Distributed Computing

, Volume 28, Issue 2, pp 91–109 | Cite as

Adversarial topology discovery in network virtualization environments: a threat for ISPs?

  • Yvonne Anne Pignolet
  • Stefan Schmid
  • Gilles Tredan


Network virtualization is a new Internet paradigm which allows multiple virtual networks (VNets) to share the resources of a given physical infrastructure. The virtualization of entire networks is the natural next step after the virtualization of nodes and links. While the problem of how to embed a VNet (“guest network”) on a given resource network (“host network”) is algorithmically well-understood, much less is known about the security implications of this new technology. This paper introduces a new model to reason about one particular security threat: the leakage of information about the physical infrastructure—often a business secret. We initiate the study of this new problem and introduce the notion of request complexity which describes the number of VNet requests needed to fully disclose the substrate topology. We derive lower bounds and present algorithms achieving an asymptotically optimal request complexity for important graph classes such as trees, cactus graphs (complexity \(O(n)\)) as well as arbitrary graphs (complexity \(O(n^2)\)). Moreover, a general motif-based topology discovery framework is described which exploits the poset structure of the VNet embedding relation.



We would like to thank Georgios Smaragdakis from Telekom Innovation Laboratories for interesting discussions. Part of this work was performed within the Virtu project, funded by NTT DOCOMO Euro-Labs, and the Collaborative Networking project, funded by Deutsche Telekom AG. We would like to thank all our colleagues in these projects


