000170030 001__ 170030
000170030 005__ 20190316235227.0
000170030 037__ $$aCONF 000170030 245__$$aShifting Network Tomography Toward A Practical Goal
000170030 269__ $$a2011 000170030 260__$$c2011
000170030 336__ $$aConference Papers 000170030 520__$$aBoolean 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).
000170030 6531_ $$anetwork tomography 000170030 6531_$$anetwork monitoring
000170030 6531_ $$alink inference 000170030 6531_$$acongestion probability
000170030 700__ $$0242764$$aGhita, Denisa$$g173437 000170030 700__$$aKarakus, Can
000170030 700__ $$0243542$$aArgyraki, Katerina$$g176638 000170030 700__$$0240373$$aThiran, Patrick$$g103925
000170030 7112_ $$aACM International Conference on emerging Networking EXperiments and Technologies (CoNext)$$cTokyo, Japan$$dDecember 6–9, 2011 000170030 773__$$tProceedings of the ACM International Conference on emerging Networking EXperiments and Technologies (CoNext)
000170030 8564_ $$s367352$$uhttps://infoscience.epfl.ch/record/170030/files/main_1.pdf$$yn/a$$zn/a
000170030 909C0 $$0252412$$pNAL$$xU12550 000170030 909C0$$0252614$$pLCA$$xUS00024
000170030 909C0 $$0252454$$pLCA3$$xU10431 000170030 909CO$$ooai:infoscience.tind.io:170030$$pconf$$pIC$$qGLOBAL_SET 000170030 917Z8$$x173437
000170030 917Z8 $$x173437 000170030 917Z8$$x173437
000170030 917Z8 $$x176638 000170030 917Z8$$x176638
000170030 917Z8 $$x176638 000170030 917Z8$$x176638
000170030 917Z8 $$x176638 000170030 937__$$aEPFL-CONF-170030
000170030 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED 000170030 980__$$aCONF