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dc.contributor.authorGhita, D.en_US
dc.contributor.authorKarakuş, Canen_US
dc.contributor.authorArgyraki, K.en_US
dc.contributor.authorThiran, P.en_US
dc.coverage.spatialTokyo, Japanen_US
dc.date.accessioned2016-02-08T12:15:49Z
dc.date.available2016-02-08T12:15:49Z
dc.date.issued2011en_US
dc.identifier.urihttp://hdl.handle.net/11693/28266
dc.descriptionDate of Conference: 6–9 December 2011en_US
dc.descriptionConference Name: 7th International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2011en_US
dc.description.abstractBoolean Inference makes it possible to observe the congestion status of end-to-end paths and infer, from that, the congestion status of individual network links. In principle, this can be a powerful monitoring tool, in scenarios where we want to monitor a network without having direct access to its links. We consider one such real scenario: a Tier-1 ISP operator wants to monitor the congestion status of its peers. We show that, in this scenario, Boolean Inference cannot be solved with enough accuracy to be useful; we do not attribute this to the limitations of particular algorithms, but to the fundamental difficulty of the Inference problem. Instead, we argue that the "right" problem to solve, in this context, is compute the probability that each set of links is congested (as opposed to try to infer which particular links were congested when). Even though solving this problem yields less information than provided by Boolean Inference, we show that this information is more useful in practice, because it can be obtained accurately under weaker assumptions than typically required by Inference algorithms and more challenging network conditions (link correlations, non-stationary network dynamics, sparse topologies).en_US
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 7th International Conference on Emerging Networking Experiments and Technologies, CoNEXT 2011en_US
dc.relation.isversionofhttps://doi.org/10.1145/2079296.2079320en_US
dc.subjectCongestion statusen_US
dc.subjectIndividual networken_US
dc.subjectInference algorithmen_US
dc.subjectInference problemen_US
dc.subjectMonitoring toolsen_US
dc.subjectNetwork conditionen_US
dc.subjectNetwork dynamicsen_US
dc.subjectNetwork tomographyen_US
dc.subjectNonstationaryen_US
dc.subjectAlgorithmsen_US
dc.subjectInference enginesen_US
dc.titleShifting network tomography toward a practical goalen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.identifier.doi10.1145/2079296.2079320en_US
dc.publisherACMen_US


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