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Super point is a special host in network which communicates with lots ofother hosts in a certain time period. The number of hosts contacting with asuper point is called as its cardinality. Cardinality estimating playsimportant roles in network management and security. All of existing works focuson how to estimate super point's cardinality under discrete time window. Butdiscrete time window causes great delay and the accuracy of estimating resultis subject to the starting of the window. sliding time window, movingforwarding a small slice every time, offers a more accuracy and timely scale tomonitor super point's cardinality. On the other hand, super point's cardinalityestimating under sliding time window is more difficult because it requires analgorithm to record the cardinality incrementally and report them immediatelyat the end of the sliding duration. This paper firstly solves this problem bydevising a sliding time window available algorithm SRLA. SRLA records hostscardinality by a novel structure which could be updated incrementally. In orderto reduce the cardinality estimating time at the end of every sliding timewindow, SRLA generates a super point candidate list while scanning packets andcalculates the cardinality of hosts in the candidate list only. It also has theability to run parallel to deal with high speed network in line speed. Thispaper gives the way to deploy SRLA on a common GPU. Experiments on real worldtraffics which have 40 GB/s bandwidth show that SRLA successfully estimatessuper point's cardinality within 100 milliseconds under sliding time windowwhen running on a low cost Nvidia GPU, GTX650 with 1 GB memory. The estimatingtime of SRLA is much smaller than that of other algorithms which consumes morethan 2000 milliseconds under discrete time window.

SRLA: A real time sliding time window super point cardinality estimation algorithm for high speed network based on GPU