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Alert-Based Network Reconfiguration and Data Evacuation

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Abstract

Network and service disruptions could have several causes ranging from software to hardware failures, due to malicious users and/or weather-related failures. While most of the failures are unpredictable, weather-based disasters such as tornadoes, hurricanes, wildfires or floods can be often predicted well in advance, which leaves enough time for operators to prepare their networks against the incoming threat. This chapter is devoted to explore recent research concepts related to the preparation of the network when such an alert associated with weather- or human-based incoming threats is an issue. We introduce techniques for data evacuation from data centres through traditional as well as satellite networks and discuss possible reconfiguration strategies of virtual software-defined networks in order to migrate the data, virtual machines and network resources to a disaster-safe area. We also provide a quantitative comparison of recovery approaches to select the most flexible strategy depending on the disaster’s time frame.

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Fig. 14.1
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[Reprinted] with permission from [14] ©The Optical Society

Fig. 14.3

[Reprinted] with permission from [14] ©The Optical Society

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Notes

  1. 1.

    We note here that depending on the technology, topology, communication end-points, computational resources, etc., the exact time threshold might vary. However, we expect that in transport network topologies it might be in this range for most connections.

  2. 2.

    \(\oplus \) denotes the exclusive or (XOR) operation (modulo 2 addition).

References

  1. Allen RM (2006) Probabilistic warning times for earthquake ground shaking in the San Francisco Bay area. Seismol Res Lett 77(3):371–376

    CrossRef  Google Scholar 

  2. Ayoub O, Huamani O, Musumeci F, Tornatore M (2019) Alert-based online virtual-machine migrations for disaster resilience. In: Proceedings of International Conference on the Design of Reliable Communication Networks

    Google Scholar 

  3. Ayoub O, Musumeci F, Tornatore M, Pattavina A (2010) Efficient routing and bandwidth assignment for inter-data-centre live virtual-machine migrations. IEEE/OSA J Opt Commun Netw 9(3):B12–B21

    CrossRef  Google Scholar 

  4. Babarczi P, Biczók G, Overby H, Tapolcai J, Soproni P (2013) Realization strategies of dedicated path protection: a bandwidth cost perspective. Elsevier Comput Netw 57(9):1974–1990

    CrossRef  Google Scholar 

  5. Babarczi P, Tapolcai J et al (2017) Diversity coding in two-connected networks. IEEE/ACM Trans Netw 25(4):2308–2319

    CrossRef  Google Scholar 

  6. Basta A, Blenk A, Hassine H, Kellerer W (2015) Towards a dynamic SDN virtualization layer: control path migration protocol. In: Proceedings of Network and Service Management (CNSM), pp 354–359

    Google Scholar 

  7. Benson T (2009) Understanding data centre traffic characteristics. In: Proceedings of the 1st ACM Workshop on Research on Enterprise Networking

    Google Scholar 

  8. Blenk A, Basta A, Kellerer W (2015) HyperFlex: an SDN virtualization architecture with flexible hypervisor function allocation. In: Proceedings of IFIP/IEEE Symposium on Integrated Network Management (IM), pp 397–405

    Google Scholar 

  9. Blenk A, Basta A, Zerwas J, Reisslein M, Kellerer W (2016) Control plane latency with SDN network hypervisors: the cost of virtualization. IEEE Trans Netw Serv Manag 13(3):366–380

    CrossRef  Google Scholar 

  10. Barritt B et al (2017) Operating a UAV mesh & Internet backhaul network using temporospatial SDN. Aerospace Conference, IEEE

    Google Scholar 

  11. Clark C et al (2005) Live migration of virtual machines. In: Proceedings of the 2nd Conference on Symposium on Networked Systems Design and Implementation, vol 2. USENIX Association

    Google Scholar 

  12. Creon L, Marshall W (2011) Improved orbit predictions using two-line elements. Adv Space Res 47(7):1107–1115

    CrossRef  Google Scholar 

  13. Dikbiyik F, Sahasrabuddhe L, Tornatore M, Mukherjee B (2012) Exploiting excess capacity to improve robustness of WDM mesh networks. IEEE/ACM Trans Netw 20(1):114–124

    CrossRef  Google Scholar 

  14. Ferdousi S, Tornatore M, Habib MF, Mukherjee B (2015) Rapid data evacuation for large-scale disasters in optical cloud networks [invited]. IEEE/OSA J Opt Commun Netw 7(12):B163–B172

    CrossRef  Google Scholar 

  15. Ford LR, Fulkerson DR (2009) Maximal flow through a network. Classic papers in combinatorics, pp 243–248

    Google Scholar 

  16. Foster JS Jr et al (2008) Report of the commission to assess the threat to the united states from electromagnetic pulse (EMP) attack: critical national infrastructures. DTIC Document

    Google Scholar 

  17. Gupta A, Mandal U, Chowdhury P, Tornatore M, Mukherjee B (2015) Cost-efficient live VM migration based on varying electricity cost in optical cloud networks. Photon Netw Commun 30(3):376–386

    CrossRef  Google Scholar 

  18. Hay D, Giaccone P (2009) Optimal routing and scheduling for deterministic delay tolerant networks. In: Sixth International Conference on Wireless On-demand Network Systems and Services, pp 27–34

    Google Scholar 

  19. Kellerer W, Basta A et al (2018) How to measure network flexibility? A proposal for evaluating softwarized networks. IEEE Commun Mag 2–8

    Google Scholar 

  20. Klügel, M, He M, Kellerer W, Babarczi P (2019) A mathematical measure for flexibility in communication networks. In: Proceedings of IFIP Networking Conference, pp 1–9

    Google Scholar 

  21. Knight S, Nguyen H, Falkner N, Bowden R, Roughan M (2011) The Internet topology zoo. IEEE J Sel Areas Commun 29(9):1765–1775

    CrossRef  Google Scholar 

  22. Lourenco RBR, Figueiredo GB, Tornatore M, Mukherjee B (2019) Data evacuation from data centers in disaster-affected regions through software-defined satellite networks. Comput Netw 148:88–100

    CrossRef  Google Scholar 

  23. Mukherjee B, Habib M, Dikbiyik F (2014) Network adaptability from disaster disruptions and cascading failures. IEEE Commun Mag 52(5):230–238

    CrossRef  Google Scholar 

  24. Rak J (2015) Resilient routing in communication networks. Springer, Berlin

    CrossRef  Google Scholar 

  25. Sherwood R, Gibb G, Yap K, Appenzeller G, Casado M, McKeown N, Parulkar G (2009) Flowvisor: a network virtualization layer. OpenFlow Switch Consortium Tech Rep 1:1–13

    Google Scholar 

  26. United States Geological Survey: Earthquake Early Earning. http://earthquake.usgs.gov/research/earlywarning/

  27. Wood T, Ramakrishnan K, Van Der Merwe J, Shenoy P (2010) Cloudnet: a platform for optimized wan migration of virtual machines. University of Massachusetts Technical Report TR-2010-002

    Google Scholar 

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Acknowledgements

This chapter is based on work from COST Action CA15127 (“Resilient communication services protecting end-user applications from disaster-based failures—RECODIS”) supported by COST (European Cooperation in Science and Technology).

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Correspondence to Massimo Tornatore .

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Tornatore, M. et al. (2020). Alert-Based Network Reconfiguration and Data Evacuation. In: Rak, J., Hutchison, D. (eds) Guide to Disaster-Resilient Communication Networks. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-44685-7_14

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  • DOI: https://doi.org/10.1007/978-3-030-44685-7_14

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