Generating Neural Networks for Diverse Networking Classification Tasks via Hardware-Aware Neural Architecture Search

Guorui Xie, Qing Li, Zhenning Shi, Hanbin Fang, Shengpeng Ji, Yong Jiang, Zhenhui Yuan, Lianbo Ma, Mingwei Xu

Research output: Contribution to journalArticlepeer-review


Neural networks (NNs) are widely used in classification-based networking analysis to help traffic transmission and system security. However, there are heterogeneous network devices (e.g., switches and routers) in a network. Manually customizing NNs with specific device requirements (e.g., max allowed running latency) can be time-consuming and labor-intensive. Furthermore, the diverse data characteristics of different networking classification tasks add to the burden of NN customization. This paper introduces Loong, a neural architecture search (NAS) based system that automatically generates NNs for various networking tasks and devices. Loong includes a neural operation embedding module, which embeds candidate neural operations into the layer to be designed. Then, the layer-wise training is used to generate a task-specific NN layer by layer. This layer-wise scheme simultaneously trains and selects candidate neural operations using gradient feedback. Finally, only the important operations are selected to form the layer, maximizing accuracy. By incorporating multiple objectives, including deployment memory and running latency of devices, into the training and selection of NNs, Loong is able to customize NNs for heterogeneous network devices. Experiments show that Loong's NNs outperform 13 manual-designed and NAS-based NNs, with a 4.11% improvement in F1-score. Additionally, Loong's NNs achieve faster (7.92X) speeds on commodity devices.

Original languageEnglish
Pages (from-to)481-494
Number of pages14
JournalIEEE Transactions on Computers
Issue number2
Early online date20 Nov 2023
Publication statusPublished - 1 Feb 2024

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