支持网络多模态共生与演化的体系结构及运行逻辑

张慧峰; 胡宇翔; 朱俊; 邹涛; 皇甫伟; 隆克平

doi:10.11999/JEIT250949

支持网络多模态共生与演化的体系结构及运行逻辑

doi: 10.11999/JEIT250949 cstr: 32379.14.JEIT250949

张慧峰¹,
胡宇翔³,
朱俊¹,
邹涛¹,
皇甫伟^2, ,,
隆克平^{1, 2}

1.
之江实验室杭州 31121
2.
北京科技大学北京 100083
3.
信息工程大学郑州 450002

基金项目: 国家重点研发计划(2023YFB2903900)

详细信息

作者简介:
张慧峰：女，高级工程师，研究方向为新型网络体系架构、智算中心网络

胡宇翔：男，教授，要研究方向为新型网络架构、网络空间安全、智能路由与可编程转发等

朱俊：男，高级工程师，研究方向为新型网络体系结构、网络(资源)管理与控制技术、网络协议设计优化、卫星互联网

邹涛：男，研究员，研究方向为智算中心网络架构、高性能网络协议、网内计算

皇甫伟：男，教授，研究方向为新一代网络理论与技术、无线通信与边缘计算

隆克平：男，教授，研究方向为光互联网络及交换技术、新一代网络理论与技术、无线移动通信、网络新业务与安全

通讯作者:
皇甫伟　huangfuwei@ustb.edu.cn

中图分类号: TN91; TP393
计量
- 文章访问数: 53
- HTML全文浏览量: 10
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2025-09-22
- 修回日期: 2025-12-09
- 录用日期: 2025-12-12
- 网络出版日期: 2025-12-17

Architecture and Operational Dynamics for Enabling Symbiosis and Evolution of Network Modalities

1.
Zhejiang Laboratory, Hangzhou 31121, China
2.
University of Science and Technology Beijing, Beijing 100083, China
3.
Information Engineering University, ZhengZhou 450002, China

Funds: The National Key Research and Development Project of China (2023YFB2903900)

摘要

摘要: 针对多模态网络(PN)动态演化与共生协同的关键需求，该文分析了多模态网络业务域、模态(网络模态(NM))域、功能域、资源域的组成与域间映射，将其建模为复杂的动力学系统，并以业务服务质量、网络资源复用水平和业务包容性为系统目标，指出多模态网络的运行遵循最小自由能原理，并揭示了该动力学系统中的双尺度现象，为网络模态共生(SNM)和演化(ENM)提供理论指导。进而，提出一种网络模态共生与演化的3切面结构，即网络模态演化决策切面、网络模态智能生成切面和网络模态共生平台切面，为实现网络模态共生和演化提供使能架构。最后，分析了该体系结构的运行逻辑，为多模态网络中网络模态的高效协同与动态演化提供了运行指导。
- 多模态网络 /
- 体系结构 /
- 运行逻辑 /
- 网络模态共生 /
- 网络模态演化
Abstract: Objective The paradigm shift toward polymorphic networks enables dynamic deployment of diverse network modalities on shared infrastructure but introduces two fundamental challenges. First, symbiosis complexity arises from the absence of formal mechanisms to orchestrate coexistence conditions, intermodal collaboration, and resource efficiency gains among heterogeneous network modalities, which results in inefficient resource use and performance degradation. Second, evolutionary uncertainty stems from the lack of lifecycle-oriented frameworks to govern triggering conditions (e.g., abrupt traffic surges), optimization objectives (service-level agreement compliance and energy efficiency), and transition paths (e.g., seamless migration from IPv6 to GEO-based modalities) during network modality evolution, which constrains adaptive responses to vertical industry demands such as vehicular networks and smart manufacturing. This study aims to establish a theoretical and architectural foundation to address these gaps by proposing a three-plane architecture that supports dynamic coexistence and evolution of polymorphic networks with deterministic service-level agreement guarantees. Methods The architecture decouples network operation into four domains: (1) The business domain dynamically clusters services using machine learning according to quality-of-service requirements. (2) The modal domain generates specialized network modalities through software-defined interfaces. (3) The function domain enables baseline capability pooling by atomizing network functions into reusable components. (4) The resource domain supports fine-grained resource scheduling through elementization techniques. The core innovation lies in three synergistic planes: (1) The evolutionary decision plane applies predictive analytics for adaptive selection and optimization of network modalities. (2) The intelligent generation plane orchestrates modality deployment with global resource awareness. (3) The symbiosis platform plane dynamically composes baseline capabilities to support modality coexistence. Results and Discussions The proposed architecture advances beyond conventional approaches by avoiding virtualization overhead through native deployment of network modalities directly on polymorphic network elements. Resource elementization and capability pooling jointly support efficient cross-modality resource sharing. Closed-loop interactions among the decision, generation, and symbiosis planes enable autonomous network evolution that adapts to time-varying service demands under unified control objectives. Conclusions A theoretically grounded framework is presented to support dynamic symbiosis of heterogeneous network modalities on shared infrastructure through business-driven decision mechanisms and autonomous evolution. The architecture provides a scalable foundation for future systems that integrate artificial intelligence. Future work will extend this paradigm to integrated 6G satellite-terrestrial scenarios, where spatial-temporal resource complementarity is expected to play a central role.
- Polymorphic network /
- Architecture /
- Operational dynamics /
- Network modality symbiosis /
- Network modality evolution

HTML全文

图 1 多模态网络环境

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图 2 多模态网络功能层次参考模型

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图 3 支持网络模态共生与演化的体系结构

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图 4 支持网络模态共生与演化的使能技术

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图 5 网络模态共生与演化的运行逻辑

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参考文献(18)

