Abstract
Super points detection plays an important role in network research and application. With the increase of network scale, distributed super points detection has become a hot research topic. The key point of super points detection in a multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication algorithm to detect super points in a distributed environment, Rough Estimator based Asynchronous Distributed super points 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 points. Meanwhile, the famous Linear Estimator (LE) is applied 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. This paper proves that the accuracy of READ in a 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 high accuracy in a distributed environment, but also has less than 5% of communication burden compared with existing algorithms.
Highlights
The Internet is one of the most important infrastructures of the modern information society
In order to test the performance of READ, four groups of high-speed network traffic are used to carry out experiments
Because rough estimator (RE) has the characteristics of small memory occupation and fast computing speed, REC can generate candidate super points from 40 Gb/s high-speed network with only 3 MB of memory
Summary
The Internet is one of the most important infrastructures of the modern information society. With the rapid development of China’s economy, the bandwidth of the core network is increasing year by year. According to the latest statistics of China Internet Information. It is a worldwide problem to manage such a large-scale network effectively and ensure its safe operation. In the face of a complex network environment, the monitoring and protection of the backbone network is the most important and basic step [2]. Internet management under the condition of large data-level network traffic is a hot research subject, which can be carried out from different aspects at the industrial and academic levels. To pay more attention to some core hosts in the network is a way to improve the efficiency of network management [3]
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