知识定义的意图网络自治

doi:10.11959/j.issn.1000-0801.2021220

Telecommunications Science ›› 2021, Vol. 37 ›› Issue (9): 1-13.doi: 10.11959/j.issn.1000-0801.2021220

Knowledge-defined intent-based network autonomy

Jingyu WANG¹, Cheng ZHOU², Lei ZHANG³, Cong LIU², Zirui ZHUANG¹, Hongwei YANG², Danyang CHEN², Yanhong ZHU², Lu LU², Jianxin LIAO¹

¹ State Key of Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing 100876, China
² China Mobile Research Institute, Beijing 100053, China
³ China United Network Communications Co., Ltd., Beijing 100033, China

Revised:2021-09-10 Online:2021-09-20 Published:2021-09-01
Supported by:
The National Key Research and Development Program of China(2020YFB1807803);The National Key Research and Development Program of China(2020YFB1807800);The National Natural Science Foundation of China(62071067);The National Natural Science Foundation of China(62001054);Ministry of Education and China Mobile Joint Fund(MCM20200202);Ministry of Education and China Mobile Joint Fund(MCM20180101);Funded by Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center

Abstract

Abstract:

The complexity of the network determines that the autonomy of the intention network can’t be achieved at one go.The key is to break through multiple problem domains of network management and control from a global perspective, to summarize the network rules, mechanisms and strategies into knowledge, to build the knowledge space of resource allocation in the whole scene, and finally to realize the ubiquitous intelligence of the intention network.Focusing on 6G intention network, which takes knowledge-defined intelligence as the key enabling technology to improve the perception and decision-making closed-loop ability of intention network, and constructs a self-learning, self-operating and self-maintaining intention network.Achieving full autonomy in 6G networks is a long-term goal, which requires step by step evolvements, from providing repeat operation alternatives, to performing perception and monitoring on network environment and equipment states, to making decisions according to a number of factors and strategies, to effectively sensing end-user experience, and finally to a fully autonomous intelligent network that senses the intention of operators and users, self-optimizes and self-evolves.

Key words: intent-based network, knowledge-defined network, network autonomy, self-operation and maintenance, perception and monitoring

CLC Number:

TP393

Jingyu WANG, Cheng ZHOU, Lei ZHANG, Cong LIU, Zirui ZHUANG, Hongwei YANG, Danyang CHEN, Yanhong ZHU, Lu LU, Jianxin LIAO. Knowledge-defined intent-based network autonomy[J]. Telecommunications Science, 2021, 37(9): 1-13.

Figures/Tables 7

References 25

[1]	张平, 许晓东, 韩书君 ,等. 智简无线网络赋能行业应用[J]. 北京邮电大学学报, 2020,43(6): 1-9.
	ZHANG P , XU X D , HAN S J ,et al. Entropy reduced mobile networks empowering industrial applications[J]. Journal of Bei-jing University of Posts and Telecommunications, 2020,43(6): 1-9.
[2]	华为技术有限公司. 智简网络（IDN）白皮书[S]. 2019.
	Huawei Technologies Co.,Ltd.. Intent-driven network white paper[S]. 2019.
[3]	ZEYDAN E , TURK Y . Recent advances in intent-based networking:a survey[C]// Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). Piscataway:IEEE Press, 2020: 1-5.
[4]	SZIGETI T , ZACKS D ,, FALKNER M , ARENA S . Cisco digital network architecture:intent-based networking for the enterprise[M]. New Jersey: Cisco Press, 2018.
[5]	LAURIER W , . Blockchain value networks[C]// Proceedings of 2019 IEEE Social Implications of Technology (SIT) and Information Management (SITIM). Piscataway:IEEE Press, 2019: 1-6.
[6]	VALTANEN K , BACKMAN J , YRJ?L? S , . Creating value through blockchain powered resource configurations:analysis of 5G network slice brokering case[C]// Proceedings of 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). Piscataway:IEEE Press, 2018: 185-190.
[7]	闫实, 彭木根, 王文博 . 通信感知计算融合:6G 网络愿景与关键技术[J]. 北京邮电大学学报, 2021: 1-10.
	YAN S , PENG M G , WANG W B . Integration of communica-tion,sensing and computing:the vision and key technologies of 6G[J]. Journal of Beijing University of Posts and Telecommu-nications, 2021: 1-10.
[8]	姚惠娟, 陆璐, 段晓东 . 算力感知网络架构与关键技术[J]. 中兴通讯技术, 2021,27(3): 7-11.
	YAO H J , LU L , DUAN X D . Architecture and key technolo-gies for computing-aware networking[J]. ZTE Technology Journal, 2021,27(3): 7-11.
[9]	华为技术有限公司. 自动驾驶网络解决方案白皮书[R]. 2020.
	Huawei Technologies Co.,Ltd.. ADN solution white paper (au-tonomous driving network)[R]. 2020.
[10]	CLARK D D , PARTRIDGE C , RAMMING J C ,et al. A knowledge plane for the Internet[C]// SIGCOMM '03:Proceedings of the 2003 Conference on Applications,Technologies,Architectures,and Protocols for Computer Communications, 2003: 3-10.
[11]	ETSI. Experiential networked intelligence (ENI); system architecture:ETSI GS ENI 005[S]. 2019.
[12]	3GPP. Telecommunication management; study on scenarios for Intent driven management services for mobile networks:TR 28.812[S]. 2020.
[13]	通信世界网. 中国移动牵头 3GPP 成立自治网络分级标准项目[N]. 2020.
	Communication World Network. China Mobile leads 3GPP to es-tablish autonomous network classification standard project[N]. 2020.
[14]	ITU. Focus group on machine learning for future networks including 5G[S]. 2020.
[15]	Gartner. Innovation insight:intent-based networking systems[EB]. 2017.
[16]	GSMA. AI in network use cases in China[S]. 2019.
[17]	ITU-T. Framework for evaluating intelligence levels of future networks including IMT-2020:ITU-T Y.3173[S]. 2020.
[18]	MESTRES A , RODRIGUEZ-NATAL A , CARNER J ,et al. Knowledge-defined networking[J]. ACM SIGCOMM Computer Communication Review, 2017,47(3): 2-10.
[19]	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.
[20]	ZHUANG Z R , WANG J Y , QI Q ,et al. Toward greater intelligence in route planning:a graph-aware deep learning approach[J]. IEEE Systems Journal, 2020,14(2): 1658-1669.
[21]	ZHUANG Z R , WANG J Y , QI Q ,et al. A case-based decision system for routing in packet-switched networks[C]// Proceedings of 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). Piscataway:IEEE Press, 2018: 1-2.
[22]	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.
[23]	ZHUANG Z R , WANG J Y ， QI Q ,et al. Adaptive and robust routing with Lyapunov-based deep RL in MEC networks enabled by blockchains[J]. IEEE Internet of Things Journal, 2021,8(4): 2208-2225.
[24]	FU X Y , YU F R ， WANG J Y ,et al. Dynamic service function chain embedding for NFV-enabled IoT: a deep reinforcement learning approach[[J]. IEEE Transactions on Wireless Communications, 2020,19(1): 507-519.
[25]	WANG J Y ， JING Y H , QI Q ,et al. ALSR: an adaptive label screening and relearning approach for interval-oriented anomaly detection[J]. Expert Systems With Applications, 2019,136: 94-104.

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