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Cardinality estimation algorithms receive a stream of elements, with possiblerepetitions, and return the number of distinct elements in the stream. Suchalgorithms seek to minimize the required memory and CPU resource consumption atthe price of inaccuracy in their output. In computer networks, cardinalityestimation algorithms are mainly used for counting the number of distinctflows, and they are divided into two categories: sketching algorithms andsampling algorithms. Sketching algorithms require the processing of allpackets, and they are therefore usually implemented by dedicated hardware.Sampling algorithms do not require processing of all packets, but they areknown for their inaccuracy. In this work we identify one of the major drawbacksof sampling-based cardinality estimation algorithms: their inability to adaptto changes in flow size distribution. To address this problem, we propose a newsampling-based adaptive cardinality estimation framework, which uses onlinemachine learning. We evaluate our framework using real traffic traces, and showsignificantly better accuracy compared to the best known sampling-basedalgorithms, for the same fraction of processed packets.

Cardinality Estimation in a Virtualized Network Device Using Online Machine Learning