Advertisement

Probability-Based Routing Symmetry Metrics

  • Qin Wang
  • Fang Dong
  • Xin-Li Yang
  • Rui Yin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

In communication networks, if streams between two endpoints follow the same physical paths for both forward and reverse direction, they are symmetric. Routing asymmetry affects several protocols, and impacts part of traffic analysis techniques. We propose two routing symmetry metrics to express different meanings when talking about routing symmetry, namely, (1) the forward and reverse flows coming from one node to another are exactly the same, and (2) one single node is visited by both flows. The two metrics are termed as identity symmetry and cross symmetry, respectively. Then, we build a model to link the macroscopic symmetry with the microscopic routing behavior, and present some analysis results, thus make it possible to design a routing algorithm with some desired symmetry. The simulation and dataset study show that routing algorithms that generate next hop randomly will lead to a symmetric network, but it is not the case for Internet. Because the paths of Internet are heavily dominated by a small number of prevalent routes, Internet is highly asymmetry.

Keywords

Routing symmetry Routing behavior model Statistical process 

References

  1. 1.
    John, W., Dusi, M., Claffy, K.: Estimating routing symmetry on single links by passive flow measurements, pp. 473–478. ACM (2010)Google Scholar
  2. 2.
    Paxson, V.: End-to-end routing behavior in the internet. IEEE/ACM Trans. Netw. 5(5), 601–615 (1997).  https://doi.org/10.1109/90.649563CrossRefGoogle Scholar
  3. 3.
    Alderson, D., Chang, H., Roughan, M., Uhlig, S., Willinger, W.: The many facets of internet topology and traffic. Netw. Heterog. Media 1(4), 569–600 (2017)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Nguyen, T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. IEEE Commun. Surv. Tutor. 10(4), 56–76 (2009)CrossRefGoogle Scholar
  5. 5.
    McGregor, A., Hall, M., Lorier, P., Brunskill, J.: Flow clustering using machine learning techniques. In: Barakat, C., Pratt, I. (eds.) PAM 2004. LNCS, vol. 3015, pp. 205–214. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24668-8_21CrossRefGoogle Scholar
  6. 6.
    Fahad, A., Alshatri, N., Tari, Z., Alamri, A.: A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans. Emerg. Top. Comput. 2(3), 267–279 (2014)CrossRefGoogle Scholar
  7. 7.
    He, Y., Faloutsos, M., Krishnamurthy, S.: Quantifying routing asymmetry in the internet at the as level, pp. 1474–1479. IEEE(2004)Google Scholar
  8. 8.
    Mao, Z.M., Qiu, L., Wang, J., Zhang, Y.: On as-level path inference, pp. 339–349. ACM (2005)Google Scholar
  9. 9.
    Crotti, M., Gringoli, F., Salgarelli, L.: Impact of asymmetric routing on statistical traffic classification, pp. 1–8. IEEE (2009)Google Scholar
  10. 10.
    Dong, F., Liu, J., Dai, S.: Identity routing symmetry metrics for routing behavior. In: Atlantis Conference, pp. 1853–1856 (2016)Google Scholar
  11. 11.
    Tozal, M.: Autonomous system ranking by topological characteristics: a comparative study. In: Systems Conference, pp. 1–8 (2017)Google Scholar
  12. 12.
    Weinsberg, U., Shavitt, Y., Schwartz, Y.: Stability and symmetry of internet routing, pp. 1–2. IEEE (2009)Google Scholar
  13. 13.
    Keralapura, R., Mellia, M., Grimaudo, L.: Self-learning classifier for internet traffic. US 8694630 B1. IEEE (2014)Google Scholar
  14. 14.
    Zhang, J., Chen, X., Xiang, Y., Zhou, W., Wu, J.: Robust network traffic classification. IEEE/ACM Trans. Netw. 23(4), 1257–1270 (2015)CrossRefGoogle Scholar
  15. 15.
    Pucha, H., Zhang, Y., Mao, Z.M., Hu, Y.C.: Understanding network delay changes caused by routing events, pp. 73–84. ACM (2007)Google Scholar
  16. 16.
    Schwartz, Y., Shavitt, Y., Weinsberg, U.: On the diversity, stability and symmetry of end-to-end internet routes, pp. 1–6. IEEE (2010)Google Scholar
  17. 17.
    Lcvenshtcin, V.: Binary coors capable or ‘correcting deletions, insertions, and reversals (1966)Google Scholar
  18. 18.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  19. 19.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Servetto, S.D., Barrenechea, G.: Constrained random walks on random graphs: routing algorithms for large scale wireless sensor networks. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 12–21. ACM (2002)Google Scholar
  21. 21.
    Blondel, O., Hilario, M.R., Santos, R.S.D., Sidoravicius, V., Teixeira, A.: Random walk on random walks: low densities. Mathematics (2017)Google Scholar
  22. 22.
    Li, M., et al.: Effects of weight on structure and dynamics in complex networks. arXiv preprint cond-mat/0601495 (2006)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.China University Program, Texas Instruments Semiconductor Technologies (Shanghai)ShanghaiChina
  2. 2.College of Information and Electronic EngineeringZhejiang University City CollegeHangzhouChina
  3. 3.Port Management Office of Haiyan CountyJiaxingChina

Personalised recommendations