TY - JOUR

T1 - Probabilistic Shared Risk Link Groups Modeling Correlated Resource Failures Caused by Disasters

AU - Vass, Balazs

AU - Tapolcai, János

AU - Heszberger, Zalan

AU - Biro, Jozsef

AU - Hay, David

AU - Kuipers, F.A.

AU - Oostenbrink, J.

AU - Valentini, Alessandro

AU - Ronyai, Lajos

PY - 2021

Y1 - 2021

N2 - 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. In this paper, we build 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 then we extend our model to capture also node failures. With such a model, one can quickly extract 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, etc. 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, in polynomial time, a quasilinear-sized data structure, 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.

AB - 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. In this paper, we build 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 then we extend our model to capture also node failures. With such a model, one can quickly extract 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, etc. 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, in polynomial time, a quasilinear-sized data structure, 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.

KW - Computational modeling

KW - Correlation

KW - Data structures

KW - Hazards

KW - Probabilistic logic

KW - Standards

KW - Stochastic processes

KW - see

UR - http://www.scopus.com/inward/record.url?scp=85102629186&partnerID=8YFLogxK

U2 - https://doi.org/10.1109/JSAC.2021.3064652

DO - https://doi.org/10.1109/JSAC.2021.3064652

M3 - Article

JO - IEEE Journal on Selected Areas in Communications

JF - IEEE Journal on Selected Areas in Communications

SN - 0733-8716

ER -