Advertisement

Cost-Optimal Job Allocation Schemes for Bandwidth-Constrained Distributed Computing Systems

  • Preetam Ghosh
  • Kalyan Basu
  • Sajal K. Das
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3769)

Abstract

This paper formulates the job allocation problem in distributed systems with bandwidth-constrained nodes. The bandwidth limitations of the nodes play an important role in the design of cost-optimal job allocation schemes. In this paper, we present a pricing strategy for generalized distributed systems by formulating an incomplete information bargaining game on two variables (price and percentage of bandwidth allocated for distributed computing jobs at each node). Next, we present a cost-optimal job allocation scheme for single class jobs that involve the communication delay and hence link bandwidth. We show that our algorithms are comparable to existing job allocation algorithms in minimizing the expected system response time.

Keywords

Price Strategy Communication Delay Bargaining Game Distribute Processing Symposium System Response Time 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic Models for Resource Management and Scheduling in Grid Computing. The Journal of Concurrency and Computation: Practice and Experience, CCPE (May 2002)Google Scholar
  2. 2.
    Grosu, D., Chronopoulos, A.T., Leung, M.Y.: Load Balancing in Distributed Systems: An Approach Using Cooperative Games. In: Proc. of the 16th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2002), Ft Lauderdale, Florida, pp. 501–510 (2002)Google Scholar
  3. 3.
    Tantawi, A.N., Towsley, D.: Optimal static load balancing in distributed computer systems. Journal of the ACM 32(2), 373-382, 445–465 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Winoto, P., McCalla, G., Vassileva, J.: An Extended Alternating-Offers Bargaining Protocol for Automated Negotiation in Multi-Agent Systems. In: Meersman, R., Tari, Z., et al. (eds.) CoopIS 2002, DOA 2002, and ODBASE 2002. LNCS, vol. 2519, pp. 179–194. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Ghosh, P., Roy, N., Basu, K., Das, S.K.: A Game Theory based Pricing Strategy for Job Allocation in Mobile Grids. In: Proc. of the 18th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2004), Santa Fe, New Mexico (2004)Google Scholar
  6. 6.
    Tang, X., Chanson, S.T.: Optimizing static job scheduling in a network of heterogeneous computers. In: Proc. of the Intl. Conf. on Parallel Processing, pp. 373–382 (August 2000)Google Scholar
  7. 7.
    Ross, K.W., Yao, D.D.: Optimal load balancing and scheduling in a distributed computer system. Journal of the ACM 38(3), 676–690 (1991)zbMATHCrossRefGoogle Scholar
  8. 8.
    Kameda, H., Li, J., Kim, C., Zhang, Y.: Optimal Load Balancing in Distributed Computer Systems. Springer, Heidelberg (1997)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Preetam Ghosh
    • 1
  • Kalyan Basu
    • 1
  • Sajal K. Das
    • 1
  1. 1.Center for Research in Wireless Mobility and Networking (CReWMaN)The University of Texas at Arlington 

Personalised recommendations