  A super point is a host that interacts with a far larger number of
counterparts in the network over a period of time. Super point detection plays
an important role in network research and application. With the increase of
network scale, distributed super point detection has become a hot research
topic. Compared with single-node super point detection algorithm, the
difficulty of super point detection in multi-node distributed environment is
how to reduce communication overhead. Therefore, this paper proposes a
three-stage communication distributed super point detection algorithm: Rough
Estimator based Asynchronous Distributed super point detection algorithm
(READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is
fast in computation and takes less memory to generate candidate super point. At
the same time, the Linear Estimator (LE) is used to accurately estimate the
cardinality of each candidate super point, so as to detect the super point
correctly. In READ, each node scans IP address pairs asynchronously. When
reaching the time window boundary, READ starts three-stage communication to
detect the super point. In this paper, we proof that the accuracy of READ in
distributed 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 are
used to test READ. The experimental results show that READ not only has higher
accuracy in distributed environment, but also has less than 5% of communication
burden compared with existing algorithms.
