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

Balazs Vass, János Tapolcai, Zalan Heszberger, Jozsef Biro, David Hay, F.A. Kuipers, J. Oostenbrink, Alessandro Valentini, Lajos Ronyai

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
JournalIEEE Journal on Selected Areas in Communications
Publication statusAccepted/In press - 2021


  • Computational modeling
  • Correlation
  • Data structures
  • Hazards
  • Probabilistic logic
  • Standards
  • Stochastic processes
  • see

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