Improving Search in Unstructured P2P Systems: Intelligent Walks (I-Walks)

  • Francis Otto
  • Song Ouyang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Random Walks (RW) search technique can greatly reduce bandwidth production but generally fails to adapt to different workloads and environments. A Random Walker can’t learn anything from its previous successes or failures, displaying low success rates and high latency. In this paper, we propose Intelligent Walks (IW) search mechanism – a modification of RW, exploiting the learning ability and the shortest path distance of node neighbors. A node probes its neighbors before forwarding the query. The probe is to find a candidate that has the shortest distance from the query source and/or has ever seen before the object that is going to be sent. If there isn’t such candidate, then a node is chosen as usual (at random). The experimental results demonstrate that new method achieves better performance than RW in terms of success rate.


Unstructured P2P Search Random Walks Intelligent Walks 


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  1. 1.
    Napster (2000),
  2. 2.
    Gnutella: The Gnutella Protocol Specification,
  3. 3.
    Clarke, I., Sandberg, O., Wiley, B., Hong, T.: Freenet: A Distributed Anonymous File Storage and Retrieval System. In: Workshop on Design Issues in Anonymity and Unobservability (2000)Google Scholar
  4. 4.
    Tsoumakos, D., Roussopoulos, N.: Analysis and Comparison of P2P Search Methods. University of Maryland, USA (2003)Google Scholar
  5. 5.
    Lin, T., Wang, H.: Search Performance Analysis in Peer-to-Peer Networks, TaiwanGoogle Scholar
  6. 6.
    Bisnik, N., Abouzeid, A.: Modeling and Analysis of Random Walk Search Algorithms in P2P Networks. Rensselaer Polytechnic Institute, Troy, New York (2005)Google Scholar
  7. 7.
    Zhong, M., Shen, K.: Popularity Biased Random Walks for Peer-to-Peer Search under the Square-Root Principle, University of Rochester (2006)Google Scholar
  8. 8.
    Tsoumakos, D., Roussopoulos, N.: Adaptive Probabilistic Search (APS) for Peer-to-Peer Networks. Technical Report CS-TR-4451, Univerisity of Maryland (2003)Google Scholar
  9. 9.
    Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of State Calculations by Fast Computing Machines. J. Chem. Phys. 21, 1087–1092 (1953)CrossRefGoogle Scholar
  10. 10.
    Daswani, S., Fisk, A.: Gnutella UDP Extension for Scalable Searches (GUESS) v0.1Google Scholar
  11. 11.
  12. 12.
    Fabrikant, A., et al.: Heuristically Optimized Trade-offs: A New Paradigm for Power Laws in the Internet,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francis Otto
    • 1
  • Song Ouyang
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangsha, HunanP.R. China

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