Advertisement

Agents’ Bidding Strategies in a Combinatorial Auction

  • Tim Stockheim
  • Michael Schwind
  • Oleg Gujo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4196)

Abstract

This paper presents an agent-based simulation environment for task scheduling in a grid. Resource allocation is performed by an iterative combinatorial auction in which proxy-bidding agents try to acquire their desired resource allocation profiles. To achieve an efficient bidding process, the auctioneer provides the bidding agents with approximated shadow prices from a linear programming formulation. The objective of this paper is to identify optimal bidding strategies in multi-agent settings with respect to varying preferences in terms of resource quantity and waiting time until bid acceptance. On the basis of a utility function we characterize two types of agents: a quantity maximizing agent with a low preference for fast bid acceptance and an impatient bidding agent with a high valuation of fast allocation of the requested resources. Bidding strategies with varying initial bid pricing and different price increments are evaluated. Quantity maximizing agents should submit initial bids with low and slowly increasing prices, whereas impatient agents should start slightly below market prices and avoid ‘overbidding’.

Keywords

Resource Agent Bidding Strategy Combinatorial Auction Distribute Computer System Bidding Behavior 
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.
    Rassenti, J.S., Smith, V.L., Bulfin, R.L.: A combinatorial auction mechanism for airport time slot allocation. The Bell Journal of Economics 13(2), 402–417 (1982)CrossRefGoogle Scholar
  2. 2.
    Milgrom, P.: Putting Auction Theory to Work. Cambridge University Press, Cambridge (2004)Google Scholar
  3. 3.
    Buyya, R., Stockinger, H., Giddy, J., Abramson, D.: Economic models for management of resources in peer-to-peer and grid computing. In: Proceedings of the SPIE International Conference on Commercial Applications for High-Performance Computing, Denver, USA (2001)Google Scholar
  4. 4.
    Foster, I., Jennings, N.R., Kesselman, C.: Brain meets brawn: Why grid and agents need each other. In: Proceedings of the 3rd Int. Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004), New York, NY , pp. 8–15 (2004)Google Scholar
  5. 5.
    Chun, B.N., Buonadonna, P., AuYoung, A., Ng, C., Parkes, D.C., Shneiderman, J., Snoeren, A.C., Vahdat, A.: Mirage: A microeconomic resource allocation system for sensornet testbeds. In: Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors (EmNetS-II), Sidney, Australia (2004)Google Scholar
  6. 6.
    AuYoung, A., Chun, B.N., Snoeren, A.C., Vahdat, A.: Resource allocation in federated distributed computing infrastructures. In: Proceedings of the 1st Workshop on Operating System and Architectural Support for the On-demand IT InfraStructure, San Francisco, USA (2004)Google Scholar
  7. 7.
    Ng, C., Parkes, D.C., Seltzer, M.: Virtual worlds: Fast and strategyproof auctions for dynamic resource allocation. In: Proceedings of the third ACM Conference on Electronic Commerce (EC-2003), San Diego, CA, pp. 238–239. ACM, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Schnizler, B., Neumann, D., Veit, D., Weinhardt, C.: A multiattribute combinatorial exchange for trading grid resources. In: Proceedings of the 12th Research Symposium on Emerging Electronic Markets (RSEEM). Amsterdam, Netherlands (2005)Google Scholar
  9. 9.
    Schwind, M., Stockheim, T., Rothlauf, F.: Optimization heuristics for the combinatorial auction problem. In: Proceedings of the Congress on Evolutionary Computation CEC 2003, pp. 1588–1595 (2003)Google Scholar
  10. 10.
    Parkes, D.C., Ungar, L.H.: Iterative combinatorial auctions: Theory and practice. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI 2000), pp. 74–81 (2000)Google Scholar
  11. 11.
    Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions: Optimal and approximate approaches. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence 1999 (IJCAI 1999), Stockholm, Sweden, pp. 548–553 (1999)Google Scholar
  12. 12.
    Kwasnica, A.M., Ledyard, J., Porter, D., DeMartini, C.: A new and improved design for multi-objective iterative auctions. Management Science 51(3), 419–434 (2005)CrossRefGoogle Scholar
  13. 13.
    Bjørndal, M., Jørnsten, K.: An analysis of a combinatorial auction. Technical Report 2001-11, Department of Finance and Management Science, Norwegian School of Economics and Business Administration, Bergen, Norway (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tim Stockheim
    • 1
  • Michael Schwind
    • 2
  • Oleg Gujo
    • 2
  1. 1.Business Information Systems and Operations ResearchTechnical University KaiserslauternKaiserslauternGermany
  2. 2.Institute of Information SystemsJohann Wolfgang Goethe UniversityFrankfurtGermany

Personalised recommendations