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A super point is a host that interacts with a far larger number ofcounterparts in the network over a period of time. Super point detection playsan important role in network research and application. With the increase ofnetwork scale, distributed super point detection has become a hot researchtopic. Compared with single-node super point detection algorithm, thedifficulty of super point detection in multi-node distributed environment ishow to reduce communication overhead. Therefore, this paper proposes athree-stage communication distributed super point detection algorithm: RoughEstimator based Asynchronous Distributed super point detection algorithm(READ). READ uses a lightweight estimator, the Rough Estimator (RE), which isfast in computation and takes less memory to generate candidate super point. Atthe same time, the Linear Estimator (LE) is used to accurately estimate thecardinality of each candidate super point, so as to detect the super pointcorrectly. In READ, each node scans IP address pairs asynchronously. Whenreaching the time window boundary, READ starts three-stage communication todetect the super point. In this paper, we proof that the accuracy of READ indistributed environment is no less than that in the single node environment.Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic areused to test READ. The experimental results show that READ not only has higheraccuracy in distributed environment, but also has less than 5% of communicationburden compared with existing algorithms.

READ: a three-communicating-stage distributed super points detections algorithm