SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch
Jiarui Guo, Yisen Hong, Yuhan Wu, Yunfei Liu, Tong Yang, Bin Cui
摘要
1 Estimating the quantile of distribution, especially tail distribution, is an interesting topic in data stream models, and has obtained extensive interest from many researchers. In this paper, we propose a novel sketch, namely SketchPolymer to accurately estimate per-item tail quantile. SketchPolymer uses a technique called Early Filtration to filter infrequent items, and another technique called VSS to reduce error. Our experimental results show that the accuracy of SketchPolymer is on average 32.67 times better than state-of-the-art techniques. We also implement our SketchPolymer on P4 and FPGA platforms to verify its deployment flexibility. All our codes are available at GitHub [1].
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- Stingy Sketch: A Sketch Framework for Accurate and Fast Frequency EstimationHaoyu Li, Qizhi Chen, Yixin Zhang, Tong Yang 等VLDB 2022 · 被引用 54 次
- KLL±: Approximate Quantile Sketches over Dynamic DatasetsFuheng Zhao, Sujaya Maiyya, Ryan Weiner, Divy Agrawal 等VLDB 2021 · 被引用 36 次
- Scalable Tail Latency Estimation for Data Center NetworksKevin Zhao, Prateesh Goyal, Mohammad Alizadeh, Thomas E. AndersonNSDI 2023 · 被引用 30 次
- HistSketch: A Compact Data Structure for Accurate Per-Key Distribution MonitoringJintao He, Jiaqi Zhu, Qun HuangICDE 2023 · 被引用 21 次
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