Skip to main content
Springer Nature Link
Log in

Determining Geographic Vulnerabilities Using a Novel Impact Based Resilience Metric

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

Various natural and man-made disasters as well as major political events (like riots) have increased the importance of understanding geographic failures and how correlated failures impact networks. Since mission critical networks are overlaid as virtual networks over a physical network infrastructure forming multilayer networks, there is an increasing need for methods to analyze multilayer networks for geographic vulnerabilities. In this paper, we present a novel impact-based resilience metric. Our new metric uses ideas borrowed from performability to combine network impact with state probability to calculate a new metric called Network Impact Resilience. The idea is that the highest impact to the mission of a network should drive its resilience metric. Furthermore, we present a state space analysis method that analyzes multilayer networks for geographic vulnerabilities. To demonstrate the methods, the inability to provision a given number of upper layer services is used as the criteria for network failure. Mapping techniques for multilayer network states are presented. Simplifying geographic state mapping techniques to reduce enumeration costs are also presented and tested. Finally, these techniques are tested on networks of varying sizes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Standley, J., Brown, V., Comitz, P., Schoolfield, J.: SWIM segment 2 deployment and utilization in NextGen R&D programs. In: Integrated Communications, Navigation and Surveillance Conference (ICNS), 2012, pp. G8-1–G8-5 (2012)

  2. Sterbenz, J.P.G., Hutchison, D., Çetinkaya, E.K., Jabbar, A., Rohrer, J.P., Schöller, M., Smith, P.: Resilience and survivability in communication networks: strategies, principles, and survey of disciplines. Comput. Netw. 54(8), 1245–1265 (2010)

    Article  MATH  Google Scholar 

  3. Habib, M.F., Tornatore, M., Dikbiyik, F., Mukherjee, B.: Disaster survivability in optical communication networks. Comput. Commun. 36(6), 630–644 (2013)

    Article  Google Scholar 

  4. Banerjee, S., Shirazipourazad, S., Sen, A.: Design and analysis of networks with large components in presence of region-based faults. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–6. IEEE (2011)

  5. Li, R., Wang, X., Jiang, X.: Network survivability against region failure. In: 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1–6. IEEE (2011)

  6. Rahnamay-Naeini, M., Pezoa, J.E., Azar, G., Ghani, N., Hayat, M.M.: Modeling stochastic correlated failures and their effects on network reliability. In: 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), pp. 1–6. IEEE (2011)

  7. Neumayer, S., Modiano, E.: Network reliability with geographically correlated failures. In: 2010 Proceedings IEEE INFOCOM, pp. 1–9. IEEE (2010)

  8. Saito, H.: Analysis of geometric disaster evaluation model for physical networks. IEEE/ACM Trans. Netw. 23(6), 1777–1789 (2015)

    Article  Google Scholar 

  9. Agarwal, P.K., Efrat, A., Ganjugunte, S.K., Hay, D., Sankararaman, S., Zussman, G.: Network vulnerability to single, multiple, and probabilistic physical attacks. In: Military Communications Conference, 2010—MILCOM 2010, pp. 1824–1829. IEEE (2010)

  10. Agarwal, P.K., Efrat, A., Ganjugunte, S.K., Hay, D., Sankararaman, S., Zussman, G.: The resilience of wdm networks to probabilistic geographical failures. IEEE/ACM Trans. Netw.: TON 21(5), 1525–1538 (2013)

    Article  Google Scholar 

  11. Fumagalli, A., Valcarenghi, L.: Ip restoration vs. wdm protection: is there an optimal choice? IEEE Netw. 14(6), 34–41 (2000)

    Article  Google Scholar 

  12. Cholda, P., Tapolcai, J., Cinkler, T., Wajda, K., Jajszczyk, A.: Quality of resilience as a network reliability characterization tool. IEEE Netw. 23(2), 11–19 (2009)

    Article  Google Scholar 

  13. Medhi, D.: A unified approach to network survivability for teletraffic networks: models, algorithms and analysis. IEEE Trans. Commun. 42, 534–548 (1994)

    Article  Google Scholar 

  14. Medhi, D., Khurana, R.: Optimization and performance of network restoration schemes for wide-area teletraffic networks. J. Netw. Syst. Manag. 3, 265–294 (1995)

    Article  Google Scholar 

  15. Pióro, M., Medhi, D.: Routing, Flow, and Capacity Design in Communication and Computer Networks. Elsevier, Amsterdam (2004)

    MATH  Google Scholar 

  16. Vasseur, J.-P., Pickavet, M., Demeester, P.: Network Recovery: Protection and Restoration of Optical, SONET-SDH, IP, and MPLS. Elsevier, Amsterdam (2004)

