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Friday, July 12 • 12:10pm - 12:30pm
Accelerating Rule-matching Systems with Learned Rankers

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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.


Zhao Lucis Li

University of Science and Technology China

Chieh-Jan Mike Liang

Microsoft Research

Wei Bai

Microsoft Research

Qiming Zheng

Shanghai Jiao Tong University

Yongqiang Xiong

Microsoft Research

Guangzhong Sun

University of Science and Technology China

Friday July 12, 2019 12:10pm - 12:30pm PDT
USENIX ATC Track II: Grand Ballroom VII–IX