A Multi-agent Approach to Resource Sharing Optimization in User Networks

  • J. C. Burguillo-Rial
  • E. Costa-Montenegro
  • F. J. González-Castaño
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


In this paper, we evaluate the feasibility of multiagent control of resources to be shared in user networks. A user network is totally controlled by the users, both at application and transport level. One of the possible applications in these networks is peer-to-peer (P2P) file exchange sharing the "external" access to the Internet (set of links between the user network and the Internet). If a node cannot serve its demand with its own external link, it requests help from another node via the high-bandwidth internal user network. We model user nodes as agents to simulate and to evaluate a new agent-based distributed control scheme. The simulation results in this paper confirm that it is possible to improve resource sharing in user networks using agents that take decisions autonomously, from local information, and check that file exchange services offered to neighbour nodes do not surpass appropriate credit limits.


Busy Period Basic Node User Network Quiet Period Selfish Node 
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.


  1. 1.
    Schweitzer, F., Zimmermann, J., Muhlenbein, H.: Coordination of decisions in a spatial agent model. Physica A 303(1-2), 189–216 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Axelrod, R.: The evolution of Cooperation. Basic Books, New York (1984)Google Scholar
  3. 3.
    Hoffmann, R.: Twenty years on: The evolution of cooperation revisited. Journal of Artificial Societies and Social Simulation 3(2) (2000)Google Scholar
  4. 4.
    IEEE 802.11. Online Available at the web site,
  5. 5.
    Hubaux, J.P., Gross, T., Boudec, J.Y.L., Vetterli, M.: Towards self-organized mobile ad-hoc networks: the terminodes project. IEEE Commun. Mag. 1, 118–124 (2001)CrossRefGoogle Scholar
  6. 6.
    Kazaa news (2004), Online Available at the web site,
  7. 7.
    Binmore, K.: Game Theory. McGraw Hill, New York (1994)Google Scholar
  8. 8.
    Kazaa participation ratio (2005), Online Available at the web site
  9. 9.
    The official BitTorrent page,
  10. 10.
    Schaerf, A., Shoham, Y., Tennenholtz, M.: Adaptive Load Balancing: A Study in Multi-Agent Learning. Journal of Artificial Intelligence Research 2, 475–500 (1995)zbMATHGoogle Scholar
  11. 11.
    Kulbak, Y., Bickson, D.: The eMule Protocol Specification (2005), Online Available at the web site
  12. 12.
    Gu, B., Jarvenpaa, S.: Are Contributions to P2P Technical Forums Private or Public Goods? - An Empirical Investigation. In: 1st Workshop on Economics of Peer-to-Peer Systems (2003)Google Scholar
  13. 13.
    Hardin, G.: The Tragedy of the Commons. Science 162, 1243–1248 (1968)CrossRefGoogle Scholar
  14. 14.
    Adar, E., Huberman, B.A.: Free riding on Gnutella (2002)Google Scholar
  15. 15.
    Feldman, M., Lai, K., Stoica, I., Chuang, J.: Robust Incentive Techniques for Peer-to-Peer Networks. In: ACM E-Commerce Conference (EC 2004) (2004)Google Scholar
  16. 16.
    Castro, M., Druschel, P., Ganesh, A., Rowstron, A., Wallach, D.S.: Security for Structured P2P Overlay Networks. In: Proceedings of Multimedia Computing and Networking (2002)Google Scholar
  17. 17.
    García-Palomares, U.M., González-Castaño, F.J., Burguillo-Rial, J.C.: A Combined Global & Local Search (CGLS) Approach to Global Optimization. Journal of Global Optimization (JOGO) (article in Press, beginning of 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. C. Burguillo-Rial
    • 1
  • E. Costa-Montenegro
    • 1
  • F. J. González-Castaño
    • 1
  1. 1.ETSET.University of VigoVigoSpain

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