Accelerating Rule-matching Systems with Learned Rankers


Zhao Lucis Li, University of Science and Technology China; Chieh-Jan Mike Liang and Wei Bai, Microsoft Research; Qiming Zheng, Shanghai Jiao Tong University; Yongqiang Xiong, Microsoft Research; Guangzhong Sun, University of Science and Technology China


Infusing machine learning (ML) and deep learning (DL) into modern systems has driven a paradigm shift towards learning-augmented system design. This paper proposes the learned ranker as a system building block, and demonstrates its potential by using rule-matching systems as a concrete scenario. Specifically, checking rules can be time-consuming, especially complex regular expression (regex) conditions. The learned ranker prioritizes rules based on their likelihood of matching a given input. If the matching rule is successfully prioritized as a top candidate, the system effectively achieves early termination. We integrated the learned rule ranker as a component of popular regex matching engines: PCRE, PCRE-JIT, and RE2. Empirical results show that the rule ranker achieves a top-5 classification accuracy at least 96.16%, and reduces the rule-matching system latency by up to 78.81% on a 8-core CPU.

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@inproceedings {234938,
author = {Zhao Lucis Li and Chieh-Jan Mike Liang and Wei Bai and Qiming Zheng and Yongqiang Xiong and Guangzhong Sun},
title = {Accelerating Rule-matching Systems with Learned Rankers},
booktitle = {2019 USENIX Annual Technical Conference (USENIX ATC 19)},
year = {2019},
isbn = {978-1-939133-03-8},
address = {Renton, WA},
pages = {1041--1048},
url = {},
publisher = {USENIX Association},
month = jul,

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