On Retrieval Order of Statistics Information from OpenFlow Switches to Locate Lossy Links by Network Tomographic Refinement

  • Takemi NakamuraEmail author
  • Masahiro Shibata
  • Masato Tsuru
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)


To maintain service quality and availability in managed networks, detecting and locating high loss-rate links (i.e., lossy links that are likely congested or physically unstable) in a fast and light-weight manner is required. In our previous study, we proposed a framework of network-assisted location of lossy links on OpenFlow networks. In the framework, a measurement host launches a series of multicast probe packets traversing all full-duplex links; and then the controller retrieves statistics on the arrival of those probe packets at different input ports on different switches and compares them to locate high loss-rate links. The number of accesses to switches required to locate all lossy links strongly depends on the retrieval order in collecting the statistics and should be small as much as possible. Therefore, in this paper, to minimize the necessary number of accesses, we develop a new location scheme with an appropriate retrieval order using a Bayesian-based network tomography to refine candidates for lossy links. The results of numerical simulation on a real-world topology demonstrate the effectiveness of the new location scheme.



The research results have been achieved by the “Resilient Edge Cloud Designed Network (19304),” NICT, and by JSPS KAKENHI JP16K00130 and JP17K00135, Japan. We thank Mr. Suguru Goto and Mr. Yuki Fujimura for assistance.


  1. 1.
    Jain, S., Kumar, A., et al.: B4: experience with a globally-deployed software defined WAN. In: Proceedings of ACM SIGCOMM 2013, pp. 3–14 (2013)Google Scholar
  2. 2.
    Atary, A., Bremler-Barr, A.: Efficient round-trip time monitoring in OpenFlow networks. In: Proceedings of IEEE INFOCOM, pp. 1–9 (2016)Google Scholar
  3. 3.
    Shibuya, M., Tachibana, A., Hasegawa, T.: Efficient active measurement for monitoring link-by-link performance in OpenFlow networks. IEICE Trans. Commun. E99B(5), 1032–1040 (2016)CrossRefGoogle Scholar
  4. 4.
    Castro, R., Coates, M., Liang, G., Nowak, R., Yu, B.: Network tomography: recent developments. Stat. Sci. 19(3), 499–517 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Duffield, N.: Network tomography of binary network performance characteristics. IEEE Trans. Inf. Theory 52(12), 5373–5388 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tachibana, A., Ano, S., Hasegawa, T., Tsuru, M., Oie, Y.: Locating congested segments over the internet based on multiple end-to-end path measurements. IEICE Trans. Commun. E89–B(4), 1099–1109 (2006)CrossRefGoogle Scholar
  7. 7.
    Ma, L., He, T., Swami, A., Towsley, D., Leung, K.K.: Network capability in localizing node failures via end-to-end path measurements. IEEE/ACM Trans. Netw. 25(1), 434–450 (2017)CrossRefGoogle Scholar
  8. 8.
    Tri, N.M., Tsuru, M.: Locating deteriorated links by network-assisted multicast proving on OpenFlow networks. In: Proceedings of the 24th IEEE Symposium on Computers and Communications (ISCC 2019), 6 p, to appear in July 2019Google Scholar
  9. 9.
    The Internet Topology Zoo: Accessed 15 Feb 2019

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Takemi Nakamura
    • 1
    Email author
  • Masahiro Shibata
    • 1
  • Masato Tsuru
    • 1
  1. 1.Computer Science and System EngineeringKyushu Institute of TechnologyIizukaJapan

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