Skip to main content

Advertisement

Log in

Multi swarms for neighbor selection in peer-to-peer overlay networks

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of neighbor selection (NS) in P2P networks. Particle swarm share some common characteristics with P2P in the dynamic socially environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The portion of the adjustment to the velocity influenced by the individual’s cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm, makes an important influence on the particles’ ergodic and synergetic performance. We also attempt to theoretically prove that the multi-swarm optimization algorithm converges with a probability of 1 towards the global optima. The performance of our approach is evaluated and compared with other two different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lua, E. K., Crowcroft, J., Pias, M., Sharma, R., & Lim, S. (2005). A survey and comparison of peer-to-peer overlay network schemes. IEEE Communications Surveys and Tutorials, 7(2), 72–93.

    Article  Google Scholar 

  2. Kwok, S. (2006). P2P searching trends: 2002–2004. Information Processing and Management, 42, 237–247.

    Article  Google Scholar 

  3. Huang, X., Chang, C., & Chen, M. (2006). PeerCluster: a cluster-based peer-to-peer system. IEEE Transactions on Parallel and Distributed Systems, 17(10), 1110–1123.

    Article  Google Scholar 

  4. Belmonte, M. V., Conejo, R., Díaz, M., & Pérez-de-la-Cruz, J. L. (2005). Lecture notes in artificial intelligence : Vol. 4177. Coalition formation in P2P file sharing systems (pp. 153–162). CAEPIA’05. Berlin: Springer.

    Google Scholar 

  5. Idris, T., & Altmann, J. (2006). A market-managed topology formation algorithm for peer-to-peer file sharing networks. Lecture Notes in Computer Science, 4033, 61–77.

    Article  Google Scholar 

  6. Cho, H. (2007). Lecture notes in computer science : Vol. 4490. An update propagation algorithm for P2P file sharing over wireless mobile networks (pp. 753–760). ICCS’07. Berlin: Springer.

    Google Scholar 

  7. Pianese, F., Perino, D., Keller, J., & Biersack, E. W. (2007). PULSE: an adaptive, incentive-based, unstructured P2P live streaming system. IEEE Transactions on Multimedia, 9(8), 1645–1660.

    Article  Google Scholar 

  8. Sigurdsson, H. M., Halldorsson, U. R., & Hasslinger, G. (2007). Potentials and challenges of peer-to-peer based content distribution. Telematics and Informatics, 24, 348–365.

    Article  Google Scholar 

  9. Yang, S., & Chen, I. (2008). A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network. International Journal of Human-Computer Studies, 66, 36–50.

    Article  Google Scholar 

  10. Kim, J. K., Kim, H. K., & Cho, Y. H. (2008). A user-oriented contents recommendation system in peer-to-peer architecture. Expert Systems with Applications, 34, 300–312.

    Article  Google Scholar 

  11. Sen, S., & Wang, J. (2004). Analyzing peer-to-peer traffic across large networks. IEEE/ACM Transactions on Networking, 12(2), 219–232.

    Article  Google Scholar 

  12. Leung, A., & Kwok, Y. (2005). Lecture notes in computer science : Vol. 3794. An efficient and practical greedy algorithm for server-peer selection in wireless peer-to-peer file sharing networks (pp. 1016–1025). MSN’05. Berlin: Springer.

    Google Scholar 

  13. Ardizzone, E., Gatani, L., LaCascia, M., LoRe, G., & Ortolani, M. (2007). Enhanced P2P services providing multimedia content. Advances in Multimedia, 2007(2), 1–12.

    Article  Google Scholar 

  14. Androutsellis-theotokis, S., & Spinellis, D. (2004). A survey of peer-to-peer content distribution technologies. ACM Computing Surveys, 36(4), 335–371.

    Article  Google Scholar 

  15. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press.

