高性能网络数字孪生仿真引擎研究

石鸿伟; 倪中阳; 陆干沂; 黄韬

doi:null

您当前的位置：

首页 >

文章列表页 >

高性能网络数字孪生仿真引擎研究

理论与技术 | 更新时间：2026-02-09

- 高性能网络数字孪生仿真引擎研究
- Research on high-performance network digital twin simulation engine
- 物联网学报 2026年页码：1-11
- 作者机构：
  
  1.东南大学网络空间安全学院，江苏南京 211189
  2.紫金山实验室，江苏南京 211111
  3.北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
  
  [ "石鸿伟（1982‒ ），男，东南大学在读博士，紫金山实验室课题负责人，高级工程师，主要研究方向为未来网络体系架构、软件定义网络、网络智能控制等。" ]
  [ "倪中阳（1987‒ ），男，紫金山实验室研究员，主要研究方向为网络管控、数字孪生等。" ]
  [ "陆干沂（1991‒ ），男，紫金山实验室研究员，主要研究方向为网络编排、数字孪生等。" ]
  [ "黄韬（1980‒ ），男，北京邮电大学教授、博士生导师，主要研究方向为路由与交换、软件定义网络、内容分发网络、确定性网络、算力网络等。" ]
- 基金信息：
  
  国家重点研发计划项目(2022YFB2702303)
- DOI：
  中图分类号：
- 收稿：2025-05-23，
  
  修回：2025-06-18，
  
  录用：2025-07-18，
  
  网络首发：2026-02-09，
- 稿件说明：
移动端阅览
石鸿伟,倪中阳,陆干沂等.高性能网络数字孪生仿真引擎研究[J].物联网学报,

SHI Hongwei,NI Zhongyang,LU Ganyi,et al.Research on high-performance network digital twin simulation engine[J].Chinese Journal on Internet of Things,
石鸿伟,倪中阳,陆干沂等.高性能网络数字孪生仿真引擎研究[J].物联网学报, DOI：10.11959/j.issn.2096-3750..00505.

SHI Hongwei,NI Zhongyang,LU Ganyi,et al.Research on high-performance network digital twin simulation engine[J].Chinese Journal on Internet of Things, DOI：10.11959/j.issn.2096-3750..00505.

摘要

网络数字孪生技术在提升IP承载网仿真测试的运维效率与决策精度方面具有重要作用，然而当前仍面临仿真精度不高与仿真性能不足等问题。本文基于PNetLab仿真平台，提出一种高性能网络数字孪生仿真引擎——ePNetLab。首先，对原有平台进行性能优化与功能扩展，改进接口响应机制，设计跨节点通信方案，提升拓扑构建效率并增强集群化组网能力。其次，设计并实现基于社区划分算法的拓扑动态构建方法，有效降低大规模仿真场景的构建时间与资源开销。最后，通过实验评估验证了所提方案的可行性与高效性。结果表明，ePNetLab在拓扑构建效率方面相较于原生PNetLab在最优条件下提升82.9%；同时，所引入的社区划分算法在仿真效率、资源利用率与业务性能等方面较其他算法有较大提升。

Abstract

Network digital twin technology plays a significant role in improving the maintenance efficiency and decision-making accuracy of IP bearer network simulation and testing. However

it still faces challenges such as low simulation accuracy and insufficient simulation performance. This paper proposes a high-performance network digital twin simulation engine

ePNetLab

based on the PNetLab simulation platform. First

the original platform is optimized in terms of performance and functionality. The interface response mechanism is improved

a cross-node communication scheme is designed

and the efficiency of topology construction is enhanced

along with the ability to form clustered networks. Second

a topology dynamic construction method based on community detection algorithms is designed and implemented

effectively reducing the construction time and resource overhead in large-scale simulation scenarios. Finally

experimental evaluations are conducted to verify the feasibility and efficiency of the proposed solution. The results show that ePNetLab improves the topology construction efficiency by 82.9% compared to the native PNetLab under optimal conditions. Meanwhile

the community partitioning algorithm introduced has greatly improved simulation efficiency

resource utilization and business performance compared with other algorithms.

关键词

Keywords

references

Wu Y , Zhang K , Zhang Y . Digital twin networks: A survey [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 18 ): 13789 - 13804 .

Madni M A A , Iranmanesh S , Raad R . DTN and Non-DTN routing protocols for inter-cubesat communications: A comprehensive survey [J ] . Electronics , 2020 , 9 ( 3 ): 482 .

Jia W , Wang W , Zhang Z . From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin [J ] . Advanced Engineering Informatics , 2022 , 53 : 101706 .

Dai Y , Zhang K , Maharjan S , et al . Deep reinforcement learning for stochastic computation offloading in digital twin networks [J ] . IEEE Transactions on Industrial Informatics , 2020 , 17 ( 7 ): 4968 - 4977 .

ITU-T . Digital twin network-Requirements and architecture [S ] . 2022 .

IETF . Network Digital Twin: Concepts and Reference Architecture in draft-irtf-nmrg-network-digital-twin-arch-00 [R ] . 2022 .

Tao S , Cheng Z , Xiao-Dong D , et al . Digital twin network (DTN): concepts architecture and key technologies [J ] . Acta Automatica Sinica , 2021 , 47 ( 3 ): 569 - 582 .

