  • Bharadwaj Veeravalli
  • Manoj Misra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4297)


Grid computing holds the great promise to effectively share geographically distributed heterogeneous resources to solve large-scale complex scientific problems. One of the distinct characteristics of the Grid system is resource heterogeneity. The effective use of the Grid requires an approach to manage the heterogeneity of the involved resources that can include computers, data, network, etc. In this paper, we proposed a de-centralized and adaptive load balancing algorithm for heterogeneous Grid environment. Our algorithm estimates different system parameters (such as job arrival rate, CPU processing rate, load at processor) and effectively performs load balancing by considering all necessary affecting criteria. Simulation results demonstrate that our algorithm outperforms conventional approaches in the event of heterogeneous environment and when communication overhead is significant.


Grid systems Heterogeneous environment Load balancing Average response time Communication overhead Migration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Murata, Y., Takizawa, H., Inaba, T., Kobayashi, H.: A distributed and cooperative load balancing mechanism for large-scale P2P systems. In: International Symposium on Applications and Internet Workshops (SAINT 2006), January 2006, pp. 126–129 (2006)Google Scholar
  2. 2.
    Shan, H., Oliker, L., Biswas, R., Smith, W.: Scheduling in heterogeneous grid environments: The effects of data migration. In: Proceedings of ADCOM 2004: International Conference on Advanced Computing and Communication, Ahmedabad, Gujarat, India (December 2004)Google Scholar
  3. 3.
    Shan, H., Oliker, L., Biswas, R.: Job superscheduler architecture and performance in computational grid environments. In: ACM/IEEE Conference on Supercomputing (November 2003)Google Scholar
  4. 4.
    Subramani, V., Kettimuthu, R., Srinivasan, S., Sadayappan, P.: Distributed job scheduling on computational grid using multiple simultaneous requests. In: Proceedings of 11th IEEE Symposium on High Performance Distributed Computing (HPDC 2002) (July 2002)Google Scholar
  5. 5.
    Arora, M., Das, S.K., Biswas, R.: A De-centralized scheduling and load balancing algorithm for heterogeneous grid environments. In: Proceedings of the International Conference on Parallel Processing Workshops (ICPPW 2002), pp. 499–505 (2002)Google Scholar
  6. 6.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: Enabling scalable virtual organizations. International Journal of High Performance Computing Applications 15(3), 200–222 (2001)CrossRefGoogle Scholar
  7. 7.
    Anand, L., Ghose, D., Mani, V.: ELISA: An Estimated Load Information Scheduling Algorithm for distributed computing systems. International Journal on Computers and Mathematics with Applications 37(8), 57–85 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a future computing infrastructure. Morgan Kaufmann Publishers, USA (1999)Google Scholar
  9. 9.
    Martin, R., Vahdat, A., Culler, D., Anderson, T.: Effects of communication latency, overhead and bandwidth in a cluster architecture. In: Proceedings 24th Annual International Symposium on Computer Architecture, June 1997, pp. 85–97 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ruchir Shah
    • 1
  • Bharadwaj Veeravalli
    • 2
  • Manoj Misra
    • 1
  1. 1.Department of Electronics and Computer EngineeringIndian Institute of TechnologyRoorkeeIndia
  2. 2.Computer Networks and Distributed Systems Laboratory, Department of Electrical and Computer EngineeringNational University of SingaporeSingapore

Personalised recommendations