    Google Scholar 

  16. Clerc, M. (2006). Particle swarm optimization. London: ISTE.

    Book  Google Scholar 

  17. Abraham, A., Guo, H., & Liu, H. (2006). Swarm intelligence: foundations, perspectives and applications. In Swarm Intelligent Systems, Studies in Computational Intelligence (pp. 3–25). Berlin: Springer.

    Google Scholar 

  18. Schollmeier, R. (2001). A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications. In Proceedings of the 1st international August conference on peer-to-peer computing (pp. 101–102).

  19. Ghosal, D., Poon, B. K., & Kong, K. (2005). P2P contracts: a framework for resource and service exchange. Future Generation Computer Systems, 21, 333–347.

    Article  Google Scholar 

  20. Koo, S. G., Kannan, K., & Lee, C. S. (2006). A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks. Future Generation Computer Systems, 22, 732–741.

    Article  Google Scholar 

  21. Surana, S., Godfrey, B., Lakshminarayanan, K., Karp, R., & Stoica, I. (2006). Load balancing in dynamic structured peer-to-peer systems. Performance Evaluation, 63, 217–240.

    Article  Google Scholar 

  22. Merrer, E., Kermarrec, A., & Massoulié, L. (2006). Peer-to-peer size estimation in large and dynamic networks: a comparative study. In Proceedings of 15th IEEE international symposium on high performance distributed computing (pp. 7–17).

  23. Meo, M., & Milan, F. (2008). QoS content management for P2P file-sharing applications. Future Generation Computer Systems, 24, 213–221.

    Article  Google Scholar 

  24. Risson, J., & Moors, T. (2006). Survey of research towards robust peer-to-peer networks: search methods. Computer Networks, 50, 3485–3521.

    Article  Google Scholar 

  25. Habib, A., & Chuang, J. (2006). Service differentiated peer selection: an incentive mechanism for peer-to-peer media streaming. IEEE Transactions on Multimedia, 8(3), 610–623.

    Article  Google Scholar 

  26. Lo, V., Zhou, D., Liu, Y., GauthierDickey, C. S., & Li, J. (2005). Scalable supernode selection in peer-to-peer overlay networks. In Proceedings of the 2nd IEEE international workshop on hot topics in peer-to-peer systems (pp. 18–27).

  27. Kothapalli, K., & Scheideler, C. (2005). Supervised peer-to-peer systems. In Proceedings of the 8th international symposium on parallel architectures, Algorithms and Networks (pp. 188–193).

  28. Koulouris, T., Henjes, R., Tutschku, K., & de Meer, H. (2004). Implementation of adaptive control for P2P overlays. Lecture Notes in Computer Science, 2982, 292–306.

    Article  Google Scholar 

  29. Liu, Y., Xiao, L., Esfahanian, A., & Ni, L. M. (2005). Approaching optimal peer-to-peer overlays. In Proceedings of the 13th IEEE international symposium on modeling, analysis, and simulation of computer and telecommunication systems (pp. 407–414).

  30. Leung, A. K., & Kwok, Y. (2008). On localized application-driven topology control for energy-efficient wireless peer-to-peer file sharing. IEEE Transactions on Mobile Computing, 7(1), 66–80.

    Article  Google Scholar 

  31. Mastronarde, N., Turaga, D. S., & van der Schaar, M. (2007). Collaborative resource exchanges for peer-to-peer video streaming over wireless mesh networks. IEEE Journal on Selected Areas in Communications, 25(1), 108–118.

    Article  Google Scholar 

  32. Fenner, T., Levene, M., Loizou, G., & Roussos, G. (2007). A stochastic evolutionary growth model for social networks. Computer Networks, 51, 4586–4595.

    Article  Google Scholar 

  33. Sacha, J., Dowling, J., Cunningham, R., & Meier, R. (2006). Discovery of stable peers in a self-organising peer-to-peer gradient topology. Lecture Notes in Computer Science, 4025, 70–83.

