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Routing control of packet flow using neural network

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  • Horiguchi, Tsuyoshi
  • Ishioka, Shigeki

Abstract

We investigate a packet flow problem in a computer network within a framework of statistical physics. We propose a model for packet routing control by putting a neural network at each node. The energy function for the whole system is defined in order to express competition among queue length, distance to destination of a packet and firing rate of neurons at each node and so on. A dynamical equation for output of neurons is derived in order to make the energy decrease as a function of time, by using a mean field approximation. By means of computer simulations, we calculate an average number of fired nodes, an average number of packets arrived at their destinations, and so on. We find that the buffer size is essential in the proposed model for packet routing control.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:297:y:2001:i:3:p:521-531
    DOI: 10.1016/S0378-4371(01)00229-1
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    References listed on IDEAS

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    1. repec:cep:stiisp:/1996/309 is not listed on IDEAS
    2. John Y Wong & Michael B Yahuda, 1996. "Hong Kong and the People's Republic of China," STICERD - International Studies Paper Series 309, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    Cited by:

    1. 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.
    2. 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).

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