High-performance algorithms and data structures to catch elephant flows

Ros-Giralt, Jordi; Commike, Alan; Lethin, Richard; Maji, Sourav; Veeraraghavan, Malathi

doi:10.1109/hpec.2016.7761600

Cited by 2 publications

(2 citation statements)

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“…Detection under partial information. The detection likelihood of a network at time follows a multivariate t hypergeometric distribution as follows: (4) where is the zero quantum error region, expressed as: (4) and (5) is equivalent to Equation (2).…”

Section: B Generalization To Arbitrary Distributionsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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High Speed Elephant Flow Detection Under Partial Information

Ros-Giralt

Commike

Maji

et al. 2018

2018 International Symposium on Networks, Computers and Communications (ISNCC)

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In this paper we introduce a new framework to detect elephant flows at very high speed rates and under uncertainty. The framework provides exact mathematical formulas to compute the detection likelihood and introduces a new flow Reconstruction Lemma under partial information. These theoretical results lead to the design of BubbleCache, a new elephant flow detection algorithm designed to operate near the optimal tradeoff between computational scalability and accuracy by dynamically tracking the traffic's natural cutoff sampling rate. We demonstrate on a real world 100 Gbps network that the BubbleCache algorithm helps reduce the computational cost by a factor of 1000 and the memory requirements by a factor of 100 while detecting the top largest flows on the network with very high probability.

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Section: B Generalization To Arbitrary Distributionsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%