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DTSTART;TZID=America/Chicago:20181111T113000
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UID:submissions.supercomputing.org_SC18_sess159_ws_indis104@linklings.com
SUMMARY:Fast Detection of Elephant Flows with Dirichlet-Categorical Infere
nce
DESCRIPTION:Workshop\nArchitectures, Networks, Security, Workshop Reg Pass
\n\nFast Detection of Elephant Flows with Dirichlet-Categorical Inference\
n\nGudibanda, Ros-Giralt, Commike, Lethin\n\nThe problem of elephant flow
detection is a longstanding research area with the goal of quickly identif
ying flows in a network that are large enough to affect the quality of ser
vice of smaller flows. Past work in this field has largely been either dom
ain-specific, based on thresholds for a specific flow size metric, or requ
ired several hyperparameters, reducing their ease of adaptation to the gre
at variety of traffic distributions present in real-world networks. In thi
s paper, we present an approach to elephant flow detection that avoids the
se limitations, utilizing the rigorous framework of Bayesian inference. By
observing packets sampled from the network, we use Dirichlet-Categorical
inference to calculate a posterior distribution that explicitly captures o
ur uncertainty about the sizes of each flow. We then use this posterior di
stribution to find the most likely subset of elephant flows under this pro
babilistic model. Our algorithm rapidly converges to the optimal sampling
rate at a speed O(1/n), where n is the number of packet samples received,
and the only hyperparameter required is the targeted detection likelihood,
defined as the probability of correctly inferring all the elephant flows.
Compared to the state-of-the-art based on static sampling rate, we show a
reduction in error rate by a factor of 20 times. The proposed method of D
irichlet-Categorical inference provides a novel, powerful framework to ele
phant flow detection that is both highly accurate and probabilistically me
aningful.
URL:https://sc18.supercomputing.org/presentation/?id=ws_indis104&sess=sess
159
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