Efficient Clustered Network Telemetry based on Failure Awareness

DOI
  • Kai Zhang
    Qilu University of Technology (Shandong Academy of Sciences)
  • Libin Liu
    Qilu University of Technology (Shandong Academy of Sciences)
  • Lizhuang Tan
    Qilu University of Technology (Shandong Academy of Sciences)
  • Ye Zhang
    Qilu University of Technology (Shandong Academy of Sciences)
  • Wei Gao
    Qilu University of Technology (Shandong Academy of Sciences)
  • Wei Zhang
    Qilu University of Technology (Shandong Academy of Sciences)

Description

Nowadays, various network telemetry technologies are proposed to monitor the network and detect failures accurately in real-time, which can be categorized into two types, including the proactive network telemetry (NT) and the passive one. The passive NT can monitor the network with low bandwidth overhead, yet, cannot guarantee full network coverage. The proactive one can achieve full coverage, yet, lead to high bandwidth cost. To deal with the problem, we propose a failure-aware clustered network telemetry approach, called CNT. CNT leverages the practical objective network operating experience: different network links have various failure probabilities. It is aware of the failure probabilities and assigns the network links into two clusters accordingly. Then, based on the original network topology, CNT designs an active path planning algorithm to connect the two clusters of links into two sub-topologies, respectively. Finally, CNT performs network telemetry with different cycles. We evaluate CNT with various simulation experiments. The results show that compared to existing proactive schemes, CNT can achieve comparable network coverage with less cost.

Journal

  • IEICE Proceeding Series

    IEICE Proceeding Series 70 PS4-12-, 2022-09-28

    The Institute of Electronics, Information and Communication Engineers

Details 詳細情報について

  • CRID
    1390294031762564992
  • DOI
    10.34385/proc.70.ps4-12
  • ISSN
    21885079
  • Text Lang
    en
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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