Genetic Programming and Evolvable Machines

, Volume 14, Issue 2, pp 127–153 | Cite as

Introducing artificial evolution into peer-to-peer networks with the distributed remodeling framework

Article

Abstract

A peer-to-peer (P2P) network is a complex system whose elements (peer nodes, or simply peers) cooperate to implement scalable distributed services. From a general point of view, the activities of a P2P system are consequences of external inputs coming from the environment, and of the internal feedbacks among nodes. The reaction of a peer to direct or indirect inputs from the environment is dictated by its functional structure, which is usually defined in terms of static rules (protocols) shared among peers. The introduction of artificial evolution mechanisms may improve the efficiency of P2P networks, with respect to resource consumption, while preserving high performance in response to the environmental needs. In this paper, we propose the distributed remodeling framework (DRF), a general approach for the design of efficient environment-driven peer-to-peer networks. As a case study, we consider an ultra-large-scale storage and computing system whose nodes perform lookups for resources provided by other nodes, to cope with task execution requests that cannot be fulfilled locally. Thanks to the DRF, workload modifications trigger reconfigurations at the level of single peers, from which global system adaptation emerges without centralized control.

Keywords

Peer-to-peer network Artificial evolution Complex adaptive system 

References

  1. 1.
    D. Schoder, K. Fischbach, in Peer-to-Peer Paradigm, ed. Proceedings of the 37th Annual Hawaii IEEE International Conference on System Sciences (HICSS’04) (Big Island, Hawaii, USA, 2004)Google Scholar
  2. 2.
    B. Paechter, T. Back, M. Schoenauer, M. Sebag, A. E. Eiben, J. J. Merelo, T.C. Fogarty, in A Distributed Resource Evolutionary Algorithm Machine (DREAM), ed. Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2000) (San Diego, CA, USA, 2000), pp. 951–958Google Scholar
  3. 3.
    M. Amoretti, a survey of peer-to-peer overlay schemes: effectiveness, efficiency and security. Recent Pat. Comput. Sci. 2(3), 195–213 (2009)Google Scholar
  4. 4.
    J. M. Ottino, Engineering complex systems. Nature 427, 399 (2004)Google Scholar
  5. 5.
    M. Mitchell, Complex systems: network thinking. Artif. Intell. 170(18), 1194–1212 (2006)Google Scholar
  6. 6.
    S. Camazine, J.-L. Deneubourg, N.R. Franks, J. Sneyd, G. Theraulaz, E. Bonabeau, Self-Organization in Biological Systems. (Princeton University Press, Princeton, 2001)Google Scholar
  7. 7.
    F. Heylighen, in Self-Organization, Emergence and the Architecture of Complexity, ed. Proceedings of the 1st European Conference on System Science (AFCET, Paris, 1989), pp. 23–32Google Scholar
  8. 8.
    F. Heylighen, in Principles of Systems and Cybernetics: An Evolutionary Perspective, ed. Proceedings of the Cybernetics and Systems (World Science, 1992), pp. 3–10Google Scholar
  9. 9.
    M. Baranger, Chaos, Complexity and Entropy: A Physics Talk for Non-Physicists, 2002, http://necsi.org/projects/baranger/cce.pdf
  10. 10.
    Y. Bar-Yam, Dynamics of Complex Systems, ed. (Addison-Wesley, 1997)Google Scholar
  11. 11.
    F. Heylighen, in The Science of Self-organization and Adaptivity, Knowledge Management, Organizational Intelligence and Learning, and Complexity, ed. by D. Kiel The Encyclopedia of Life Support Systems (EOLSS) (Eolss Publishers, 2001)Google Scholar
  12. 12.
    L. Onana Alima, S. El-Ansary S, P. Brand, S. Haridi, in DKS(N,k,f): A Family of Low Communication, Scalable and Fault-Tolerant Infrastructures for p2p Communications, ed. Proceedings of the 3rd IEEE/ACM Inter Symposium on Cluster Computing and the Grid (CCGRID ’03), (Tokyo, Japan, 2003)Google Scholar
  13. 13.
    I. Stoica, R. Morris, D. Liben-Nowell, D.R. Karger, M.F. Kaashoek, F. Dabek, H. Balakrishnan, Chord: a scalable peer-to-peer lookup protocol for internet applications. IEEE/ACM Trans Netw. 11(1), 17–32 (2003)CrossRefGoogle Scholar
  14. 14.
    L. Steels, Modeling the cultural evolution of language. Phys. Life. Rev. 8, 339–356 (2011)Google Scholar
  15. 15.
