Abstract
More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. The management and optimization of P2P networks have become the important research contents. This paper presents a framework for network self-evolving problem based on distributed swarm intelligence, which is achieved by the collaboration of different nodes. Each node, as an independent agent, only has the information of its local topology. Through the consensus method, each node searches for an evolving structure to evolve its local topology. The self-evolving of each node’s local topology makes the whole topology converge to the optimal topology model. In the experiments, two simulated examples under different network topologies illustrate the feasibility of our approach.
Supported by the National Key Research and Development Program of China under Grant No. 2019YFB1005203.
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References
Rozario, F., Han, Z., Niyato, D.: Optimization of non-cooperative P2P network from the game theory point of view. In: 2011 IEEE Wireless Communications and Networking Conference, pp. 868–873. IEEE (2011)
Charilas, D.E., Panagopoulos, A.D.: A survey on game theory applications in wireless networks. Comput. Netw. 54(18), 3421–3430 (2010)
Lee, S.W., Palmer-Brown, D., Roadknight, C.M.: Performance-guided neural network for rapidly self-organising active network management. Neurocomputing 61, 5–20 (2004)
Auvinen, A., Keltanen, T., Vapa, M.: Topology management in unstructured P2P networks using neural networks. In: 2007 IEEE Congress on Evolutionary Computation, pp. 2358–2365. IEEE (2007)
Tian, C., Zhang, Y., Yin, T.: Topology self-optimization for anti-tracking network via nodes distributed computing. In: Gao, H., Wang, X. (eds.) CollaborateCom 2021. LNICST, vol. 406, pp. 405–419. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92635-9_24
Berahas, A.S., Bollapragada, R., Keskar, N.S., Wei, E.: Balancing communication and computation in distributed optimization. IEEE Trans. Autom. Control 64(8), 3141–3155 (2018)
Wang, H., Liao, X., Huang, T., Li, C.: Cooperative distributed optimization in multiagent networks with delays. IEEE Trans. Syst. Man Cybern. Syst. 45(2), 363–369 (2014)
Tian, C., Zhang, Y., Yin, T.: Modeling of anti-tracking network based on convex-polytope topology. In: Krzhizhanovskaya, V.V., Závodszky, G., Lees, M.H., Dongarra, J.J., Sloot, P.M.A., Brissos, S., Teixeira, J. (eds.) ICCS 2020. LNCS, vol. 12138, pp. 425–438. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50417-5_32
Acknowledgements
The authors would like to thank the anonymous reviewers for their insightful comments and suggestions on this paper. This work was supported in part by the National Key Research and Development Program of China under Grant No. 2019YFB1005203.
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Tian, C., Zhang, Y., Yin, T. (2022). A Framework for Network Self-evolving Based on Distributed Swarm Intelligence. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_24
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