GAI使能网络智能化：基于LLM的网络操作系统

霍如; 沙宗轩; 吕科呈; 陈伟; 汪硕; 黄韬; F. Richard Yu

doi:10.11959/j.issn.1000-436x.2025113

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GAI使能网络智能化：基于LLM的网络操作系统

学术论文 | 更新时间：2025-07-04

- GAI使能网络智能化：基于LLM的网络操作系统
- General artificial intelligence enables network intelligence: a network operating system based on large language model
- 通信学报 2025年46卷第6期页码：32-44
- 作者机构：
  
  1.北京工业大学信息科学技术学院，北京 100124
  2.紫金山实验室，江苏南京 211111
  3.北京邮电大学网络与交换国家重点实验室，北京 100876
  4.卡尔顿大学，渥太华 K1S 5B6
- 作者简介：
  
  [ "霍如（1988- ），女，黑龙江哈尔滨人，博士，北京工业大学副教授，主要研究方向为未来网络、网络智能化、区块链、资源调度等。" ]
  沙宗轩（1990- ），男，回族，安徽蚌埠人，北京工业大学博士生，主要研究方向为未来网络、网络人工智能、深度学习、强化学习等。
  吕科呈（2001- ），男，广西玉林人，北京工业大学硕士生，主要研究方向为未来网络、车联网、资源调度等。
  陈伟（1996- ），男，河北三河人，北京邮电大学博士生，主要研究方向为边缘计算、数字孪生、区块链等。
  汪硕（1991- ），男，河南灵宝人，博士，北京邮电大学副教授，主要研究方向为数据中心网络、软件定义网络、网络流量调度等。
  黄韬（1980- ），男，重庆人，博士，北京邮电大学教授，主要研究方向为未来网络体系架构、软件定义网络、网络虚拟化等。
  F. Richard Yu（1974- ），男，加拿大工程院院士，卡尔顿大学教授，主要研究方向为互联网自主智能、自动驾驶、网络空间安全等。
- 基金信息：
  
  北京市自然科学基金资助项目(4254064)
- DOI：10.11959/j.issn.1000-436x.2025113
  中图分类号： TP393
- 收稿日期：2025-04-28，
  
  修回日期：2025-06-09，
  
  纸质出版日期：2025-06-25
- 稿件说明：
移动端阅览
霍如,沙宗轩,吕科呈等.,GAI使能网络智能化：基于,LLM的网络操作系统[J].通信学报,2025,46(06):32-44.

HUO Ru,SHA Zongxuan,LYU Kecheng,et al.General artificial intelligence enables network intelligence: a network operating system based on large language model[J].Journal on Communications,2025,46(06):32-44.
霍如,沙宗轩,吕科呈等.,GAI使能网络智能化：基于,LLM的网络操作系统[J].通信学报,2025,46(06):32-44. DOI： 10.11959/j.issn.1000-436x.2025113.

HUO Ru,SHA Zongxuan,LYU Kecheng,et al.General artificial intelligence enables network intelligence: a network operating system based on large language model[J].Journal on Communications,2025,46(06):32-44. DOI： 10.11959/j.issn.1000-436x.2025113.

摘要

为了实现对网络高智能化可管可控的目标，提出了基于大语言模型（LLM）的高可控大网级网络操作系统。通过网络态势感知和微调大模型的意图理解优化资源配置，实现智能化运维。同时，设计了面向中国网络操作系统（CNOS）的网络大模型微调和推理流程，可识别CNOS指令并对系统反馈结果进行归纳总结，周期性的自主训练及模型迭代更新。实验结果表明，所提系统能够快速识别并准确转换用户指令，有效降低操作系统管理任务时间，实现网络资源的智能化调度和配置，提升网络操作系统的可控性和人机交互的友好程度。

Abstract

To achieve the goal of highly intelligent and controllable network

and a highly controllable large-scale network operating system was proposed based on large language model (LLM). Through network situation awareness and the intention understanding of fine-tuning LLM

resource allocation was optimized to achieve intelligent operation and maintenance. Meanwhile

the fine-tuning and inference flow for China network operation system (CNOS) oriented network large model was designed to recognize the CNOS system instructions and summarize the feedback results of the system

as well as periodic autonomous training and iterative model updating. The experimental results show that the proposed system can quickly identify and accurately convert user instructions

effectively reduce the management task time of the operating system

realize the intelligent scheduling and configuration of network resources

and improve the controllability of the network operating system and the friendliness of human-computer interaction.

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references

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