Impact of Small-World Effect on the ip-level Routing Dynamics

  • Frédéric Tounwendyam OuédraogoEmail author
  • Tegawendé Bissyandé
  • Sawadogo Daouda
  • Didier Bassolé
  • Abdoulaye Séré
  • Oumarou Sié
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 171)


Running periodically traceroute-like measurements at suite frequency from a given monitor towards a fixed set of destinations allows observing a dynamics of routing topology around the monitor. This observed dynamics has revealed two main characteristics: the topology evolves at a pace much higher than expected and the occurrence of observed ip addresses provides a pattern of the ip-level routing dynamics. In this paper, we aim to provide some explanation of these characteristics through the small-world effect, observed on most complex networks. We are able to reproduce the observed dynamics by modeling the measurement on small-world graph. Thus, we show by simulation the influence of the coefficient clustering and the average path lengths on the dynamics.


Internet Dynamics Modeling Topology Characterization 



This work is supported by the National Scientific Research Fund, MESS/BF/2014.


  1. 1.
    Barrat, A., Weight, M.: On the properties of small-word network models. Eur. Phys. J. B 13(3), 547–560 (2000)CrossRefGoogle Scholar
  2. 2.
    Chang, H., Jamin, S., Willinger, W.: Internet connectivity at the as-level: an optimization-driven modeling approach. In: ACM SIGCOMM MoMeTools Workshop (2003)Google Scholar
  3. 3.
    Cunha, Í., Teixeira, R., Diot, C.: Measuring and characterizing end-to-end route dynamics in the presence of load balancing. In: Spring, N., Riley, G.F. (eds.) PAM 2011. LNCS, vol. 6579, pp. 235–244. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19260-9_24 CrossRefGoogle Scholar
  4. 4.
    Cunha, I., Teixeira, R., Veitch, D., Diot, C.: Dtrack: a system to predict and track internet path changes. IEEE/ACM Trans. Networking 22(4), 1025–1038 (2014)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Donnet, B., Raoult, P., Friedman, T., Crovella, M.: Efficient algorithms for large-scale topology discovery. In: Eager, D.L., Williamson, C.L., Borst, S.C., Lui, J.C.S. (eds.) Proceedings of the International Conference on Measurements and Modeling of Computer Systems, SIGMETRICS, 6–10 June 2005, Banff, Alberta, Canada, pp. 327–338. ACM (2005)Google Scholar
  7. 7.
    Haddadi, H., Uhlig, S., Moore, A.W., Mortier, R., Rio, M.: Modeling internet topology dynamics. Comput. Commun. Rev. 38(2), 65–68 (2008)CrossRefGoogle Scholar
  8. 8.
    Latapy, M., Magnien, C., Ouédraogo, F.: A radar for the internet. In: Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 15–19 December 2008, Pisa, Italy, pp. 901–908. IEEE Computer Society (2008)Google Scholar
  9. 9.
    Magnien, C., Ouedraogo, F., Valadon, G., Latapy, M.: Fast dynamics in internet topology: observations and first explanations. In: Proceedings of the 2009 Fourth International Conference on Internet Monitoring and Protection, ICIMP 2009, pp. 137–142. IEEE Computer Society, Washington, DC (2009)Google Scholar
  10. 10.
    Marchetta, P., Pescape, A.: Drago: detecting, quantifying and locating hidden routers in traceroute ip paths. In: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 109–114, April 2013Google Scholar
  11. 11.
    Medem, A., Magnien, C., Tarissan, F.: Impact of power-law topology on ip-level routing dynamics: simulation results. In: 2012 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 220–225, March 2012Google Scholar
  12. 12.
    Ni, J., Xie, H., Tatikonda, S., Yang, Y.R.: Efficient and dynamic routing topology inference from end-to-end measurements. IEEE/ACM Trans. Networking 18(1), 123–135 (2010)CrossRefGoogle Scholar
  13. 13.
    Oliveira, R.V., Zhang, B., Zhang, L.: Observing the evolution of internet as topology. SIGCOMM Comput. Commun. Rev. 37(4), 313–324 (2007)CrossRefGoogle Scholar
  14. 14.
    Pansiot, J.-J.: Local and dynamic analysis of internet multicast router topology. Ann. Telecommun. 62(3–4), 408–425 (2007)Google Scholar
  15. 15.
    Park, S.-T., Pennock, D.M., Giles, C.L.: Comparing static and dynamic measurements and models of the internet’s as topology. In: IEEE Infocom (2004)Google Scholar
  16. 16.
    Paxson, V.: End-to-end internet packet dynamics. IEEETON 7(3), 277–292 (1999)Google Scholar
  17. 17.
    Viger, F., Augustin, B., Cuvellier, X., Orgogozo, B., Friedman, T., Latapy, M., Magnien, C., Teixeira, R.: Detection and prevention in internet graphs. Comput. Netw. 52, 998–1018 (2008)CrossRefzbMATHGoogle Scholar
  18. 18.
    Wang, F., Mao, Z.M., Wang, J., Gao, L., Bush, R.: A measurement study on the impact of routing events on end-to-end internet path performance. SIGCOMM Comput. Commun. Rev. 36(4), 375–386 (2006)CrossRefGoogle Scholar
  19. 19.
    Wang, X., Loguinov, D.: Wealth-based evolution model for the internet as-level topology. In: IEEE INFOCOM (2006)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Frédéric Tounwendyam Ouédraogo
    • 1
    Email author
  • Tegawendé Bissyandé
    • 2
  • Sawadogo Daouda
    • 3
  • Didier Bassolé
    • 2
  • Abdoulaye Séré
    • 4
  • Oumarou Sié
    • 2
  1. 1.Université de KoudougouKoudougouBurkina Faso
  2. 2.Université de OuagadougouOuagaBurkina Faso
  3. 3.Univeristé de La RochelleLa RochelleFrance
  4. 4.Université de Polytechnique de Bobo-DioulassoBoboBurkina Faso

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