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Super point is a special kind of host whose cardinality, the number ofcontacting hosts in a certain period, is bigger than a threshold. Super pointcardinality estimation plays important roles in network field. This paperproposes a super point cardinality estimation algorithm under sliding timewindow. To maintain the state of previous hosts with few updating operations, anovel counter, asynchronous time stamp (AT), is proposed. For a sliding timewindow containing k time slices, AT only needs to be updated every k timeslices at the cost of 1 more bit than a previous state-of-art counter whichrequires $log_2(k+1)$ bits but updates every time slice. Fewer updatingoperations mean that more AT could be contained to acquire higher accuracy inreal-time. This paper also devises a novel reversible hash function scheme torestore super point from a pool of AT. Experiments on several real-worldnetwork traffic illustrate that the algorithm proposed in this paper coulddetect super points and estimate their cardinalities under sliding time windowin real time.

Distributed super point cardinality estimation under sliding time window for high speed network