知识定义多模态网络按需服务体系研究

王敬宇; 庄子睿

doi:10.11959/j.issn.1000-436x.2022076

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知识定义多模态网络按需服务体系研究

专题：多模态网络环境关键技术 | 更新时间：2024-06-05

- 知识定义多模态网络按需服务体系研究
- Research on a knowledge-defined polymorphic network attainable service architecture
- 通信学报 2022年43卷第4期页码：71-82
- 作者机构：
  
  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：
  
  [ "王敬宇（1978- ），男，吉林长春人，博士，北京邮电大学教授、博士生导师，主要研究方向为智能网络、机器学习、边缘计算等" ]
  [ "庄子睿（1993- ），男，北京人，博士，北京邮电大学在站博士后，主要研究方向为网络智能路由、资源优化、深度强化学习、图神经网络等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62071067);国家自然科学基金资助项目(62101064);北京邮电大学-中国移动研究院联合创新中心基金资助项目
- DOI：10.11959/j.issn.1000-436x.2022076
  中图分类号： TN91/TN92
- 网络首发：2022-04，
  
  纸质出版：2022-04-25
- 稿件说明：
移动端阅览
王敬宇, 庄子睿. 知识定义多模态网络按需服务体系研究[J]. 通信学报, 2022,43(4):71-82.

Jingyu WANG, Zirui ZHUANG. Research on a knowledge-defined polymorphic network attainable service architecture[J]. Journal on Communications, 2022, 43(4): 71-82.
王敬宇, 庄子睿. 知识定义多模态网络按需服务体系研究[J]. 通信学报, 2022,43(4):71-82. DOI： 10.11959/j.issn.1000-436x.2022076.

Jingyu WANG, Zirui ZHUANG. Research on a knowledge-defined polymorphic network attainable service architecture[J]. Journal on Communications, 2022, 43(4): 71-82. DOI： 10.11959/j.issn.1000-436x.2022076.

摘要

针对管理、控制、数据三平面解耦的未来移动通信网络系统，提出了一种知识定义多模态网络按需服务体系架构。该架构仿照生物多态性的原理，将“网络知识”作为贯穿多个平面的“基因”主干，分场景、分层次地提取关键局部网络知识。通过构建逻辑统一的网络知识空间图谱，根据具体业务对特定知识的依赖诉求，不同的局部知识之间可以进行交换并形成有机联动。网络知识将能够面向不同服务场景、跨越多级服务层次、融合多种服务指标，为网络整体的优化管理提供引导和支撑。知识定义多模态网络可以帮助移动通信网络应对复杂多变的业务需求，使最终用户动态而多样化的需求可以得到及时、有效的满足和保障。

Abstract

Given the consideration of future generation mobile communication network systems with decoupled administration

control

and data planes

a knowledge-defined polymorphic network (KDPN) attainable service architecture was proposed.In the proposed architecture

while mimicking the biological polymorphism

“network knowledge” was acted as a genetic core connecting all the planes

where local network knowledge was extracted from different scenarios and layers of the network.By constructing a network knowledge graph with unified logic

and depending on the service requirement for specific knowledge

different local knowledge could be exchanged and collaborated.Network knowledge will provide guidance and foundation for general network optimization and management across different service scenarios

service layers

and service objectives.KDPN will help the communication networks cope with sophisticated and dynamic service demands and provide dynamic and diversified services to general users promptly and effectively with quality of experience assurances.

关键词

Keywords

references

CHEN X , JIAO L , LI W Z , et al . Efficient multi-user computation offloading for mobile-edge cloud computing [J ] . IEEE/ACM Transactions on Networking , 2016 , 24 ( 5 ): 2795 - 2808 .

SARDELLITTI S , SCUTARI G , BARBAROSSA S . Joint optimization of radio and computational resources for multicell mobile-edge computing [J ] . IEEE Transactions on Signal and Information Processing Over Networks , 2015 , 1 ( 2 ): 89 - 103 .

张建敏 , 谢伟良 , 杨峰义 , 等 . 移动边缘计算技术及其本地分流方案 [J ] . 电信科学 , 2016 , 32 ( 7 ): 132 - 139 .

ZHANG J M , XIE W L , YANG F Y , et al . Mobile edge computing and application in traffic offloading [J ] . Telecommunications Science , 2016 , 32 ( 7 ): 132 - 139 .