    Google Scholar 

  17. Oikonomou, K.N., Sinha, R.K., Doverspike, R.D.: Multi-layer network performance and reliability analysis. Int. J. Interdiscip. Telecommun. Netw.: IJITN 1(3), 1–30 (2009)

    Google Scholar 

  18. Gardner, M.T., Beard, C., Medhi, D.: Using network measure to reduce state space enumeration in resilient networks. In: 2013 9th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 250–257. IEEE (2013)

  19. Lee, K., Modiano, E., Lee, H.-W.: Cross-layer survivability in wdm-based networks. IEEE/ACM Trans. Netw.: TON 19(4), 1000–1013 (2011)

    Article  Google Scholar 

  20. Pacharintanakul, P., Tipper, D.: Crosslayer survivable mapping in overlay-ip-wdm networks. In: 7th International Workshop on Design of Reliable Communication Networks, 2009. DRCN 2009, pp. 168–174. IEEE (2009)

  21. Oki, E., Matsuura, N., Shiomoto, K., Yamanaka, N.: A disjoint path selection scheme with shared risk link groups in gmpls networks. IEEE Commun. Lett. 6(9), 406–408 (2002)

    Article  Google Scholar 

  22. Lee, H.-W., Modiano, E., Lee, K.: Diverse routing in networks with probabilistic failures. IEEE/ACM Trans. Netw. 18(6), 1895–1907 (2010)

    Article  Google Scholar 

  23. Esmaeili, M., Peng, M., Khan, S., Finochietto, J., Jin, Y., Ghani, N.: Multi-domain dwdm network provisioning for correlated failures. In: Optical Fiber Communication Conference and Exposition (OFC/NFOEC), 2011 and the National Fiber Optic Engineers Conference, pp. 1–3. IEEE (2011)

  24. Diaz, O., Feng, X., Min-Allah, N., Khodeir, M., Peng, M., Khan, S., Ghani, N.: Network survivability for multiple probabilistic failures. IEEE Commun. Lett. 16(8), 1320–1323 (2012)

    Article  Google Scholar 

  25. Yu, H., Qiao, C., Anand, V., Liu, X., Di, H., Sun, G.: Survivable virtual infrastructure mapping in a federated computing and networking system under single regional failures. In: 2010 IEEE Global Telecommunications Conference (GLOBECOM 2010), pp. 1–6 (2010)

  26. Rizwanur Rahman, M., Svne, R.B.: Survivable virtual network embedding algorithms for network virtualization. IEEE Trans. Netw. Serv. Manag. 10(2), 105–118 (2013)

    Article  Google Scholar 

  27. Deepankar, M.: Network Reliability and Fault-Tolerance. Wiley Encyclopedia of Electrical and Electronics Engineering, Hoboken (1999)

    Google Scholar 

  28. Menascé, D.A., Almeida, V.: Capacity Planning for Web Services: Metrics, Models, and Methods. Prentice Hall PTR, Upper Saddle River (2001)

    Google Scholar 

  29. Colbourn, C.J.: Reliability issues in telecommunications network planning. In: Sansò, B., Soriano, P. (eds.) Telecommunications Network Planning, pp. 135–146. Springer (1999)

  30. Ball, M.O., Colbourn, C.J., Provan, J.S.: Network reliability. Handb. Oper. Res. Manag. Sci. 7, 673–762 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  31. Li, V.O.K., Silvester, J.A.: Performance analysis of networks with unreliable components. IEEE Trans. Commun. 32(10), 1105–1110 (1984)

    Article  Google Scholar 

  32. Jarvis, J.P., Shier, D.R.: An improved algorithm for approximating the performance of stochastic flow networks. INFORMS J. Comput. 8(4), 355–360 (1996)

    Article  MATH  Google Scholar 

  33. Dotson, W., Gobien, J.: A new analysis technique for probabilistic graphs. IEEE Trans. Circuits Syst. 26(10), 855–865 (1979)

    Article  MathSciNet  Google Scholar 

  34. Gomes, T., Craveirinha, J., Martins, L.: An efficient algorithm for sequential generation of failure states in a network with multi-mode components. Reliab. Eng. Syst Saf. 77(2), 111–119 (2002)

    Article  Google Scholar 

  35. Bigdeli, A., Tizghadam, A., Leon-Garcia, A.: Comparison of network criticality, algebraic connectivity, and other graph metrics. In: Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners, pp. 4. ACM (2009)

