Probabilistic Shared Risk Link Groups Modeling Correlated Resource Failures Caused by Disasters

Abstract

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.

On the resilience of cellular networks: how can national roaming help?

“…Second, correlated regional failures represent failures in a spatial locality due to certain events, e.g., a thunderstorm in a smaller region or an earthquake or flood affecting a larger region. Failures on the backhaul transport network can also be considered in this category, as such failures affect multiple BSs in a certain region simultaneously [45], [46]. In this case, BSs located in the same region will be affected similarly.…”

The complexity landscape of disaster‐aware network extension problems

“…For each predefined disaster, a probability is also assigned, such that these probabilities are summed up to 1 in the input. This failure model is similar to Probabilistic Shared Risk Link Group failures, where the set of failure events with their probabilities is known in network planning [1,15,21,27,29].…”

“…Note that the failure probabilities of [22] are computed using stochastic models of the disasters, where the failed points form circular disks in the simulations of [27,29]. On the contrary, the network should be prepared for an infinite number of possible disaster scenarios in the worst-case analysis.…”

“…Several studies revealed how vulnerable the Internet backbone is to natural disasters, such as earthquakes, hurricanes, and tsunamis. Such disasters may destroy several nodes and links located in possibly a few hundred kilometers wide geographic areas [4, 7‐11, 13, 16, 19, 20, 23, 24, 29]. Consequently, extensive research has been conducted to address the challenge of deploying new network equipment, specifically fiber cables [18, 26, 31], with the aim of enhancing network resilience against natural disasters.…”

A Stochastic Planning Model for Improving Resilience of Distribution System Considering Master-Slave Distributed Generators and Network Reconfiguration

“…In addition, we presume that failure probabilities of poles are independent and all poles have the same fragility function. However, a few resilience researches consider the fact that the joint failure probabilities of multiple lines can be modeled [33].…”