LIANG B , WONG V , SCHOBER R , et al . Mobile edge computing [J ] . Key Technologies for 5G Wireless Systems , 2017 , 16 ( 3 ): 1397 - 1411 .

TALEB T , SAMDANIS K , MADA B , et al . On multi-access edge computing:a survey of the emerging 5G network edge cloud architecture and orchestration [J ] . IEEE Communications Surveys ＆ Tutorials , 2017 , 19 ( 3 ): 1657 - 1681 .

MAO Y Y , ZHANG J , SONG S H , et al . Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems [J ] . IEEE Transactions on Wireless Communications , 2017 , 16 ( 9 ): 5994 - 6009 .

AI Y , PENG M G , ZHANG K C . Edge computing technologies for Internet of things:a primer [J ] . Digital Communications and Networks , 2018 , 4 ( 2 ): 77 - 86 .

施巍松 , 孙辉 , 曹杰 , 等 . 边缘计算:万物互联时代新型计算模型 [J ] . 计算机研究与发展 , 2017 , 54 ( 5 ): 907 - 924 .

SHI W S , SUN H , CAO J , et al . Edge computing—an emerging computing model for the Internet of everything era [J ] . Journal of Computer Research and Development , 2017 , 54 ( 5 ): 907 - 924 .

CHEN M , HAO Y X . Task offloading for mobile edge computing in software defined ultra-dense network [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 3 ): 587 - 597 .

李子姝 , 谢人超 , 孙礼 , 等 . 移动边缘计算综述 [J ] . 电信科学 , 2018 , 34 ( 1 ): 87 - 101 .

LI Z S , XIE R C , SUN L , et al . A survey of mobile edge computing [J ] . Telecommunications Science , 2018 , 34 ( 1 ): 87 - 101 .

WANG X F , HAN Y W , WANG C Y , et al . In-edge AI:intelligentizing mobile edge computing,caching and communication by federated learning [J ] . IEEE Network , 2019 , 33 ( 5 ): 156 - 165 .

POULARAKIS K , LLORCA J , TULINO A M , et al . Joint service placement and request routing in multi-cell mobile edge computing networks [C ] // Proceedings of IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2019 : 10 - 18 .

施巍松 , 张星洲 , 王一帆 , 等 . 边缘计算:现状与展望 [J ] . 计算机研究与发展 , 2019 , 56 ( 1 ): 69 - 89 .

SHI W S , ZHANG X Z , WANG Y F , et al . Edge computing:state-of-the-art and future directions [J ] . Journal of Computer Research and Development , 2019 , 56 ( 1 ): 69 - 89 .

KAMEL M , HAMOUDA W , YOUSSEF A . Ultra-dense networks:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2016 , 18 ( 4 ): 2522 - 2545 .

RUI L L , YANG Y T , GAO Z P , et al . Computation offloading in a mobile edge communication network:a joint transmission delay and energy consumption dynamic awareness mechanism [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 6 ): 10546 - 10559 .

张海波 , 李虎 , 陈善学 , 等 . 超密集网络中基于移动边缘计算的任务卸载和资源优化 [J ] . 电子与信息学报 , 2019 , 41 ( 5 ): 1194 - 1201 .

ZHANG H B , LI H , CHEN S X , et al . Computing offloading and resource optimization in ultra-dense networks with mobile edge computation [J ] . Journal of Electronics ＆ Information Technology , 2019 , 41 ( 5 ): 1194 - 1201 .

CLAFFY K , CLARK D . Platform models for sustainable Internet regulation [J ] . Journal of Information Policy , 2014 , 4 ( 1 ): 463 - 488 .

LEHR W , CLARK D D , BAUER S , et al . Regulation when platforms are layered [J ] . Social Science Electronic Publishing , 2019 :doi.org/10.2139/ssrn.3427499.

GIOTSAS V , NOMIKOS G , KOTRONIS V , et al . O peer,where art thou? uncovering remote peering interconnections at IXPs [J ] . IEEE/ACM Transactions on Networking , 2021 , 29 ( 1 ): 1 - 16 .

JIANG W , STRUFE M , SCHOTTEN H D . Experimental results for artificial intelligence-based self-organized 5G networks [C ] // Proceedings of 2017 IEEE 28th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications . Piscataway:IEEE Press , 2017 : 1 - 6 .

