A Learning Automata-Based Version of SG-1 Protocol for Super-Peer Selection in Peer-to-Peer Networks

  • Shahrbanoo Gholami
  • Mohammad Reza Meybodi
  • Ali Mohammad Saghiri
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)


Super-peer topologies have been found efficient and effective in heterogeneous peer-to-peer networks. Due to dominant position of super-peers, super-peer selection requires a protocol that is aware of peer capacities. Lack of global information about other peers’ capacity and dynamic nature of peer-to-peer networks are two major challenges that impose uncertainty in decision-making. SG-1, is a well-known super-peer selection protocol considering peer capacities, but lack of an appropriate decision-making mechanism makes this protocol slow at convergence and imposes overhead of client transfer between selected super-peers. In this paper, we propose an improved version of SG-1 that uses learning automata as an adaptive decision-making mechanism. For this purpose, each peer is equipped with a learning automaton which is used locally in the decisions taken by that peer. Simulations show effectiveness of proposed protocol in terms of convergence time, scalability, capacity utilization, behavior towards super-peer failure and communication cost, compared to SG-1.


peer-to-peer network super-peer learning automata 


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  1. 1.
    Androutsellis-Theotokis, S., Spinellis, D.: A survey of peer-to-peer content distribution technologies. ACM Computing Surveys (CSUR) 36(4), 335–371 (2004)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Meshkova, E., Riihijärvi, J., Petrova, M., Mähönen, P.: A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks. Computer Networks 52(11), 2097–2128 (2008)CrossRefGoogle Scholar
  4. 4.
    Yang, B., Garcia-Molina, H.: Designing a super-peer network. In: Proceedings of 19th International Conference on Data Engineering, pp. 49–60 (2003)Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Najim, K., Poznyak, A.S.: Learning automata: theory and applications. Pergamon Press, Inc., (1994)CrossRefGoogle Scholar
  8. 8.
    Narendra, K.S., Thathachar, M.A.: Learning automata: an introduction. Printice-Hall Inc., Englewood Cliffs (1989)zbMATHGoogle Scholar
  9. 9.
    Akbari Torkestani, J., Meybodi, M.R.: An intelligent backbone formation algorithm for wireless ad hoc networks based on distributed learning automata. Computer Networks 54(5), 826–843 (2010)CrossRefGoogle Scholar
  10. 10.
    Esnaashari, M., Meybodi, M.R.: Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach. Wireless Networks 19(5), 945–968 (2013)CrossRefGoogle Scholar
  11. 11.
    Montresor, A.: A robust protocol for building superpeer overlay topologies. In: Proceeding of Fourth International Conference on Peer-to-Peer Computing, pp. 202–209 (2004)Google Scholar
  12. 12.
    Snyder, P.L., Greenstadt, R., Valetto, G.: Myconet: A fungi-inspired model for superpeer-based peer-to-peer overlay topologies. In: 3rd International Conference on Self-Adaptive and Self-Organizing Systems, pp. 40–50 (2009)Google Scholar
  13. 13.
    Liu, M., Harjula, E., Ylianttila, M.: An efficient selection algorithm for building a super-peer overlay. Journal of Internet Services and Applications 4(4), 1–12 (2013)Google Scholar
  14. 14.
    Singh, A., Haahr, M.: Creating an adaptive network of hubs using Schelling’s model. Communications of the ACM 49(3), 69–73 (2006)CrossRefGoogle Scholar
  15. 15.
    Gao, Z., Gu, Z., Wang, W.: SPSI: A hybrid super-node election method based on information theory. In: 14th International Conference on Advanced Communication Technology, pp. 1076–1081 (2012)Google Scholar
  16. 16.
