基于大语言模型的SDN网络自动化配置研究

杨兴源; 田乐; 姚莹; 潘璠; 胡宇翔

doi:10.12263/DZXB.20250463

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基于大语言模型的SDN网络自动化配置研究

大模型与互联网 | 更新时间：2025-12-27

- 基于大语言模型的SDN网络自动化配置研究
- Research on SDN Network Automated Configuration Based on Large Language Models
- 电子学报 2025年53卷第9期页码：3078-3088
- 作者机构：
  
  信息工程大学，河南郑州 450001
- 作者简介：
  
  [ "杨兴源男，2002年9月生，江西萍乡人.现为信息工程大学信息技术研究所先进网络系统与技术研究室硕士研究生.主要研究方向为可编程网络、匿名通信、大语言模型. E-mail: yxy32735452@163.com" ]
  [ "田乐男，1987年11月生，陕西咸阳人.现为信息工程大学、国家数字交换系统工程技术研究中心副研究员、硕士生导师.主要研究方向为新型网络体系架构、网络安全.E-mail: xxgctianle@163.com" ]
  [ "姚莹女，2001年8月生，河南南阳人.现为信息工程大学信息技术研究所先进网络系统与技术研究室硕士研究生.主要研究方向为可编程网络、网络验证、大语言模型. E-mail: yao.2023@foxmail.com" ]
  [ "潘璠男，2001年8月生，安徽安庆人.现为信息工程大学信息技术研究所先进网络系统与技术研究室博士研究生.主要研究方向为可编程数据平面、零信任、网络智能. E-mail: panfan2024@126.com" ]
  [ "胡宇翔男，1982年11月生，河南周口人.现为信息工程大学教授、博士生导师.主要研究方向为新型网络体系架构、路由与交换、网络系统安全等技术. E-mail: chxachxa@126.com" ]
- 基金信息：
  
  国家重点研发计划(2022YFB2901501);中原科技创新领军人才项目(244200510038);河南省重大科技专项项目(221100210900-02)
- DOI：10.12263/DZXB.20250463
  中图分类号： TP393;
- 收稿：2025-05-31，
  
  录用：2025-09-11，
  
  纸质出版：2025-09-25
- 稿件说明：
移动端阅览
杨兴源, 田乐, 姚莹, 等. 基于大语言模型的SDN网络自动化配置研究[J]. 电子学报, 2025, 53(09): 3078-3088.

YANG Xing-yuan, TIAN Le, YAO Ying, et al. Research on SDN Network Automated Configuration Based on Large Language Models[J]. Acta Electronica Sinica, 2025, 53(09): 3078-3088.
杨兴源, 田乐, 姚莹, 等. 基于大语言模型的SDN网络自动化配置研究[J]. 电子学报, 2025, 53(09): 3078-3088. DOI：10.12263/DZXB.20250463

YANG Xing-yuan, TIAN Le, YAO Ying, et al. Research on SDN Network Automated Configuration Based on Large Language Models[J]. Acta Electronica Sinica, 2025, 53(09): 3078-3088. DOI：10.12263/DZXB.20250463

摘要

传统网络依赖人工配置，在应对规模激增、需求复杂化及实时性要求提升的现代网络环境时，效率低下且成本高昂.大语言模型（Large Language Model，LLM）凭借其出色的自然语言理解能力，在网络自动化配置中展现出巨大的潜力.面向软件定义网络（Software Defined Networking，SDN），本文提出了一种基于LLM的轻量级自动化配置方法.在数据平面，提出了一种基于检索增强生成（Retrieval-Augmented Generation，RAG）技术的代码自动生成方法RetroP4，支持基于用户意图生成P4代码；在控制平面，提出了一种基于任务分解的流表自动生成方法CtrlSynth，支持基于用户意图和数据平面P4代码生成流表配置.实验结果表明：相较于通用大模型，RetroP4生成的P4代码的语法正确性提高了25%，语义正确性提高了87.5%；CtrlSynth能够准确生成与P4代码匹配的流表信息，在流量意图不超过300条时，准确率可达100%.

Abstract

Traditional networks

which depend on manual configuration

are inefficient and expensive in the face of today’s rapidly expanding scales

increasingly complex demands

and the growing need for real-time responsiveness. Large language models (LLM)

known for their exceptional ability to understand natural language

show immense promise for automating network configurations. This paper introduces a streamlined approach to automated configuration for software defined networking (SDN)

leveraging LLM. For the data plane

we present RetroP4

a code generation method that uses retrieval-augmented generation (RAG) technology

enabling the creation of P4 code tailored to users’ intentions. In the control plane

we propose CtrlSynth

a method for automatically generating flow tables by breaking down tasks

aligning the configurations with users’ intentions and the P4 code from the data plane. Compared with general-purpose large models

the syntactic correctness of P4 code generated by RetroP4 is improved by 25%

and the semantic correctness is enhanced by 87.5%. CtrlSynth accurately produces flow table information that corresponds to the P4 code

achieving a 100% accuracy rate when dealing with up to 300 traffic-related intentions.

关键词

Keywords

references

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