KIBRIA M G , NGUYEN K , VILLARDI G P , et al . Big data analytics,machine learning,and artificial intelligence in next-generation wireless networks [J ] . IEEE Access , 2018 , 6 : 32328 - 32338 .

PARK J , SAMARAKOON S , BENNIS M , et al . Wireless network intelligence at the edge [J ] . Proceedings of the IEEE , 2019 , 107 ( 11 ): 2204 - 2239 .

XU S J , QIAN Y , HU R Q . Data-driven network intelligence for anomaly detection [J ] . IEEE Network , 2019 , 33 ( 3 ): 88 - 95 .

XU S J , QIAN Y , HU R Q . Data-driven edge intelligence for robust network anomaly detection [J ] . IEEE Transactions on Network Science and Engineering , 2020 , 7 ( 3 ): 1481 - 1492 .

GUTIERREZ-ESTEVEZ D M , GRAMAGLIA M , DOMENICO A D , et al . Artificial intelligence for elastic management and orchestration of 5G networks [J ] . IEEE Wireless Communications , 2019 , 26 ( 5 ): 134 - 141 .

CHEN D W , LIU Y C , KIM B , et al . Edge computing resources reservation in vehicular networks:a meta-learning approach [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 5 ): 5634 - 5646 .

QIAO C M , MEI Y S , MYUNGSIK Y , et al . Polymorphic control for cost-effective design of optical networks [J ] . European Transactions on Telecommunications , 2000 , 11 ( 1 ): 17 - 26 .

CHEN L , MNAOUER A B , FOH C H . An optimized polymorphic hybrid multicast routing protocol (OPHMR) for ad hoc networks [C ] // Proceedings of 2006 IEEE International Conference on Communications . Piscataway:IEEE Press , 2006 : 3572 - 3577 .

胡宇翔 , 董芳 , 王鹏 , 等 . 面向多样化服务定制的多态路由机制研究 [J ] . 通信学报 , 2015 , 36 ( 7 ): 48 - 59 .

HU Y X , DONG F , WANG P , et al . Research on polymorphic routing mechanism for customized diversified services [J ] . Journal on Communications , 2015 , 36 ( 7 ): 48 - 59 .

胡宇翔 , 伊鹏 , 孙鹏浩 , 等 . 全维可定义的多模态智慧网络体系研究 [J ] . 通信学报 , 2019 , 40 ( 8 ): 1 - 12 .

HU Y X , YI P , SUN P H , et al . Research on the full-dimensional defined polymorphic smart network [J ] . Journal on Communications , 2019 , 40 ( 8 ): 1 - 12 .

邬江兴 , 胡宇翔 . 网络技术体系与支撑环境分离的发展范式 [J ] . 信息通信技术与政策 , 2021 , 47 ( 8 ): 1 - 11 .

WU J X , HU Y X . The development paradigm of separation between network technical system and supporting environment [J ] . Information and Communications Technology and Policy , 2021 , 47 ( 8 ): 1 - 11 .

HU Y X , LI D , SUN P H , et al . Polymorphic smart network:an open,flexible and universal architecture for future heterogeneous networks [J ] . IEEE Transactions on Network Science and Engineering , 2020 , 7 ( 4 ): 2515 - 2525 .

MARDER E . Invertebrate neurobiology:polymorphic neural networks [J ] . Current Biology , 1994 , 4 ( 8 ): 752 - 754 .

TEAMA S . DNA polymorphisms:DNA-based molecular markers and their application in medicine [J ] . Genetic Diversity and Disease Susceptibility , 2019 :doi.org/10.1007/978-1-4615-3690-1.

CLARK D D , PARTRIDGE C , RAMMING J C , et al . A knowledge plane for the Internet [C ] // Proceedings of the 2003 Conference on Applications,Technologies,Architectures,and Protocols for Computer Communications .[S.l.:s.n. ] , 2003 : 3 - 10 .

MESTRES A , RODRIGUEZ-NATAL A , CARNER J , et al . Knowledge-defined networking [J ] . ACM SIGCOMM Computer Communication Review , 2017 , 47 ( 3 ): 2 - 10 .

朱近康 . 知识+数据驱动学习:未来网络智能的基础 [J ] . 中兴通讯技术 , 2020 , 26 ( 4 ): 46 - 49 .

