## 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 language | American English |
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Title of host publication | INFOCOM 2018 - IEEE Conference on Computer Communications |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 2105-2113 |

Number of pages | 9 |

ISBN (Electronic) | 9781538641286 |

DOIs | |

State | Published - 8 Oct 2018 |

Event | 2018 IEEE Conference on Computer Communications, INFOCOM 2018 - Honolulu, United States Duration: 15 Apr 2018 → 19 Apr 2018 |

### Publication series

Name | Proceedings - IEEE INFOCOM |
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Volume | 2018-April |

ISSN (Print) | 0743-166X |

### Conference

Conference | 2018 IEEE Conference on Computer Communications, INFOCOM 2018 |
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Country/Territory | United States |

City | Honolulu |

Period | 15/04/18 → 19/04/18 |

### Bibliographical note

Funding Information:Part of this work has been supported by COST Action CA15127 (RE-CODIS), and the Hungarian Scientific Research Fund (grant No. OTKA K124171 and K115288).

Publisher Copyright:

© 2018 IEEE.