An Efficient Design of Intelligent Network Data Plane


Guangmeng Zhou, Tsinghua University; Zhuotao Liu, Tsinghua University and Zhongguancun Laboratory; Chuanpu Fu, Tsinghua University; Qi Li and Ke Xu, Tsinghua University and Zhongguancun Laboratory

Distinguished Paper Award Winner


Deploying machine learning models directly on the network data plane enables intelligent traffic analysis at line-speed using data-driven models rather than predefined protocols. Such a capability, referred to as Intelligent Data Plane (IDP), may potentially transform a wide range of networking designs. The emerging programmable switches provide crucial hardware support to realize IDP. Prior art in this regard is divided into two major categories: (i) focusing on extract useful flow information from the data plane, while placing the learning-based traffic analysis on the control plane; and (ii) taking a step further to embed learning models into the data plane, while failing to use flow-level features that are critical to achieve high learning accuracies. In this paper, we propose NetBeacon to advance the state-of-the-art in both model accuracy and model deployment efficiency. In particular, NetBeacon proposes a multi-phase sequential model architecture to perform dynamic packet analysis at different phases of a flow as it proceeds, by incorporating flow-level features that are computable at line-speed to boost learning accuracies. Further, NetBeacon designs efficient model representation mechanisms to address the table entry explosion problem when deploying tree-based models on the network data plane. Finally, NetBeacon hardens its scalability for handling concurrent flows via multiple tightly-coupled designs for managing stateful storage used to store per-flow state. We implement a prototype of NetBeacon and extensively evaluate its performance over multiple traffic analysis tasks.

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@inproceedings {285495,
author = {Guangmeng Zhou and Zhuotao Liu and Chuanpu Fu and Qi Li and Ke Xu},
title = {An Efficient Design of Intelligent Network Data Plane},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {6203--6220},
url = {},
publisher = {USENIX Association},
month = aug