170030
20190316235227.0
CONF
Shifting Network Tomography Toward A Practical Goal
2011
2011
Conference Papers
Boolean 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).
network tomography
network monitoring
link inference
congestion probability
Ghita, Denisa
173437
242764
Karakus, Can
Argyraki, Katerina
176638
243542
Thiran, Patrick
103925
240373
ACM International Conference on emerging Networking EXperiments and Technologies (CoNext)
Tokyo, Japan
December 6–9, 2011
Proceedings of the ACM International Conference on emerging Networking EXperiments and Technologies (CoNext)
n/a
367352
n/a
http://infoscience.epfl.ch/record/170030/files/main_1.pdf
NAL
252412
U12550
LCA
252614
US00024
LCA3
252454
U10431
oai:infoscience.tind.io:170030
IC
conf
GLOBAL_SET
173437
173437
173437
176638
176638
176638
176638
176638
EPFL-CONF-170030
EPFL
PUBLISHED
REVIEWED
CONF