High Speed Elephant Flow Detection Under Partial Information

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 flows on the network with very high probability.

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