    Article  Google Scholar 

  34. Bisnik, N., & Abouzeid, A. A. (2007). Optimizing random walk search algorithms in P2P networks. Computer Networks, 51(6), 1499–1514.

    Article  Google Scholar 

  35. Kersch, P., Szabo, R., Cheng, L., Jean, K., & Galis, A. (2007). Stochastic maintenance of overlays in structured P2P systems. Computer Communications. doi:10.1016/j.comcom.2007.08.017.

    Google Scholar 

  36. Krishnamurthy, B., & Wang, J. (2001). Topology modeling via cluster graphs. In Proceedings of the 1st ACM SIGCOMM workshop on Internet measurement (pp. 19–23).

  37. Padmanabhan, V. N., & Subramanian, L. (2001). An investigation of geographic mapping techniques for Internet hosts. In Proceedings of the ACM conference on applications, technologies, architectures, and protocols for computer communications (pp. 173–185).

  38. Nakao, A., Peterson, L., & Bavier, A. (2003). A routing underlay for overlay networks. In Proceedings of the ACM conference on applications, technologies, architectures, and protocols for computer communications (pp. 11–18).

  39. Xu, X. (2005). ABC: a cluster-based protocol for resource location in peer-to-peer systems. Journal of Parallel and Distributed Computing. doi:10.1016/j.jpdc.2005.02.004.

    Google Scholar 

  40. Ramaswamy, L., Gedik, B., & Liu, L. (2005). A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Transactions on Parallel and Distributed Systems, 16(9), 814–829.

    Article  Google Scholar 

  41. Tewari, S., & Kleinrock, L. (2007). Optimal search performance in unstructured peer-to-peer networks with clustered demands. IEEE Journal on Selected Areas in Communications, 25(1), 84–95.

    Article  Google Scholar 

  42. Kurmanowytsch, R., Kirda, E., Kerer, C., & Dustdar, S. (2003). OMNIX: a topology-independent P2P middleware. In Proceedings of the 15th conference on advanced information systems engineering.

  43. Gupta, R., Sekhri, V., & Somani, A. K. (2006). CompuP2P: an architecture for Internet computing using peer-to-peer networks. IEEE Transactions on Parallel and Distributed Systems, 17(11), 1306–1320.

    Article  Google Scholar 

  44. Zeinalipour-Yazti, D., Kalogeraki, V., & Gunopulos, D. (2007). pFusion: a P2P architecture for Internet-scale content-based search and retrieval. IEEE Transactions on Parallel and Distributed Systems, 18(6), 804–817.

    Article  Google Scholar 

  45. Ghanea-Hercock, R. A., Wang, F., & Sun, Y. (2006). Self-organizing and adaptive peer-to-peer network. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(6), 1230–1236.

    Article  Google Scholar 

  46. Biersack, E. W., Rodriguez, P., & Felber, P. (2004). Lecture notes in computer science : Vol. 3266. Performance analysis of peer-to-peer networks for file distribution (pp. 1–10). QofIS’04. Berlin: Springer.

    Google Scholar 

  47. Carchiolo, V., Malgeri, M., Mangioni, G., & Nicosia, V. (2007). Emerging structures of P2P networks induced by social relationships. doi:10.1016/j.comcom.2007.08.016.

  48. Zhuge, H., & Li, X. (2007). Peer-to-peer in metric space and semantic space. IEEE Transactions on Knowledge and Data Engineering, 19(6), 759–771.

    Article  Google Scholar 

  49. Wang, S., Chou, H., Wei, D., & Kuo, S. (2007). On the fundamental performance limits of peer-to-peer data replication in wireless Ad hoc networks. Journal on Selected Areas in Communications, 25(1), 211–221.

    Article  Google Scholar 

  50. Qiu, D., & Sang, W. (2007). Global stability of peer-to-peer file sharing systems. Computer Communications. doi:10.1016/j.comcom.2007.08.012.