    Liu, W., Yu, J., Song, J., Lan, X., Cao, B.: ERASP: an efficient and robust adaptive superpeer overlay network. In: Progress in WWW Research and Development, pp. 468–474 (2008) Google Scholar
  17. 17.
    Xiao, L., Zhuang, Z., Liu, Y.: Dynamic layer management in superpeer architectures. IEEE Transactions on Parallel and Distributed Systems 16(11), 1078–1091 (2005)CrossRefGoogle Scholar
  18. 18.
    Sachez-Artigas, M., Garcia-Lopez, P., Skarmeta, A.F.G.: On the feasibility of dynamic superpeer ratio maintenance. In: International Conference on Peer-to-Peer Computing, pp. 333–342 (2008)Google Scholar
  19. 19.
    Chen, N., Hu, R., Zhu, Y., Wang, Z.: Constructing fixed-ratio superpeer-based overlay. In: 3rd International Conference on Computer Science and Information Technology, vol. 7, pp. 242–245 (2010)Google Scholar
  20. 20.
    Li, J.S., Chao, C.H.: An Efficient Superpeer Overlay Construction and Broadcasting Scheme Based on Perfect Difference Graph. IEEE Trans. Parallel Distrib. Syst. 21(5), 594–606 (2010)CrossRefGoogle Scholar
  21. 21.
    Tan, Y., Lin, Y., Yu, J., Chen, Z.: k-PDG-Based Topology Construction and Maintenance Approaches for Querying in P2P Networks. J. Comput. Inf. Syst. 7(9), 3209–3218 (2011)Google Scholar
  22. 22.
    Teng, Y.H., Lin, C.N., Hwang, R.H.: SSNG: A Self-Similar Super-Peer Overlay Construction Scheme for Super Large-Scale P2P Systems. In: 17th International Conference on Parallel and Distributed Systems, pp. 782–787 (2011)Google Scholar
  23. 23.
    Sacha, J., Dowling, J., Cunningham, R., Meier, R.: Using aggregation for adaptive super-peer discovery on the gradient topology. In: Keller, A., Martin-Flatin, J.-P. (eds.) SelfMan 2006. LNCS, vol. 3996, pp. 73–86. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Lo, V., Zhou, D., Liu, Y., GauthierDickey, C., Li, J.: Scalable supernode selection in peer-to-peer overlay networks. In: Second International Workshop on Hot Topics in Peer-to-Peer Systems, HOT-P2P 2005, pp. 18–25 (2005)Google Scholar
  25. 25.
    Jesi, G., Montresor, A., Babaoglu, O.: Proximity-aware superpeer overlay topologies. IEEE Trans Network Serv. Manage 4(2), 78–83 (2007)Google Scholar
  26. 26.
    Dumitrescu, M., Andonie, R.: Clustering Superpeers in P2P Networks by Growing Neural Gas. In: 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pp. 311–318 (2012)Google Scholar
  27. 27.
    Wolf, S.: On the complexity of the uncapacitated single allocation p-hub median problem with equal weights. In: Internal Report 363/07, University of Kaiserslautern, Kaiserslautern, Germany (2007)Google Scholar
  28. 28.
    Wolf, S., Merz, P.: Evolutionary local search for the super-peer selection problem and the p-hub median problem. In: Bartz-Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HM 2007. LNCS, vol. 4771, pp. 1–15. Springer, Heidelberg (2007)Google Scholar
  29. 29.
    Chen, J., Wang, R.M., Li, L., Zhang, Z.H., Dong, X.S.: A Distributed Dynamic Super Peer Selection Method Based on Evolutionary Game for Heterogeneous P2P Streaming Systems. In: Mathematical Problems in Engineering (2013)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Jelasity, M., Kowalczyk, W., Van Steen, M.: Newscast computing. Technical Report IR-CS-006, Vrije Universiteit Amsterdam, Department of Computer Science, Amsterdam, The Netherlands (2003)Google Scholar
  31. 31.
    Jelasity, M., Montresor, A., Jesi, G.P., Voulgaris, S.: PeerSim P2P Simulator (2009),

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shahrbanoo Gholami
    • 1
  • Mohammad Reza Meybodi
    • 1
  • Ali Mohammad Saghiri
    • 1
  1. 1.Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIran

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