An Efficient Design of Intelligent Network Data Plane

Authors: 

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

Abstract: 

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.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

BibTeX
@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 = {https://www.usenix.org/conference/usenixsecurity23/presentation/zhou-guangmeng},
publisher = {USENIX Association},
month = aug
}

Presentation Video