Network outages have significant economic and societal costs. While network operators have become adept at managing smaller failures, this is not the case for larger, regional failures such as natural disasters. Although it is not possible, and certainly not economic, to prevent all potential disaster damage and impact, we can reduce their impact by adding cost-efficient, geographically redundant, cable connections to the network.In this paper, we provide algorithms for finding cost-efficient, disaster-aware cable routes based on empirical hazard data. In contrast to previous work, our approach finds disaster-aware routes by considering the impact of a large set of input disasters on the network as a whole, as well as on the individual cable. For this, we propose the Disaster-Aware Network Augmentation Problem of finding a new cable connection that minimizes a function of disaster impact and cable cost. We prove that this problem is NP-hard and give an exact algorithm, as well as a heuristic, for solving it. Our algorithms are applicable to both planar and geographical coordinates. Using actual seismic hazard data, we demonstrate that by applying our algorithms, network operators can cost-efficiently raise the resilience of their network and future cable connections.

To evaluate the expected availability of a backbone network service, the administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single component failures is often insufficient. This paper builds a stochastic model of geographically correlated link failures caused by disasters to estimate the hazards an optical backbone network may be prone to and to understand the complex correlation between possible link failures. We first consider link failures only and later extend our model also to capture node failures. With such a model, one can quickly extract essential information such as the probability of an arbitrary set of network resources to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a disaster. Furthermore, we introduce standard data structures and a unified terminology on Probabilistic Shared Risk Link Groups (PSRLGs), along with a pre-computation process, which represents the failure probability of a set of resources succinctly. In particular, we generate a quasilinear-sized data structure in polynomial time, which allows the efficient computation of the cumulative failure probability of any set of network elements. Our evaluation is based on carefully pre-processed seismic hazard data matched to real-world optical backbone network topologies.

It is often assumed that a network will not be struck by multiple disasters in a relatively short period of time; that is, a subsequent disaster will not strike within the recovery phase of a previous disaster. However, recent events have shown that combinations of disasters are not implausible. This realization calls for a new perspective on how we assess the vulnerability of our networks and shows a need for a framework to assess the vulnerability of networks to successive independent disasters. We propose a network and disaster model capable of modeling a sequence of disasters in time, while taking into account recovery operations. Based on that model, we develop both an exact and a Monte Carlo method to compute the vulnerability of a network to successive disasters. By applying our approach to real empirical disaster data, we show that the probability of a second disaster striking the network during recovery can be significant even for short repair times. Our framework is a first step towards determining the vulnerability of networks to such successive disasters.