    M. Sipper, E. Sanchez, D. Mange, M. Tomassini, A. Perez-Uribe, A. Stauffer, A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems. IEEE Trans. Evol. Comput. 1(1), 83–97 (1997)Google Scholar
  16. 16.
    A. Rowstron, P. Druschel, in Pastry: Scalable, Decentralized Object Location and Routing for Large-Scale Peer-to-Peer Systems, ed. Proceedings of the 3rd IFIP/ACM Int’l Conference on Middleware (ACM, Heildelberg, Germany, 2001), pp. 329–350Google Scholar
  17. 17.
    E. Kalyvianki, I. Pratt, in Building Adaptive Peer-to-Peer Systems, ed. Proceedings of the 4th IEEE Int’l Conference on Peer-to-Peer Computing (P2P’04) (Zurich, Switzerland, 2004), pp. 268–269Google Scholar
  18. 18.
    J. Holland, Adaptation in Natural and Artificial Systems. (The MIT Press, Cambridge, 1992)Google Scholar
  19. 19.
    R. Poli, W.B. Langdon, N.F. McPhee, in A Field Guide to Genetic Programming (Lulu Enterprises, 2008)Google Scholar
  20. 20.
    A. Engelbrecht, Computational Intelligence: An Introduction, 2nd edn. (Wiley, Hoboken, 2007)CrossRefGoogle Scholar
  21. 21.
    T. Hu, J.L. Payne, W. Banzhaf, J.H. Moore, Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming. Genet. Program. Evol. Mach. 13(3), 305–337 (2012)CrossRefGoogle Scholar
  22. 22.
    A. E. Eiben, in Evolutionary Computing and Autonomic Computing: Shared Problems, Shared Solutions? ed. Self-Star Properties in Complex Information Systems, LNCS No. 3460 (Springer, 2005), pp. 36–48Google Scholar
  23. 23.
    W. Wickramasinghe, M. van Steen, A. E. Eiben, in Peer-to-Peer Evolutionary Algorithms with Adaptive Autonomous Selection. ed. Proceedings 9th Annual Conference on Genetic and Evolutionary Computation (GECCO 2007) (ACM, 2007), pp. 1460–1467Google Scholar
  24. 24.
    M. Jelasity, M. Preuβ, M. Van Steen, B. Paechter, in Maintaining Connectivity in a Scalable and Robust Distributed Environment, ed. Proceedings of the 2nd IEEE/ACM Int’l Symposium on Cluster Computing and the Grid (Berlin, Germany, May 2002), pp. 389–394Google Scholar
  25. 25.
    D. Hales, in From Selfish Nodes to Cooperative Networks-Emergent Link-Based Incentives in Peer-to-Peer Networks, ed. Proceedings of the 4th IEEE Int’l Conference on Peer-to-Peer Computing (P2P’04) (Zurich, Switzerland, 2004), pp. 151–158Google Scholar
  26. 26.
    D. Devescovi, E. Di Nitto, D. Dubois, R. Mirandola, in Self-Organization Algorithms for Autonomic Systems in the SelfLet Approach, ed. Proceedings of the 1st International Conference on Autonomic Computing and Communication Systems (Rome, Italy, 2007)Google Scholar
  27. 27.
    G. Tyson, P. Grace, A. Mauthe, S. Kaune, in The Survival of the Fittest: An Evolutionary Approach to Deploying Adaptive Functionality in Peer-to-Peer Systems, ed. Proceedings of the 7th workshop on Reflective and adaptive middleware (ACM, Leuven, Belgium, 2008), pp. 23–28Google Scholar
  28. 28.
    T. Nakano, T. Suda, Self-organizing network services with evolutionary adaptation. IEEE Trans. Neural Netw. 16(5), 1269–1278 (2005)CrossRefGoogle Scholar
  29. 29.
  30. 30.
  31. 31.
    P.T. Eugster, R. Guerraoui, A.-M. Kermarrec, L. Massoulie, From epidemics to distributed computing. IEEE Comput. 37(5), 60–67 (2004)CrossRefGoogle Scholar
  32. 32.
    E. Ahi, M. Caglar, O. Ozkasap, in Stepwise fair-share buffering underneath bio-inspired P2P data dissemination, ed. Proceedings of the 6th IEEE International Symposium on Parallel and Distributed Computing (ISPDC’07) (Hagenberg, Austria, 2007), pp. 177–184Google Scholar
  33. 33.
    The Gnutella Protocol Specification 0.4. http://rfc-gnutella.sourceforge.net/developer/stable/index.html
  34. 34.
    J. L. Laredo, A. E. Eiben, M. Steen, J. J. Merelo, EvAg: a scalable peer-to-peer evolutionary algorithm. Genet. Program. Evol. Mach. Arch. 11(2), 227–246Google Scholar
  35. 35.
    M. Amoretti, M. Agosti, F. Zanichelli, in DEUS: a Discrete Event Universal Simulator, ed. Proceedings of the 2nd ICST/ACM International Conference on Simulation Tools and Techniques (SIMUTools 2009) (ICST, Roma, Italy, 2009)Google Scholar
  36. 36.
    A.-L. Barabasi, R. Albert, Emergence of scaling in random networks. Science. 286(5439):509–512 (1999)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Centro Interdipartimentale SITEIA.PARMAParmaItaly

Personalised recommendations