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Efficient routing strategy with transmission history information and its surrogate analysis

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  • Yoshida, Akinori
  • Shimada, Yutaka
  • Kimura, Takayuki

Abstract

Obtaining the optimum shortest paths for packets from their sources to destinations in communication networks is referred to as the packet routing problem. In a packet routing problem, the distribution of packets in the network always changes with time. Therefore, there is no guarantee that the shortest route at the current time is also the shortest one at the next time. For addressing these problems, a routing method using local transmission history information has already been proposed; this method shows effective performance. However, it is still unknown what kinds of topologies this method shows excellent performance for and how the transmission history information works to reduce packet congestion. To this end, we herein comprehensively evaluate the routing method using memory information. Numerical simulations clarify that the routing method using memory information shows excellent performance for heterogeneous type communication networks. Further, analysis of our method using surrogate data revealed that the transmission history information is useful for decentralizing packet congestion in communication networks.

Suggested Citation

  • Yoshida, Akinori & Shimada, Yutaka & Kimura, Takayuki, 2021. "Efficient routing strategy with transmission history information and its surrogate analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    • Handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120308955
    DOI: 10.1016/j.physa.2020.125597
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    References listed on IDEAS

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    1. Yang, Han-Xin & Tang, Ming, 2014. "Adaptive routing strategy on networks of mobile nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 1-7.
    2. Horiguchi, Tsuyoshi & Hayashi, Keisuke & Tretiakov, Alexei, 2005. "Reinforcement learning for congestion-avoidance in packet flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(1), pages 329-348.
    3. Horiguchi, Tsuyoshi & Ishioka, Shigeki, 2001. "Routing control of packet flow using neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 297(3), pages 521-531.
    4. Kimura, Takayuki & Nakajima, Hiroyuki & Ikeguchi, Tohru, 2007. "A packet routing method for complex networks by a stochastic neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 658-672.
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    Cited by:

    1. Ma, Jinlong & Kong, Lingkang & Li, Hui-Jia, 2023. "An effective edge-adding strategy for enhancing network traffic capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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