Metis: Understanding and Enhancing In-Network Regular Expressions
Authors: Zhengxin Zhang, Yucheng Huang, Guanglin Duan, Qing Li, Dan Zhao, Yong Jiang, Lianbo Ma, Xi Xiao, Hengyang Xu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Metis is more accurate than original REs and other baselines, achieving superior throughput when deployed on network devices. We collect network traffic data on a large data center for three weeks and evaluate Metis on them. 4 Experiment 4.1 Experiment Setup |
| Researcher Affiliation | Collaboration | Tsinghua University, Peng Cheng Laboratory, Northeastern University Tsinghua Shenzhen International Graduate School, Tencent |
| Pseudocode | No | The paper describes various algorithms and processes (e.g., Thompson's construction, DFA construction, SSKD steps) but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our codes are available at Github2. (Footnote: https://github.com/You Are Special To Me/Metis.) |
| Open Datasets | No | The paper states that "we collect packet-level traces at gateways of a large data center" for evaluation, indicating a custom dataset that is not explicitly made publicly available with specific access information (link, DOI, formal citation). |
| Dataset Splits | Yes | We split each category of the dataset into the training set, test set, and validation set with a ratio of 7 : 2 : 1. |
| Hardware Specification | Yes | We conduct our experiments on a server with two 16-core CPUs (Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz), 64GB DRAM memory, and six Ge Force RTX 2080 SUPER GPUs. |
| Software Dependencies | No | The paper mentions software like "P4 language", "Barefoot P4 Studio Software Development Environment(SDE)", and "Intel DPDK library", but does not provide specific version numbers for these components. |
| Experiment Setup | Yes | For DFA2BRNN, we set the learning rate to 10 4, batch size to 500, and hidden state to 200 for each model. We use the cross-entropy loss as the objective function. We train each model for 200 epochs and use early stopping to avoid overfitting. For BRNN2PSRF, we set t to 7, α to 0.3, # split features to 7 and minimum samples to 15. |