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Efficient Heavy Hitters Identification over Speed Traffic Streams

Shuzhuang Zhang1, Hao Luo1, Zhigang Wu1, Yanbin Sun2, *, Yuhang Wang2, Tingting Yuan3
1 Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, China.
2 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.
3 Inria Diana Sophia Antipolis-Mediterranee, Sophia Antipolis, France.
* Corresponding Author: Yanbin Sun. Email: .

Computers, Materials & Continua 2020, 63(1), 213-222. https://doi.org/10.32604/cmc.2020.07496

Received 28 May 2019; Accepted 01 July 2019; Issue published 30 March 2020

Abstract

With the rapid increase of link speed and network throughput in recent years, much more attention has been paid to the work of obtaining statistics over speed traffic streams. It is a challenging problem to identify heavy hitters in high-speed and dynamically changing data streams with less memory and computational overhead with high measurement accuracy. In this paper, we combine Bloom Filter with exponential histogram to query streams in the sliding window so as to identify heavy hitters. This method is called EBF sketches. Our sketch structure allows for effective summarization of streams over time-based sliding windows with guaranteed probabilistic accuracy. It can be employed to address problems such as maintaining frequency statistics and finding heavy hitters. Our experimental results validate our theoretical claims and verifies the effectiveness of our techniques.

Keywords

Traffic stream, heavy hitter, sliding window, frequency statistics.

Cite This Article

S. Zhang, H. Luo, Z. Wu, Y. Sun, Y. Wang et al., "Efficient heavy hitters identification over speed traffic streams," Computers, Materials & Continua, vol. 63, no.1, pp. 213–222, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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