  36. Long, X., Tipper, D., Gomes, T.: Measuring the survivability of networks to geographic correlated failures. Opt. Switch. Netw. 14, 117–133 (2014)

    Article  Google Scholar 

  37. Çetinkaya, E.K., Alenazi, M.J.F., Cheng, Y., Peck, A.M., Sterbenz, J.P.G.: A comparative analysis of geometric graph models for modelling backbone networks. Opt. Switch. Netw. 14(Part 2), 95–106 (2014) (Special Issue on (RNDM) 2013)

  38. Fay, D., Haddadi, H., Thomason, A., Moore, A.W., Mortier, R., Jamakovic, A., Uhlig, S., Rio, M.: Weighted spectral distribution for internet topology analysis: theory and applications. IEEE/ACM Trans. Netw. 18(1), 164–176 (2010)

    Article  Google Scholar 

  39. Sterbenz, J.P.G., Cetinkaya, E.K., Hameed, M.A., Jabbar, A., Rohrer, J.P.: Modelling and analysis of network resilience. In: 2011 Third International Conference on Communication Systems and Networks (COMSNETS), pp. 1–10. IEEE (2011)

  40. Ahn, Y.-Y., Han, S., Kwak, H., Moon, S., Jeong, H.: Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th International Conference on World Wide Web, pp. 835–844. ACM (2007)

  41. Garbin, D., Shortle, J.: Critical thinking: moving from infrastructure protection to infrastructure resiliency. Critical Infrastructure Protection Program Discussion Paper Series (2007)

  42. Manzano, M., Calle, E., Torres-Padrosa, V., Segovia, J., Harle, D.: Endurance: a new robustness measure for complex networks under multiple failure scenarios. Comput. Netw. 57(17), 3641–3653 (2013)

    Article  Google Scholar 

  43. Menth, M., Duelli, M., Martin, R., Milbrandt, J.: Resilience analysis of packet-switched communication networks. IEEE/ACM Trans. Netw.: TON 17(6), 1950–1963 (2009)

    Article  Google Scholar 

  44. Gardner, M.T., Beard, C.: Evaluating geographic vulnerabilities in networks. In: 2011 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pp. 1–6. IEEE (2011)

  45. Gardner, M.T., May, R., Beard, C., Medhi, D.: Finding geographic vulnerabilities in multilayer networks using reduced network state enumeration. In: 2015 11th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 49–56. IEEE (2015)

  46. Orlowski, S., Wessäly, R., Pióro, M., Tomaszewski, A.: SNDlib 1.0-survivable network design library. Networks 55(3), 276–286 (2010)

    Google Scholar 

  47. Sterbenz, J.P.G., Cetinkaya, E.K., Hameed, M.A., Jabbar, A., Qian, S., Rohrer, J.P.: Evaluation of network resilience, survivability, and disruption tolerance: analysis, topology generation, simulation, and experimentation. Telecommun. Syst. 52(2), 705–736 (2013)

    Google Scholar 

  48. Sterbenz, J.P.G., Rohrer, J.P., Cetinkaya, E.K., Alenazi, M.J.F., Cosner, A., Rolfe, J.: KU-Topview Network Topology Tool, The University of Kansas. http://www.ittc.ku.edu/resilinets/maps/, [Online; accessed 30-October-2013] (2010)

  49. Gardner, M.T., Cheng, Y., May, R., Beard, C., Sterbenz, J., Medhi, D.: Creating network resilience against disasters using service level agreements. In : 2016 12th International Conference on the Design of Reliable Communication Networks (DRCN’2016), Paris, France, IEEE (2016)

Download references

Acknowledgments

This research is supported in part by the National Science Foundation under Grant No. CNS-1217736 and by the Federal Aviation Administration under Cooperative Agreement No. 11-G-0182.

Author information

Authors and Affiliations

  1. Department of Computer Science and Electrical Engineering, University of Missouri–Kansas City, 5110 Rockhill Road, Kansas City, MO, 64110, USA

    M. Todd Gardner, Rebecca May, Cory Beard & Deep Medhi

  2. Federal Aviation Administration, 901 Locust Street, Kansas City, MO, 64113, USA

    M. Todd Gardner

Authors
  1. M. Todd Gardner
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rebecca May
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Cory Beard
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Deep Medhi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to M. Todd Gardner.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gardner, M.T., May, R., Beard, C. et al. Determining Geographic Vulnerabilities Using a Novel Impact Based Resilience Metric. J Netw Syst Manage 24, 711–745 (2016). https://doi.org/10.1007/s10922-016-9383-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-016-9383-y

Keywords