ZHU J K . Knowledge-and-data driven learning:foundation of future network intelligence [J ] . ZTE Technology Journal , 2020 , 26 ( 4 ): 46 - 49 .

HYUN J , TU N V , HONG J W K . Towards knowledge-defined networking using in-band network telemetry [C ] // Proceedings of 2018 IEEE/IFIP Network Operations and Management Symposium . Piscataway:IEEE Press , 2018 : 1 - 7 .

PHAM T A Q , HADJADJ-AOUL Y , OUTTAGARTS A . Deep reinforcement learning based qos-aware routing in knowledge-defined networking [C ] // Proceedings of the International Conference on Heterogeneous Networking for Quality,Reliability,Security and Robustness . Berlin:Springer , 2018 : 14 - 26 .

XIE J F , YU F R , HUANG T , 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 .

GARRAHAN J J , RUSSO P A , KITAMI K , et al . Intelligent network overview [J ] . IEEE Communications Magazine , 1993 , 31 ( 3 ): 30 - 36 .

MANOJ B S , RAO R R , ZORZI M . CogNet:a cognitive complete knowledge network system [J ] . IEEE Wireless Communications , 2008 , 15 ( 6 ): 81 - 88 .

YANG S Y , CHANG Y Y . An active and intelligent network management system with ontology-based and multi-agent techniques [J ] . Expert Systems with Applications , 2011 , 38 ( 8 ): 10320 - 10342 .

JIANG W , STRUFE M , SCHOTTEN H D . Intelligent network management for 5G systems:the SELFNET approach [C ] // Proceedings of 2017 European Conference on Networks and Communications (EuCNC) . Piscataway:IEEE Press , 2017 : 1 - 5 .

QI Q , WANG J Y , MA Z Y , et al . Knowledge-driven service offloading decision for vehicular edge computing:a deep reinforcement learning approach [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 5 ): 4192 - 4203 .

DONG T J , QI Q , WANG J Y , et al . Generative adversarial network-based transfer reinforcement learning for routing with prior knowledge [J ] . IEEE Transactions on Network and Service Management , 2021 , 18 ( 2 ): 1673 - 1689 .

WEI Y F , YU F R , SONG M , et al . Joint optimization of caching,computing,and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 2 ): 2061 - 2073 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceedings of the Advances in Neural Information Processing Systems .[S.l.:s.n. ] , 2017 : 5998 - 6008 .

SZIGETI T , ZACKS D , FALKNER M , et al . Cisco digital network architecture:intent-based networking for the enterprise [M ] .S.l.: Cisco Press , 2018 .

RIFTADI M , KUIPERS F . P4I/O:intent-based networking with P4 [C ] // Proceedings of 2019 IEEE Conference on Network Softwarization (NetSoft) . Piscataway:IEEE Press , 2019 : 438 - 443 .

李福亮 , 范广宇 , 王兴伟 , 等 . 基于意图的网络研究综述 [J ] . 软件学报 , 2020 , 31 ( 8 ): 2574 - 2587 .

LI F L , FAN G Y , WANG X W , et al . State-of-the-art survey of intent-based networking [J ] . Journal of Software , 2020 , 31 ( 8 ): 2574 - 2587 .

王敬宇 , 周铖 , 张蕾 , 等 . 知识定义的意图网络自治 [J ] . 电信科学 , 2021 , 37 ( 9 ): 1 - 13 .

WANG J Y , ZHOU C , ZHANG L , et al . Knowledge-defined intent-based network autonomy [J ] . Telecommunications Science , 2021 , 37 ( 9 ): 1 - 13 .

TANG W , WANG J Y , QI Q , et al . Deep graph alignment network [J ] . Neurocomputing , 2021 , 465 : 289 - 300 .

LIU C H , DAI Z P , YANG H M , et al . Multi-task-oriented vehicular crowdsensing:a deep learning approach [C ] // Proceedings of IEEE Conference on Computer Communications . Piscataway:IEEE Press , 2020 : 1123 - 1132 .

QIAN L P , WU Y , JIANG F L , et al . NOMA assisted multi-task multi-access mobile edge computing via deep reinforcement learning for industrial Internet of things [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 8 ): 5688 - 5698 .

QI Q , ZHANG L X , WANG J Y , et al . Scalable parallel task scheduling for autonomous driving using multi-task deep reinforcement learning [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 11 ): 13861 - 13874 .

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