Open Access

ARTICLE

Efficient Heavy Hitters Identification over Speed Traffic Streams

Shuzhuang Zhang¹, Hao Luo¹, Zhigang Wu¹, Yanbin Sun^{2, *}, 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.

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Citations