  1. 1.
    Acharya, H., Gouda, M.: On the hardness of topology inference. In: Proceedings of ICDCN, pp. 251–262 (2011)Google Scholar
  2. 2.
    Achlioptas, D., Clauset, A., Kempe, D., Moore, C.: On the bias of traceroute sampling: or, power-law degree distributions in regular graphs. In: Proceedings of 37th Annual ACM Symposium on Theory of Computing (STOC), pp. 694–703 (2005)Google Scholar
  3. 3.
    Ambühl, C., Mastrolilli, M., Svensson, O.: Inapproximability results for maximum edge biclique, minimum linear arrangement, and sparsest cut. SIAM J. Comput. 40(2), 567–596 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Anandkumar, A., Hassidim, A., Kelner, J.: Topology discovery of sparse random graphs with few participants. In: Proceedings of SIGMETRICS (2011)Google Scholar
  5. 5.
    Bansal, N., Lee, K.-W., Nagarajan, V., Zafer, M.: Minimum congestion mapping in a cloud. In: Proceedings of 30th PODC, pp. 267–276 (2011)Google Scholar
  6. 6.
    Caucal, D.: Deterministic graph grammars. In: Logic and Automata, pp. 169–250 (2008)Google Scholar
  7. 7.
    Cheswick, B., Burch, H., Branigan, S.: Mapping and visualizing the internet. In: Proceedings of USENIX Annual Technical Conference (ATEC) (2000)Google Scholar
  8. 8.
    Chowdhury, N.M.M.K., Boutaba, R.: A survey of network virtualization. Comput. Netw. 54(5), 862–876 (2010). doi: 10.1016/j.comnet.2009.10.017 Google Scholar
  9. 9.
    Cook, D.J., Holder, L.B.: Substructure discovery using minimum description length and background knowledge. J. Artif. Intell. Res. 1, 231–255 (1994)Google Scholar
  10. 10.
    Díaz, J., Petit, J., Serna, M.: A survey of graph layout problems. ACM Comput. Surv. 34(3), 313–356 (2002)CrossRefGoogle Scholar
  11. 11.
    Even, G., Medina, M., Schaffrath, G., Schmid, S.: Competitive and deterministic embeddings of virtual networks. In: Proceedings of ICDCN (2012)Google Scholar
  12. 12.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of SIGCOMM, pp. 251–262 (1999)Google Scholar
  13. 13.
    Fan, J., Ammar, M.H.: Dynamic topology configuration in service overlay networks: a study of reconfiguration policies. In: Proceedings of IEEE INFOCOM (2006) Google Scholar
  14. 14.
    Fuerst, C., Schmid, S., Feldmann, A.: Virtual network embedding with collocation: benefits and limitations of pre-clustering. In: Proceedings of 2nd IEEE International Conference on Cloud Networking (CLOUDNET) (2013)Google Scholar
  15. 15.
    Haider, A., Potter, R., Nakao, A.: Challenges in resource allocation in network virtualization. In: Proceedings of ITC Specialist Seminar on Network Virtualization (2009)Google Scholar
  16. 16.
    Houidi, I., Louati, W., Zeghlache, D.: A distributed virtual network mapping algorithm. In: Proceedings of IEEE ICC (2008)Google Scholar
  17. 17.
    Lakhina, A., Byers, J., Crovella, M., Xie, P.: Sampling biases in ip topology measurements. In: Proceedings of IEEE INFOCOM (2003)Google Scholar
  18. 18.
    Lischka, J., Karl, H.: A virtual network mapping algorithm based on subgraph isomorphism detection. In: Proceedings of ACM SIGCOMM VISA (2009)Google Scholar
  19. 19.
    McKeown, N., Anderson, T., Balakrishnan, H., Parulkar, G., Peterson, L., Rexford, J., Shenker, S., Turner, J.: Openflow: enabling innovation in campus networks. SIGCOMM Comput. Commun. Rev. 38, 69–74 (2008)CrossRefGoogle Scholar
  20. 20.
    Pignolet, Y.A., Schmid, S., Tredan, G.: Brief announcement: Do vnet embeddings reveal isp topology? In: Proceedings of 26th International Symposium on Distributed Computing (DISC) (2012)Google Scholar
  21. 21.
    Pignolet, Y.-A., Schmid, S., Tredan, G.: Adversarial VNet embeddings: a threat for ISPs? In: IEEE INFOCOM (2013)Google Scholar
  22. 22.
    Pignolet, Y.A., Schmid, S., Tredan, G.: Request complexity of vnet topology extraction: dictionary-based attacks. In: International Conference on Networked Systems (NETYS) (2013)Google Scholar
  23. 23.
    Pignolet, Y.A., Tredan, G., Schmid, S.: Misleading stars: what cannot be measured in the internet?. In: Proceedings of DISC (2011)Google Scholar
  24. 24.
    Plotkin, J.M., Rosenthal, J.W.: How to obtain an asymptotic expansion of a sequence from an analytic identity satisfied by its generating function. J. Aust. Math. Soc. Ser. A (1994)Google Scholar
  25. 25.
    Ristenpart, T., Tromer, E., Shacham, H., Savage, S.: Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds. In: Proceedings of 16th ACM CCS, pp. 199–212 (2009)Google Scholar
  26. 26.
    Schaffrath, G., Schmid, S., Feldmann, A.: Optimizing long-lived cloudnets with migrations. In: Proceedings of IEEE/ACM UCC (2012)Google Scholar
  27. 27.
    Schaffrath, G., Werle, C., Papadimitriou, P., Feldmann, A., Bless, R., Greenhalgh, A., Wundsam, A., Kind, M., Maennel, O., Mathy, L.: Network virtualization architecture: proposal and initial prototype. In: Proceedings of ACM SIGCOMM VISA (2009)Google Scholar
  28. 28.
    Spring, N., Mahajan, R., Wetherall, D., Anderson, T.: Measuring isp topologies with rocketfuel. IEEE/ACM Trans. Netw. 12(1), 2–16 (2004)CrossRefGoogle Scholar
  29. 29.
    Washio, T., Motoda, H.: State of the art of graph-based data mining. SIGKDD Explor. Newsl. 5, 59–68 (2003)CrossRefGoogle Scholar
  30. 30.
    Yao, B., Viswanathan, R., Chang, F., Waddington, D.: Topology inference in the presence of anonymous routers. In: Proceedings of IEEE INFOCOM, pp. 353–363 (2003)Google Scholar
  31. 31.
    Zhang, S., Qian, Z., Wu, J., Lu, S.: An opportunistic resource sharing and topology-aware mapping framework for virtual networks. In: Proceedings of IEEE INFOCOM (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yvonne Anne Pignolet
    • 1
  • Stefan Schmid
    • 2
  • Gilles Tredan
    • 3
  1. 1.ABB Corporate ResearchDättwilSwitzerland
  2. 2.Telekom Innovation Laboratories and TU BerlinBerlinGermany
  3. 3.LAAS-CNRSToulouseFrance

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