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