SteadySketch: A High-Performance Algorithm for Finding Steady Flows in Data Streams

In this paper, we study steady flows in data streams, which refers to the flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch-based algorithm, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the memory requirement by 75%. Our theoretical proofs show that the negative impact of the reborn technique is small. Experimental results show that, compared with the two comparison schemes, SteadySketch improves the Precision Rate (PR) by around 79.5% and 82.8%, and reduces the Average Relative Error (ARE) by around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$905.9\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$657.9\times $ </tex-math></inline-formula>, respectively. Finally, we provide three concrete cases: cache prefetch, Redis and P4 implementation. As we will demonstrate, SteadySketch can effectively improve the cache hit ratio while achieving satisfying performance on both Redis and Tofino switches. All related codes of SteadySketch are available at GitHub.

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Publication Details

Journal
IEEE/ACM Transactions on Networking
Published
2024-08-22
DOI
https://doi.org/10.1109/tnet.2024.3444488
Citations
5
Primary Topic
Data Stream Mining Techniques
Type
article
Field-Weighted Citation Impact
1.74

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article

SteadySketch: A High-Performance Algorithm for Finding Steady Flows in Data Streams

S.I. Long, Xuebin Chen, Bin Cui, Xiaodong Li et al.
5 citations
IEEE/ACM Transactions on Networking
Data Stream Mining Techniques
1.74
article

SteadySketch: A High-Performance Algorithm for Finding Steady Flows in Data Streams

S.I. Long, Xuebin Chen, Bin Cui, Xiaodong Li, Tong Yang, Jiarui Guo, Zhuochen Fan, Haoyu Li, Wenrui Liu, Zheng Zhong, Xiangyuan Wang
article en
5 citations

Abstract

In this paper, we study steady flows in data streams, which refers to the flows whose arrival rate is always non-zero and around a fixed value for several consecutive time windows. To find steady flows in real time, we propose a novel sketch-based algorithm, SteadySketch, aiming to accurately report steady flows with limited memory. To the best of our knowledge, this is the first work to define and find steady flows in data streams. The key novelty of SteadySketch is our proposed reborn technique, which reduces the memory requirement by 75%. Our theoretical proofs show that the negative impact of the reborn technique is small. Experimental results show that, compared with the two comparison schemes, SteadySketch improves the Precision Rate (PR) by around 79.5% and 82.8%, and reduces the Average Relative Error (ARE) by around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$905.9\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$657.9\times $ </tex-math></inline-formula>, respectively. Finally, we provide three concrete cases: cache prefetch, Redis and P4 implementation. As we will demonstrate, SteadySketch can effectively improve the cache hit ratio while achieving satisfying performance on both Redis and Tofino switches. All related codes of SteadySketch are available at GitHub.

IEEE/ACM Transactions on NetworkingVol. 32(6)
North China University of Science and Technology (CN), North China University of Technology (CN), Peking University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, National Key Research and Development Program of China
Openalex Percentile: Top 13%
Data Stream Mining Techniques
1.74
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