Evolutionary Market Agents for Resource Allocation in Decentralised Systems
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.
Keywordsdecentralised systems market-based control co-evolution load-balancing self-interested agents
Unable to display preview. Download preview PDF.
- 1.Singh, M.P., Huhns, M.N.: Service-Oriented Computing: Semantics, Processes, Agents. John Wiley and Sons, Chichester (2005)Google Scholar
- 3.Papazoglou, M.P., Traverso, P., Dustdar, S., Leymann, F.: Service-oriented computing: Research roadmap (2006)Google Scholar
- 5.Clearwater, S.H. (ed.): Market-Based Control: A Paradigm for Distributed Resource Allocation. World Scientific, Singapore (1996)Google Scholar
- 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.Cliff, D., Bruten, J.: Simple bargaining agents for decentralized market-based control. Technical Report HPL-98-17, HP Laboratories, Bristol, UK (1998)Google Scholar
- 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.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
- 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.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
- 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.Mas-Colell, A., Whinston, M.D., Green, J.R.: Micro-Economic Theory. Oxford University Press, Oxford (1995)Google Scholar
- 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