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Super point is a kind of special host in the network which contacts with hugeof other hosts. Estimating its cardinality, the number of other hostscontacting with it, plays important roles in network management. But all ofexisting works focus on discrete time window super point cardinality estimationwhich has great latency and ignores many measuring periods. Sliding time windowmeasures super point cardinality in a finer granularity than that of discretetime window but also more complex. This paper firstly introduces an algorithmto estimate super point cardinality under sliding time window from distributededge routers. This algorithm's ability of sliding super point cardinalityestimating comes from a novel method proposed in this paper which can recordthe time that a host appears. Based on this method, two sliding cardinalityestimators, sliding rough estimator and sliding linear estimator, are devisedfor super points detection and their cardinalities estimation separately. Whenusing these two estimators together, the algorithm consumes the smallest memorywith the highest accuracy. This sliding super point cardinality algorithm canbe deployed in distributed environment and acquire the global super points'cardinality by merging estimators of distributed nodes. Both of theseestimators could process packets parallel which makes it becom possible to dealwith high speed network in real time by GPU. Experiments on a real worldtraffic show that this algorithm have the highest accuracy and the smallestmemory comparing with others when running under discrete time window. Undersliding time window, this algorithm also has the same performance as underdiscrete time window.

Memory efficient distributed sliding super point cardinality estimation by GPU