Evolutionary Market Agents for Resource Allocation in Decentralised Systems

  • Peter R. Lewis
  • Paul Marrow
  • Xin Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


We introduce self-interested evolutionary market agents, which act on behalf of service providers in a large decentralised system, to adaptively price their resources over time. Our agents competitively co-evolve in the live market, driving it towards the Bertrand equilibrium, the non-cooperative Nash equilibrium, at which all sellers charge their reserve price and share the market equally. We demonstrate that this outcome results in even load-balancing between the service providers.

Our contribution in this paper is twofold; the use of on-line competitive co-evolution of self-interested service providers to drive a decentralised market towards equilibrium, and a demonstration that load-balancing behaviour emerges under the assumptions we describe.

Unlike previous studies on this topic, all our agents are entirely self-interested; no cooperation is assumed. This makes our problem a non-trivial and more realistic one.


decentralised systems market-based control co-evolution load-balancing self-interested agents 


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  1. 1.
    Singh, M.P., Huhns, M.N.: Service-Oriented Computing: Semantics, Processes, Agents. John Wiley and Sons, Chichester (2005)Google Scholar
  2. 2.
    He, M., Jennings, N.R., Leung, H.: On agent-mediated electronic commerce. IEEE Transactions on Knowledge and Data Engineering 15(4), 985–1003 (2003)CrossRefGoogle Scholar
  3. 3.
    Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: Research roadmap (2006)Google Scholar
  4. 4.
    Gupta, A., Stahl, D.O., Whinston, A.B.: The economics of network management. Communications of the ACM 42(9), 57–63 (1999)CrossRefGoogle Scholar
  5. 5.
    Clearwater, S.H. (ed.): Market-Based Control: A Paradigm for Distributed Resource Allocation. World Scientific, Singapore (1996)Google Scholar
  6. 6.
    Ardellini, V.C., Olajanni, M.C., Yu, P.S.: Dynamic load balancing on web server systems. IEEE Internet Computing 3(3), 28–39 (1999)CrossRefGoogle Scholar
  7. 7.
    Alfano, R., Caprio, G.D.: Turbo: an autonomous execution environment with scalability and load balancing features. In: Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and its Applications (DIS 2006), pp. 377–382 (2006)Google Scholar
  8. 8.
    Cliff, D., Bruten, J.: Simple bargaining agents for decentralized market-based control. Technical Report HPL-98-17, HP Laboratories, Bristol, UK (1998)Google Scholar
  9. 9.
    Esteva, M., Padget, J.: Auctions without auctioneers: Distributed auction protocols. In: Moukas, A., Ygge, F., Sierra, C. (eds.) Agent Mediated Electronic Commerce II. LNCS, vol. 1788, pp. 20–28. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Kikuchi, H.: (m+1)st-price auction protocol. In: Proceedings of the 5th International Conference on Financial Cryptography, London, UK, pp. 351–363. Springer, Heidelberg (2002)Google Scholar
  11. 11.
    Hausheer, D., Stiller, B.: Peermart: The technology for a distributed auction-based market for peer-to-peer services. In: Proceedings of the IEEE International Conference on Communications, vol. 3, pp. 1583–1587 (2005)Google Scholar
  12. 12.
    Haque, N., Jennings, N.R., Moreau, L.: Scalability and robustness of a network resource allocation system using market-based agents. Netnomics 7(2), 69–96 (2005)CrossRefGoogle Scholar
  13. 13.
    Ramasubramanian, V., Sirer, E.G.: Perils of transitive trust in the domain name system. Technical Report TR2005-1994, Cornell University, Ithaca, New York, USA (2005)Google Scholar
  14. 14.
    Kuwabara, K., Ishida, T., Nishibe, Y., Suda, T.: An equilibratory market-based approach for distributed resource allocation and its applications to communication network control. In: Clearwater, S.H. (ed.) Market-Based Control: A Paradigm for Distributed Resource Allocation, pp. 53–73. World Scientific, Singapore (1996)CrossRefGoogle Scholar
  15. 15.
    Midgley, D.F., Marks, R.E., Cooper, L.G.: Breeding competitive strategies. Management Science 43(3), 257–275 (1997)CrossRefMATHGoogle Scholar
  16. 16.
    Cheung, Y., Bedingfield, S., Huxford, S.: Monitoring and interpreting evolved behaviours in an oligopoly. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 697–701 (1997)Google Scholar
  17. 17.
    Mas-Colell, A., Whinston, M.D., Green, J.R.: Micro-Economic Theory. Oxford University Press, Oxford (1995)Google Scholar
  18. 18.
    Kephart, J.O., Hanson, J.E., Sairamesh, J.: Price-war dynamics in a free-market economy of software agents. In: Proceedings of the Sixth International Conference on Artificial Life, pp. 53–62. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Peter R. Lewis
    • 1
  • Paul Marrow
    • 1
    • 2
  • Xin Yao
    • 1
  1. 1.University of BirminghamBirminghamUK
  2. 2.BT Group plcAdastral ParkUK

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