IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v566y2021ics0378437120308955.html
   My bibliography  Save this article

Efficient routing strategy with transmission history information and its surrogate analysis

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120308955
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125597?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Yin, Rongrong & Zhang, Kai & Ma, Xuyao & Wang, Yumeng & Li, Linhui, 2023. "Analysis of cascading failures caused by mobile overload attacks in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    3. Su, Zhu & Liu, Sannyuya & Deng, Weibing & Li, Wei & Cai, Xu, 2019. "Transportation dynamics on networks of heterogeneous mobile agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1379-1386.
    4. Ding, Zhongjun & Chen, Bokui & Zhang, Lele & Jiang, Rui & Wu, Yao & Ding, Jianxun, 2019. "Segment travel time route guidance strategy in advanced traveler information systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:566:y:2021:i:c:s0378437120308955. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.