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.
Authors
- S.I. Long (ORCID: https://orcid.org/0000-0001-9977-0897)
- Xuebin Chen (ORCID: https://orcid.org/0000-0001-8335-1612)
- Bin Cui (ORCID: https://orcid.org/0000-0003-1681-4677)
- Xiaodong Li (ORCID: https://orcid.org/0000-0002-3534-8045)
- Tong Yang (ORCID: https://orcid.org/0000-0003-2402-5854)
- Jiarui Guo (ORCID: https://orcid.org/0000-0003-1551-7646)
- Zhuochen Fan (ORCID: https://orcid.org/0000-0003-0042-1828)
- Haoyu Li (ORCID: https://orcid.org/0009-0004-9836-7747)
- Wenrui Liu (ORCID: https://orcid.org/0000-0002-5589-0085)
- Zheng Zhong
- Xiangyuan Wang (ORCID: https://orcid.org/0009-0003-1321-0293)
Institutions
- North China University of Science and Technology (CN)
- North China University of Technology (CN)
- Peking University (CN)
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
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- National Key Research and Development Program of China