TY - GEN
T1 - The resilience of WDM networks to probabilistic geographical failures
AU - Agarwal, Pankaj K.
AU - Efrat, Alon
AU - Ganjugunte, Shashidhara
AU - Hay, David
AU - Sankararaman, Swaminathan
AU - Zussman, Gil
PY - 2011
Y1 - 2011
N2 - Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures of 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 are not deterministic. Therefore, we provide a unified framework to model the 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, in which 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, thereby demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows for identifying locations which 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.
AB - Telecommunications networks, and in particular optical WDM networks, are vulnerable to large-scale failures of 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 are not deterministic. Therefore, we provide a unified framework to model the 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, in which 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, thereby demonstrating the applicability of our algorithms to real-world scenarios. Our novel approach allows for identifying locations which 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.
KW - Network survivability
KW - computational geometry
KW - geographic networks
KW - network protection
KW - optical networks
UR - http://www.scopus.com/inward/record.url?scp=79960864959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960864959&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2011.5934942
DO - 10.1109/INFCOM.2011.5934942
M3 - Conference contribution
AN - SCOPUS:79960864959
SN - 9781424499212
T3 - Proceedings - IEEE INFOCOM
SP - 1521
EP - 1529
BT - 2011 Proceedings IEEE INFOCOM
T2 - IEEE INFOCOM 2011
Y2 - 10 April 2011 through 15 April 2011
ER -