Evolutionary P2P Networking for Realizing Adaptive Networks

  • Kei Ohnishi
  • Mario Köppen
  • Kaori Yoshida
  • Yuji Oie
Part of the Studies in Computational Intelligence book series (SCI, volume 422)

Abstract

Recently, a peer-to-peer (P2P) network model became very popular. This model is different from a conventional client-server network model. While a conventional client-server network model explicitly distinguishes nodes providing services (servers) from nodes receiving services (clients), a P2P network model does not assign fixed roles to nodes. One type of P2P networks are unstructured P2P networks that do not include any mechanism to manage data locations and which consist only of nodes that communicate with each other through direct connections. A direct connection between two nodes in unstructured P2P networks is represented by a logical network link, and therefore, a structure formed by logical links and nodes, that is, a P2P network topology, can be formed freely. Thus, unstructured P2P networks are flexible. However, they require some mechanisms to realize quick, accurate, and reliable searches. A free-form P2P network topology can be a control object for realizing such searches. One method that can adaptively modify a free-form topology of a running unstructured P2P network for quick, accurate, and reliable searches is an evolutionary algorithms (EA) inspired by biological genetics and evolution. Although EA is not the only way for adjusting the entire network topology of a running P2P network, EA would be suitable for this modification because EA can hold several solution candidates (i.e. future options for network topologies) simultaneously in a running P2P network. An evolutionary P2P networking technique (EP2P) and a parallel evolutionary P2P networking technique (P-EP2P) are both a fusion technique of an EA and an unstructured P2P network, and optimize the manner in which the nodes belong to different P2P network topologies in realtime. We will introduce EP2P and P-EP2P and show simulation results that validate both techniques.

Keywords

Network Topology Crossover Operator Node Group Evaluation Scenario Uniform Crossover 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press (1996)Google Scholar
  6. 6.
    Cheng, S.T.: Topological optimization of a reliable communication network. IEEE Transactions on Reliability 47(3), 225–233 (1998)CrossRefGoogle Scholar
  7. 7.
    Clarke, I., Snadberg, O., Wiley, B., Hong, T.W.: Freenet: A distributed anonymous information storage and retrieval system. In: Proceedings of Workshop on Design Issue in Anonymity and Unobservability. International Computer Science Institute, Berkeley, CA, USA (2000)Google Scholar
  8. 8.
    Cohen, E., Shenker, S.: Replication strategies in unstructured peer-to-peer networks. In: Proceedings of the ACM SIGCOMM 2002 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, Pittsburgh, PA, USA (2002)Google Scholar
  9. 9.
    Das, T., Nandi, S., Deutsch, A., Ganguly, N.: Bio-inspired Search and Distributed Memory Formation on Power-Law Networks. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 154–164. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Ganguly, N., Canright, G., Deutsch, A.: Design of an Efficient Search Algorithm for P2P Networks Using Concepts from Natural Immune Systems. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 491–500. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Imai, P., Tschudin, C.: Practical online network stack evolution. In: SASO 2010 Workshop on Self-Adaptive Networking (2010)Google Scholar
  12. 12.
    Knowles, J., Corne, D.: A new evolutionary approach to the degree-constrained minimum spanning tree problem. IEEE Transactions on Evolutionary Computation 4, 125–134 (2000)CrossRefGoogle Scholar
  13. 13.
    Koo, S.G.M., Lee, C.S.G., Kannan, K.: A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks. In: Proc. of the 13th Intl. Conference on Computer Communications and Networks (ICCCN 2004), pp. 469–474 (2004)Google Scholar
  14. 14.
    Rong, L.: Multimedia resource replication strategy for a pervasive peer-to-peer environment. Journal of Computers 3(4), 9–15 (2008)CrossRefGoogle Scholar
  15. 15.
    Lua, E.K., Crowcroft, J., Pias, M., Sharma, R., Lim, S.: A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys & Tutorials 7(2), 72–93 (2005)CrossRefGoogle Scholar
  16. 16.
    Lv, Q., Cao, P., Cohen, E., Li, K., Shenker, S.: Search and replication in unstructured peer-to-peer networks. In: Proceedings of the 16th International Conference on Supercomputing, New York, USA, pp. 84–95 (2002)Google Scholar
  17. 17.
    Merz, P., Wolf, S.: Evolutionary Local Search for Designing Peer-to-Peer Overlay Topologies Based on Minimum Routing Cost Spanning Trees. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 272–281. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Munetomo, M., Takai, Y., Sato, Y.: An adaptive network routing algorithm employing path genetic operators. In: Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 643–649 (1997)Google Scholar
  19. 19.
    Neri, F., Kotilainen, N., Vapa, M.: A memetic-neural approach to discover resources in P2P networks. SCI, vol. 153, pp. 113–129 (2008)Google Scholar
  20. 20.
    Ohnishi, K., Oie, Y.: Evolutionary P2P networking that fuses evolutionary computation and P2P networking together. IEICE Transactions on Communications E93-B(2), 317–328 (2010)CrossRefGoogle Scholar
  21. 21.
    Ohnishi, K., Oie, Y.: Parallel evolutionary P2P networking for realizing adaptive large-scale networks. In: The Second Workshop on Heuristic Methods for the Design, Deployment, and Reliability of Networks and Network Applications (HEUNET 2011) (SAINT 2011 Workshop) (2011)Google Scholar
  22. 22.
    Pournaras, E., Exarchakos, G., Antonopoulos, N.: Load-driven neighbourhood reconfiguration of gnutella overlay. Computer Communications 31(13), 3030–3039 (2008)CrossRefGoogle Scholar
  23. 23.
    Srivatsa, M., Gedik, B., Liu, L.: Large scaling unstructured peer-to-peer networks with heterogeneity-aware topology and routing. IEEE Transactions on Parallel and Distributed Systems 17(11), 1277–1293 (2006)CrossRefGoogle Scholar
  24. 24.
    Thampi, S.M., Chandra, S.K.: Review of replication schemes for unstructured P2P networks. In: Proceedings of IEEE International Advance Computing Conference IEEE (IACC 2009), Patiala, India, pp. 794–800 (2009)Google Scholar
  25. 25.
    Walkowiak, K., Przewoniczek, M.: Modeling and optimization of survivable P2P multicasting. Computer Communications 34(12), 1410–1424 (2011)CrossRefGoogle Scholar
  26. 26.
    Zhou, G., Gen, M.: A note on genetic algorithm approach to the degree-constrained spanning tree problems. International Journal of Networks 30(2), 91–95 (1997)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kei Ohnishi
    • 1
  • Mario Köppen
    • 1
  • Kaori Yoshida
    • 1
  • Yuji Oie
    • 1
  1. 1.Kyushu Institute of TechnologyIizukaJapan

Personalised recommendations