Measuring the survivability of networks to geographic correlated failures

Abstract

Wide area backbone communication networks are subject to a variety of hazards that can result in network component failures. Hazards such as power failures and storms can lead to geographical correlated failures. Recently there has been increasing interest in determining the ability of networks to survive geographic correlated failures and a number of measures to quantify the effects of failures have appeared in the literature. This paper proposes a the use of weighted spectrum to evaluate network survivability regarding geographic correlated failures. Further we conduct a comparative analysis by finding the most vulnerable geographic cuts or nodes in the network though solving an optimization problem to determine the cut with the largest impact for a number of measures in the literature as well as weighted spectrum. Numerical results on several sample network topologies show that the worst-case geographic cuts depend on the measure used in an unweighted or weighted graph. The proposed weighted spectrum measure is shown to be more versatile than other measures in both unweighted and weighted graphs.

List of shared risk link groups representing regional failures with limited size

“…(2) the point with the maximum average two terminal reliability between every node-pair [7], [21]- [23]. (3) the point with the maximum average all-terminal reliability [13], [22] which allows the identification of network areas that can disconnect any component in the network. (4) the point with the maximum average value of the maximum flow between given pair of nodes [7].…”

“…(4) the point with the maximum average value of the maximum flow between given pair of nodes [7]. (5) the point with maximal average shortest path length between every pair of nodes [13], [23], (6) survivability as a measure of weighted spectrum based on the eigenvalues of the normalized Laplacian of a graph [13], (7) network criticality which is determined from the trace of the inverse of the Laplacian matrix and can be related to the node and link betweenness [13].…”

A survey of strategies for communication networks to protect against large-scale natural disasters

“…Long et al [21] propose the use of weighted spectrum (WS) to evaluate network survivability regarding geographic correlated failures. Furthermore, a comparative analysis is conducted by solving an optimization problem to determine the cut with the largest impact for a number of measures in the literature (namely, Algebraic Connectivity, Network Criticality, Average Shortest Path, Network Diameter) as well as WS.…”

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

“…N = 3 is associated to the clustering coefficient, meanwhile N = 4 is related to the number of disjoint paths in a network. The network robustness is calculated as W 0 -W, where W denotes the default WS of the original graph and W 0 denotes the WS of the resulting graph after link or nodal failures [17].…”