000201580 001__ 201580
000201580 005__ 20190812205806.0
000201580 0247_ $$2doi$$a10.1145/2619239.2626308
000201580 02470 $$2ISI$$a000350564600009
000201580 037__ $$aCONF
000201580 245__ $$aNetwork Neutrality Inference
000201580 269__ $$a2014
000201580 260__ $$c2014
000201580 336__ $$aConference Papers
000201580 520__ $$aWhen can we reason about the neutrality of a network based on external observations? We prove conditions under which it is possible to (a) detect neutrality violations and (b) localize them to specific links, based on external observations. Our insight is that, when we make external observations from different vantage points, these will most likely be inconsistent with each other if the network is not neutral. Where existing tomographic techniques try to form solvable systems of equations to infer network properties, we try to form unsolvable systems that reveal neutrality violations. We present an algorithm that relies on this idea to identify sets of non-neutral links based on external observations, and we show, through network emulation, that it achieves good accuracy for a variety of network conditions.
000201580 6531_ $$aNetwork neutrality
000201580 6531_ $$aNetwork tomography
000201580 700__ $$0246743$$g230100$$aZhang, Zhiyong
000201580 700__ $$0245787$$g188002$$aMara, Ovidiu Sebastian
000201580 700__ $$aArgyraki, Katerina$$g176638$$0243542
000201580 7112_ $$dAugust 19-21, 2014$$cChicago, Illinois, USA$$aACM SIGCOMM Conference
000201580 773__ $$tProceedings of the ACM SIGCOMM Conference
000201580 8564_ $$zPublisher's version$$yPublisher's version$$uhttps://infoscience.epfl.ch/record/201580/files/neut-sigcom14.pdf$$s358011
000201580 909C0 $$xU12550$$pNAL$$0252412
000201580 909CO $$ooai:infoscience.tind.io:201580$$qGLOBAL_SET$$pconf$$pIC
000201580 917Z8 $$x176638
000201580 917Z8 $$x176638
000201580 917Z8 $$x176638
000201580 937__ $$aEPFL-CONF-201580
000201580 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000201580 980__ $$aCONF