A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters

J. Tapolcai, Balazs Vass, Zalan Heszberger, Jozsef Biro, David Hay, Fernando A. Kuipers, L. Rónyai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationINFOCOM 2018 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2105-2113
Number of pages9
Volume2018-April
ISBN (Electronic)9781538641286
DOIs
Publication statusPublished - Oct 8 2018
Event2018 IEEE Conference on Computer Communications, INFOCOM 2018 - Honolulu, United States
Duration: Apr 15 2018Apr 19 2018

Other

Other2018 IEEE Conference on Computer Communications, INFOCOM 2018
CountryUnited States
CityHonolulu
Period4/15/184/19/18

Fingerprint

Stochastic models
Disasters
Availability
Probability distributions
Data structures
Hazards
Polynomials

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

Cite this

Tapolcai, J., Vass, B., Heszberger, Z., Biro, J., Hay, D., Kuipers, F. A., & Rónyai, L. (2018). A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters. In INFOCOM 2018 - IEEE Conference on Computer Communications (Vol. 2018-April, pp. 2105-2113). [8486218] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2018.8486218

A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters. / Tapolcai, J.; Vass, Balazs; Heszberger, Zalan; Biro, Jozsef; Hay, David; Kuipers, Fernando A.; Rónyai, L.

INFOCOM 2018 - IEEE Conference on Computer Communications. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. p. 2105-2113 8486218.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tapolcai, J, Vass, B, Heszberger, Z, Biro, J, Hay, D, Kuipers, FA & Rónyai, L 2018, A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters. in INFOCOM 2018 - IEEE Conference on Computer Communications. vol. 2018-April, 8486218, Institute of Electrical and Electronics Engineers Inc., pp. 2105-2113, 2018 IEEE Conference on Computer Communications, INFOCOM 2018, Honolulu, United States, 4/15/18. https://doi.org/10.1109/INFOCOM.2018.8486218
Tapolcai J, Vass B, Heszberger Z, Biro J, Hay D, Kuipers FA et al. A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters. In INFOCOM 2018 - IEEE Conference on Computer Communications. Vol. 2018-April. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2105-2113. 8486218 https://doi.org/10.1109/INFOCOM.2018.8486218
Tapolcai, J. ; Vass, Balazs ; Heszberger, Zalan ; Biro, Jozsef ; Hay, David ; Kuipers, Fernando A. ; Rónyai, L. / A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters. INFOCOM 2018 - IEEE Conference on Computer Communications. Vol. 2018-April Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2105-2113
@inproceedings{f75e3418132c46c8a51ec34b53041600,
title = "A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters",
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.",
author = "J. Tapolcai and Balazs Vass and Zalan Heszberger and Jozsef Biro and David Hay and Kuipers, {Fernando A.} and L. R{\'o}nyai",
year = "2018",
month = "10",
day = "8",
doi = "10.1109/INFOCOM.2018.8486218",
language = "English",
volume = "2018-April",
pages = "2105--2113",
booktitle = "INFOCOM 2018 - IEEE Conference on Computer Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A Tractable Stochastic Model of Correlated Link Failures Caused by Disasters

AU - Tapolcai, J.

AU - Vass, Balazs

AU - Heszberger, Zalan

AU - Biro, Jozsef

AU - Hay, David

AU - Kuipers, Fernando A.

AU - Rónyai, L.

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

UR - http://www.scopus.com/inward/citedby.url?scp=85056161168&partnerID=8YFLogxK

U2 - 10.1109/INFOCOM.2018.8486218

DO - 10.1109/INFOCOM.2018.8486218

M3 - Conference contribution

AN - SCOPUS:85056161168

VL - 2018-April

SP - 2105

EP - 2113

BT - INFOCOM 2018 - IEEE Conference on Computer Communications

PB - Institute of Electrical and Electronics Engineers Inc.

ER -