4.5 Article

The Resilience of WDM Networks to Probabilistic Geographical Failures

期刊

IEEE-ACM TRANSACTIONS ON NETWORKING
卷 21, 期 5, 页码 1525-1538

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNET.2012.2232111

关键词

Computational geometry; geographic networks; network protection; network survivability; optical networks

资金

  1. NSF [CNS-05-40347, CCF-06-35000, IIS-07-13498, CCF-09-40671, CNS-1018379]
  2. ARO [W911NF-07-1-0376, W911NF-08-1-0452]
  3. NIH [1P50-GM-08183-01]
  4. DOE [OEG-P200A070505]
  5. U.S. Israel Binational Science Foundation
  6. NSF under CAREER Grant [0348000, CNS-1017714]
  7. DTRA [HDTRA1-09-1-0057]
  8. CIAN NSF ERC [EEC-0812072]
  9. Legacy Heritage Fund program of the Israel Science Foundation [1816/10]
  10. Israeli Centers of Research Excellence (I-CORE) program [4/11]
  11. Israeli Smart Grid (ISG) Consortium
  12. Division Of Computer and Network Systems
  13. Direct For Computer & Info Scie & Enginr [1017114, 1017800] Funding Source: National Science Foundation
  14. Division Of Computer and Network Systems
  15. Direct For Computer & Info Scie & Enginr [1018379] Funding Source: National Science Foundation

向作者/读者索取更多资源

Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures in their physical infrastructure, resulting from physical attacks (such as an electromagnetic pulse attack) or natural disasters (such as solar flares, earthquakes, and floods). Such events happen at specific geographical locations and disrupt specific parts of the network, but their effects cannot be determined exactly in advance. Therefore, we provide a unified framework to model network vulnerability when the event has a probabilistic nature, defined by an arbitrary probability density function. Our framework captures scenarios with a number of simultaneous attacks, when network components consist of several dependent subcomponents, and in which either a 1+1 or a 1:1 protection plan is in place. We use computational geometric tools to provide efficient algorithms to identify vulnerable points within the network under various metrics. Then, we obtain numerical results for specific backbone networks, demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows to identify locations that require additional protection efforts (e. g., equipment shielding). Overall, the paper demonstrates that using computational geometric techniques can significantly contribute to our understanding of network resilience.

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