The Resilience of WDM Networks to Probabilistic Geographical Failures

TLDR

Telecommunications, especially optical WDM networks, are vulnerable to large‑scale failures from attacks or natural disasters that occur at specific geographic locations and whose effects cannot be precisely predicted. The authors propose a unified framework that models network vulnerability when the failure event has a probabilistic nature defined by an arbitrary probability density function. The framework handles multiple simultaneous attacks, dependent subcomponents, and 1+1 or 1:1 protection plans, and employs computational geometry to efficiently locate vulnerable points under various metrics. Numerical experiments on real backbone networks show the framework’s applicability, identify critical locations needing extra protection, and demonstrate that computational geometry substantially enhances understanding of network resilience.

Abstract

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.