中国移动研究院 . 数字孪生网络（DTN）白皮书 [R ] . 2021 .

CMRI . Digital twin network (DTN) white paper [R ] . 2021 .

Ferriol-Galmés M , Suárez-Varela J , Paillissé J , et al . Building a digital twin for network optimization using graph neural networks [J ] . Computer Networks , 2022 , 217 : 109329 .

Ferriol-Galmés M , Cheng X , Shi X , et al . FlowDT: a flow-aware digital twin for computer networks [C ] // ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE , 2022 : 8907 - 8911 .

Ferriol-Galmés M , Rusek K , Suárez-Varela J , et al . Routenet-erlang: A graph neural network for network performance evaluation [C ] // IEEE INFOCOM 2022-IEEE Conference on Computer Communications . IEEE , 2022 : 2018 - 2027 .

Rodrigo M S , Rivera D , Moreno J I , et al . Digital twins for 5G networks: A modeling and deployment methodology [J ] . IEEE Access , 2023 , 11 : 38112 - 38126 .

Vilà I , Sallent O , Pérez-Romero J . On the design of a network digital twin for the radio access network in 5g and beyond [J ] . Sensors , 2023 , 23 ( 3 ): 1197 .

Zhang Z , Liu Y , Peng Z , et al . Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [J ] . IEEE Journal on Selected Areas in Communications , 2024 .

Wang K , Du H , Su L . Digital twin network based network slice security provision [C ] // 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) . IEEE , 2022 : 1 - 6 .

Wang H , Wu Y , Min G , et al . A graph neural network-based digital twin for network slicing management [J ] . IEEE Transactions on Industrial Informatics , 2020 , 18 ( 2 ): 1367 - 1376 .

Hao Y , Wang J , Huo D , et al . Digital twin-assisted URLLC-enabled task offloading in mobile edge network via robust combinatorial optimization [J ] . IEEE Journal on Selected Areas in Communications , 2023 .

Aliyu I , Arigi A , Oh S , et al . Towards a Partial Computation offloading in In-networking Computing-Assisted MEC: A Digital Twin Approach [C ] // NOMS 2024-2024 IEEE Network Operations and Management Symposium . IEEE , 2024 : 1 - 9 .

Cui Y , Liu J , Yu M , et al . Network digital twin [J ] . IEEE Network , 2024 , 38 ( 1 ): 5 - 6 .

Hui L , Wang M , Zhang L , et al . Digital twin for networking: A data-driven performance modeling perspective [J ] . IEEE Network , 2022 , 37 ( 3 ): 202 - 209 .

Villa D , Tehrani-Moayyed M , Robinson C P , et al . Colosseum as a digital twin: Bridging real-world experimentation and wireless network emulation [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 10 ): 9150 - 9166 .

Chen M , Miao Y , Humar I , et al . Introduction to OPNET network simulation [J ] . OPNET IoT Simulation , 2019 : 77 - 153 .

Xian X , Shi W , Huang H . Comparison of OMNET++ and other simulator for WSN simulation [C ] // 2008 3rd IEEE Conference on Industrial Electronics and Applications . IEEE , 2008 : 1439 - 1443 .

Campanile L , Gribaudo M , Iacono M , et al . Computer network simulation with ns-3: A systematic literature review [J ] . Electronics , 2020 , 9 ( 2 ): 272 .

Chen J , Zheng X , Zhang L , et al . WLAN simulation experiment based on ENSP [C ] // Journal of Physics: Conference Series . IOP Publishing , 2019 , 1325 ( 1 ): 012046 .

邱鹏，霍瑛，蒋悦 . 基于H3C Cloud Lab的企业网络设计与仿真 [J ] . 实验室研究与探索 , 2020 , 39 ( 10 ): 118 - 125 .

QIU Peng , HUO Ying , JIANG Yue . Design and Simulation of Enterprise Networks Based on H3C Cloud Lab [J ] . Research and Exploration in Laboratory , 2020 , 39 ( 10 ): 118 - 125 .

Korniyenko B , Galata L , Ladieva L . Research of information protection system of corporate network based on GNS3 [C ] // 2019 IEEE international conference on advanced trends in information theory (ATIT) . IEEE , 2019 : 244 - 248 .

Ajiardiawan G , Suryanto Y . Performance analysis of segment routing on MPLS L3VPN using PNETLAB [C ] // 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST) . IEEE , 2021 : 185 - 190 .

Arvind T . A comparative study of various network simulation tools [J ] . International Journal of Computer Science & Engineering Technology (IJCSET) , 2016 , 7 ( 08 ): 374 - 378 .

Pan J , Jain R . A survey of network simulation tools: Current status and future developments [J ] . Email: jp10@ cse. wustl. edu , 2008 , 2 ( 4 ): 45 .

Nutalapati P . Service Mesh in Kubernetes: Implementing Istio for Enhanced Observability and Security [J ] . Journal of Scientific and Engineering Research , 2021 , 8 ( 11 ): 200 - 206 .

Girvan M , Newman M E J . Community structure in social and biological networks [J ] . Proceedings of the national academy of sciences , 2002 , 99 ( 12 ): 7821 - 7826 .

Newman M E J , Girvan M . Finding and evaluating community structure in networks [J ] . Physical review E , 2004 , 69 ( 2 ): 026113 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据