TY - GEN
T1 - A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters
AU - Tapolcai, Janos
AU - Vass, Balazs
AU - Heszberger, Zalan
AU - Biro, Jozsef
AU - Hay, David
AU - Kuipers, Fernando A.
AU - Ronyai, Lajos
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056161168&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2018.8486218
DO - 10.1109/INFOCOM.2018.8486218
M3 - منشور من مؤتمر
T3 - Proceedings - IEEE INFOCOM
SP - 2105
EP - 2113
BT - INFOCOM 2018 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Computer Communications, INFOCOM 2018
Y2 - 15 April 2018 through 19 April 2018
ER -