[1]	XIE Kun, WANG Lele, WANG Xin, et al. Accurate recovery of internet traffic data: A sequential tensor completion approach[J]. IEEE/ACM Transactions on Networking, 2018, 26(2): 793–806. doi: 10.1109/TNET.2018.2797094.
[2]	LI Rongpeng, ZHAO Zhifeng, ZHENG Jianchao, et al. The learning and prediction of application-level traffic data in cellular networks[J]. IEEE Transactions on Wireless Communications, 2017, 16(6): 3899–3912. doi: 10.1109/TWC.2017.2689772.
[3]	ZHANG Lixia, AFANASYEV A, BURKE J, et al. Nameddata networking[J]. ACM SIGCOMM Computer Communication Review, 2014, 44(3): 66–73. doi: 10.1145/2656877.2656887.
[4]	WU Jiangxing, LI Junfei, SUN Penghao, et al. Theoretical framework for a polymorphic network environment[J]. Engineering, 2024, 39: 222–234. doi: 10.1016/j.eng.2024.01.018.
[5]	HU Yuxiang, LI Dan, SUN Penghao, et al. Polymorphicsmart network: An open, flexible and universal architecturefor future heterogeneous networks[J]. IEEE Transactions onNetwork Science and Engineering, 2020, 7(4): 2515–2525. doi: 10.1109/TNSE.2020.3006249.
[6]	胡宇翔, 崔子熙, 李子勇, 等. 基于领域专用软硬件协同的多模态网络环境构造技术[J]. 通信学报, 2022, 43(4): 3–13. doi: 10.11959/j.issn.1000−436x.2022086. HU Yuxiang, CUI Zixi, LI Ziyong, et al. Construction technologies of polymorphic network environment based on codesign of domain-specific software/hardware[J]. Journal on Communications, 2022, 43(4): 3–13. doi: 10.11959/j.issn.1000−436x.2022086.
[7]	凃化清, 方徐鑫, 朱俊, 等. 面向多模态网络的SONiC网元控制通道容器设计[J]. 电信科学, 2025, 41(3): 128–141. doi: 10.11959/j.issn.1000-0801.2025026. TU Huaqing, FANG Xuxin, ZHU Jun, et al. Desigh of SONiC network element control channel container for plymorphic network[J]. Telecommunications Science, 2025, 41(3): 128–141. doi: 10.11959/j.issn.1000-0801.2025026.
[8]	余宏志. 多模态网络智能流量控制方法研究[D]. [硕士论文], 电子科技大学, 2024. doi: 10.27005/d.cnki.gdzku.2024.003018. YU Hongzhi. Research on intelligent traffic control methods for PINet[D]. [Master dissertation], University of Electronic Science and Technology of China, 2024. doi: 10.27005/d.cnki.gdzku.2024.003018.
[9]	李梦龙, 田乐, 申涓, 等. 一种多模态智慧网络仿真器架构及仿真测试方法[P]. 中国, 202211186728.9, 2023. LI Menglong, TIAN Le, SHEN Juan, et al. Multi-mode intelligent network simulator architecture and simulation test method[P]. CN, 202211186728.9, 2023.
[10]	张慧峰, 华梓强, 骆汉光, 等. 一种支持网络多模态共生演化的系统及方法[P]. 中国, 202411290331.3, 2024. ZHANG Huifeng, HUA Ziqiang, LUO Hanguang, et al. System and method for supporting multi-mode symbiotic evolution of network[P]. CN, 202411290331.3, 2024.
[11]	中国通信学会. T/ZGTXXH 029-2022 多模态网元技术要求[S]. 中国通信学会, 2022. China Institute of Communications. T/ZGTXXH 029-2022 Technical specification for polymorphic network element[S]. China Institute of Communications, 2022.
[12]	中国通信学会. T/ZGTXXH 102-2025 多模态网络术语[S]. 中国通信学会, 2025. China Institute of Communications. T/ZGTXXH 102-2025 Multimodal network terminology[S]. China Institute of Communications, 2025.
[13]	JOSE S T and SIMEONE O. Free energy minimization: A unified framework for modeling, inference, learning, and optimization [lecture notes][J]. IEEE Signal Processing Magazine, 2021, 38(2): 120–125. doi: 10.1109/MSP.2020.3041414.
[14]	中国通信学会. T/ZGTXXH 103-2025 多模态网络多模态试验网的网络模态部署技术要求[S]. 中国通信学会, 2025. China Institute of Communications. T/ZGTXXH 103-2025 Multimodal networks technical requirements for network modality deployment of multimodal test networks[S]. China Institute of Communications, 2025.
[15]	XIE Junfeng, YU F R, HUANG Tao, et al. A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges[J]. IEEE Communications Surveys & Tutorials, 2019, 21(1): 393–430. doi: 10.1109/COMST.2018.2866942.
[16]	JAURO F, CHIROMA H, GITAL A Y, et al. Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend[J]. Applied Soft Computing, 2020, 96: 106582. doi: 10.1016/j.asoc.2020.106582.
[17]	MIJUMBI R, HASIJA S, DAVY S, et al. Topology-aware prediction of virtual network function resource requirements[J]. IEEE Transactions on Network and Service Management, 2017, 14(1): 106–120. doi: 10.1109/TNSM.2017.2666781.
[18]	ZHU Rui, LIU Bang, NIU Di, et al. Network latency estimation for personal devices: A matrix completion approach[J]. IEEE/ACM Transactions on Networking, 2017, 25(2): 724–737. doi: 10.1109/TNET.2016.2612695.