Towards Adaptive, Resilient and Self-organizing Peer-to-Peer Systems

  • Alberto Montresor
  • Hein Meling
  • Özalp Babaoğlu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2376)


Peer-to-peer (P2P) systems are characterized by decentralized control, large scale and extreme dynamism of their operating environment. Developing applications that can cope with these characteristics requires a paradigm shift, placing adaptation, resilience and self-organization as primary concerns. In this note, we argue that complex adaptive systems (CAS), which have been used to explain certain biological, social and economical phenomena, can be the basis of a programming paradigm for P2P applications. In order to pursue this idea, we are developing Anthill, a framework to support the design, implementation and evaluation of P2P applications based on ideas such as multi-agent and evolutionary programming borrowed from CAS.


Multiagent System Complex Adaptive System Runtime Environment Programming Paradigm Group Object 
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.


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  1. 1.
    O. Babaoğlu, H. Meling, and A. Montresor. Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems. In Proc. of the 22th Int. Conf. on Distributed Computing Systems, Wien, Austria, July 2002.Google Scholar
  2. 2.
    O. Babaoğlu, H. Meling, and A. Montresor. Implementing a Load-Balancing Algorithm in Anthill. Technical Report UBLCS-02-9, Dept. of Computer Science, University of Bologna, Apr. 2002.Google Scholar
  3. 3.
    S. R. et al. A Scalable Content-Addressable Network. In Proc. of the ACM SIG-COMM’01, San Diego, CA, 2001.Google Scholar
  4. 4.
    Project JXTA.
  5. 5.
    N. Minar, R. Burkhart, C. Langton, and M. Askenazi. The Swarm Simulation System, A Toolkit for Building Multi-Agent Simulations. Technical report, Swarm Development Group, June 1996.
  6. 6.
    M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Apr. 1998.Google Scholar
  7. 7.
    M. Resnick. Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. MIT Press, 1994.Google Scholar
  8. 8.
    A. Rowstron and P. Druschel. Pastry: Scalable, Decentralized Object Location and Routing for Large-Scale Peer-to-Peer Systems. In Proc. of the 18th International Conference on Distributed Systems Platforms, Heidelberg, Germany, Nov. 2001.Google Scholar
  9. 9.
  10. 10.
    G. Weiss. Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, 1999.Google Scholar
  11. 11.
    B. Y. Zhao, J. Kubiatowicz, and A. D. Joseph. Tapestry: An Infrastructure for Fault-Tolerant Wide-Area Location and Routing. Technical Report UCB/CSD-01-1141, U.C. Berkeley, Apr. 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alberto Montresor
    • 1
  • Hein Meling
    • 2
  • Özalp Babaoğlu
    • 1
  1. 1.Department of Computer ScienceUniversity of BolognaBolognaItaly
  2. 2.Department of TelematicsNorwegian University of Science and TechnologyTrondheimNorway

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