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

ZHANG Huifeng; HU Yuxiang; ZHU Jun; ZOU Tao; HUANGFU Wei; LONG Keping

doi:10.11999/JEIT250949

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

ZHANG Huifeng, HU Yuxiang, ZHU Jun, ZOU Tao, HUANGFU Wei, LONG Keping. Architecture and Operational Dynamics for Enabling Symbiosis and Evolution of Network Modalities[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250949

Citation:

ZHANG Huifeng, HU Yuxiang, ZHU Jun, ZOU Tao, HUANGFU Wei, LONG Keping. Architecture and Operational Dynamics for Enabling Symbiosis and Evolution of Network Modalities[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250949

Citation:

PDF( 4808 KB)

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

doi: 10.11999/JEIT250949 cstr: 32379.14.JEIT250949

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)

Received Date: 2025-09-22
Accepted Date: 2025-12-12
Rev Recd Date: 2025-12-09

Available Online: 2025-12-17

Abstract

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

FullText(HTML)

References(18)

References

[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.