    Google Scholar 

  51. Salman, A., Ahmad, I., & Al-Madani, S. (2002). Particle swarm optimization for task assignment problem. Microprocessors and Microsystems, 26, 363–371.

    Article  Google Scholar 

  52. Clerc, M., & Kennedy, J. (2002). The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.

    Article  Google Scholar 

  53. Cristian, T. I. (2003). The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters, 85(6), 317–325.

    Article  Google Scholar 

  54. van den Bergh, F., & Engelbrecht, A. P. (2006). A study of particle swarm optimization particle trajectories. Information Sciences, 176, 937–971.

    Article  Google Scholar 

  55. Liu, H., Abraham, A., & Clerc, M. (2007). Chaotic dynamic characteristics in swarm intelligence. Applied Soft Computing, 7, 1019–1026.

    Article  Google Scholar 

  56. Kennedy, J., & Eberhart, R. (2001). Swarm Intelligence. Los Altos: Kaufmann.

    Google Scholar 

  57. Liu, H., Li, B., Ji, Y., & Sun, T. (2006). Particle swarm optimisation from lbest to gbest. In Applied soft computing technologies: the challenge of complexity (pp. 537–545). Berlin: Springer.

    Chapter  Google Scholar 

  58. Grosan, C., Abraham, A., & Nicoara, M. (2005). Search optimization using hybrid particle sub-swarms and evolutionary algorithms. International Journal of Simulation Systems, Science and Technology, 6(10), 60–79.

    Google Scholar 

  59. Jiang, C. W., & Etorre, B. (2005). A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Mathematics and Computers in Simulation, 68, 57–65.

    Article  Google Scholar 

  60. Liu, H., & Abraham, A. (2007). An hybrid fuzzy variable neighborhood particle swarm optimization algorithm for solving quadratic assignment problems. Journal of Universal Computer Science, 13(7), 1032–1054.

    Google Scholar 

  61. Liang, J. J., Qin, A. K., Suganthan, P. N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10(3), 281–295.

    Article  Google Scholar 

  62. Elshamy, W., Emara, H. M., & Bahgat, A. (2007). Clubs-based particle swarm optimization. In Proceedings of the IEEE international conference on swarm intelligence symposium (Vol. 1, pp. 289–296).

  63. Guo, C., & Tang, H. (2001). Global convergence properties of evolution strategies. Mathematica Numerica Sinica, 23(1), 105–110.

    Google Scholar 

  64. He, R., Wang, Y., Wang, Q., Zhou, J., & Hu, C. (2005). An improved particle swarm optimization based on self-adaptive escape velocity. Journal of Software, 16(12), 2036–2044.

    Article  Google Scholar 

  65. Halmos, P. (1950). Measure theory. New York: Van Nostrand.

    Google Scholar 

  66. Xu, Z., Cheng, G., & Liang, Y. (1999). Search capability for an algebraic crossover. Journal of Xi’an Jiaotong University, 33(10), 88–99.

    Google Scholar 

  67. Whitley, L. D. (1991). Fundamental principles of deception in genetic search. In Foundation of genetic algorithms (pp. 221–241). California: Kaufmann.

    Google Scholar 

  68. Mastrolilli, M., & Gambardella, L. M. (2002). Effective neighborhood functions for the flexible job shop problem. Journal of Scheduling, 3(1), 3–20.

    Article  Google Scholar 

  69. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.

    Google Scholar 

  70. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading: Addison-Wesley.

    Google Scholar 

  71. Abraham, A. (2005). Evolutionary computation. In Handbook for measurement systems design (pp. 920–931). London: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ajith Abraham.

Additional information

This work is supported partially by NSFC Grant 60873054.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Abraham, A., Liu, H. & Hassanien, A.E. Multi swarms for neighbor selection in peer-to-peer overlay networks. Telecommun Syst 46, 195–208 (2011). https://doi.org/10.1007/s11235-010-9285-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-010-9285-3

Keywords

Navigation