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.@en

Guaranteeing a high availability of network services is a crucial part of network management. In this study, we show how to compute the availability of network services under earthquakes, by using empirical data. We take a multi-disciplinary approach and create an earthquake model based on seismological research and historical data. We then show how to integrate this empirical disaster model into existing network resiliency models to obtain the vulnerability and availability of a network under earthquakes. While previous studies have applied their models to ground shaking hazard models or earthquake scenarios, we compute (1) earthquake activity rates and (2) a relation between magnitude and disaster area, and use both as input data for our modeling. This approach is more in line with existing network resiliency models: it provides better information on the correlation between link failures than ground shaking hazard models and a more comprehensive view than a fixed set of scenarios. @en