Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | IEEE INFOCOM | Data structure,Joint probability distribution,Computer science,Stochastic process,Network administrator,Stochastic modelling,Time complexity,Distributed computing |
DocType | ISSN | Citations |
Conference | 0743-166X | 0 |
PageRank | References | Authors |
0.34 | 18 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| János Tapolcai | 1 | 364 | 41.42 |
| Balazs Vass | 2 | 8 | 2.83 |
| Zalán Heszberger | 3 | 63 | 9.15 |
| József Bíró | 4 | 89 | 18.01 |
| David Hay | 5 | 185 | 12.25 |
| Fernando Kuipers | 6 | 143 | 10.78 |
| Lajos Rónyai | 7 | 397 | 52.05 |