On incorporating dependent link failures in a traffic engineering model

Faragó, András; Unghváry, Ferenc; Fumagalli, A.

doi:10.1109/icc.2003.1204190

Cited by 1 publication

(3 citation statements)

References 3 publications

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“…Let be the NVC for the entire network, the NVC for the backbone with completely functional subnetworks (computed with flow ), and the NVCs within the th subnetwork. Then, for all (8) where is the probability that the Bernoulli attack eliminates none of the links in the direct path in the backbone from to . Under further modelling assumptions, this formula can be used to generate simpler identities and approximations.…”

Section: A Decomposition Of Vulnerabilitymentioning

confidence: 99%

“…Suppose in (8) , , , and as a common "within-subnetworks" NVC in proportion. If the backbone is distributed in the sum according to common proportions , , and , then (8) becomes (9) where is the total expected within-subnetworks flow.…”

Section: B Similar Subnetwork and Self-similar Networkmentioning

confidence: 99%

“…Farago et al [8] developed a method to compute the end-to-end blocking probability of a route based on dependent probabilities of failures of links in its path. Their methods are useful for the computation of the NVC in the presence of dependent link failures.…”

Section: A Related Workmentioning

confidence: 99%

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Measures of Network Vulnerability

Vardi

Zhang

2007

IEEE Signal Process. Lett.

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We propose measures of network vulnerability and study some of their properties. We define a network as a triplet ( ), where is the graph of the network, is the routing protocol specifying how traffic flows on , and is the traffic load (typically random). Based on this structure, we calculate the expected loss of traffic from an "attack" which eliminates 100% of the network's links. The result is a network vulnerability curve (NVC) measuring expected loss as a function of 0 1.Clearly, different attack strategies (e.g., random versus malicious) could cause different levels of damages so, accordingly, we define four key NVCs: malicious, greedy, random, and Bernoulli, and study their mathematical properties. Some extensions are discussed.

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Section: A Decomposition Of Vulnerabilitymentioning

confidence: 99%

Section: B Similar Subnetwork and Self-similar Networkmentioning

confidence: 99%