A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters
János Tapolcai (Budapest University of Technology and Economics)
Balazs Vass (Budapest University of Technology and Economics)
Zalan Heszberger (Budapest University of Technology and Economics)
Jozsef Biro (Budapest University of Technology and Economics)
David Hay (The Hebrew University of Jerusalem)
F.A. Kuipers (TU Delft - Embedded Systems)
Lajos Ronyai (Computer and Automation Research Institute Hungarian Academy of Sciences, Budapest University of Technology and Economics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
In order to evaluate the expected availability of a service, a network 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 link failures is often insufficient. In this paper, we build a stochastic model of geographically correlated link failures caused by disasters, in order to estimate the hazards a network may be prone to, and to understand the complex correlation between possible link failures. With such a model, one can quickly extract information, such as the probability of an arbitrary set of links to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a failure, etc. Furthermore, we introduce a pre-computation process, which enables us to succinctly represent the joint probability distribution of link failures. In particular, we generate, in polynomial time, a quasilinear-sized data structure, with which the joint failure probability of any set of links can be computed efficiently.