自智算力网络：架构、技术与展望

周晓茂; 贾庆民; 胡玉姣; 郭凯; 马千飘; 刘辉; 谢人超

doi:10.11959/j.issn.2096-3750.2023.00350

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自智算力网络：架构、技术与展望

理论与技术 | 更新时间：2024-08-16

- 自智算力网络：架构、技术与展望
- Autonomous computing and network convergence:architecture, technologies, and prospects
- 物联网学报 2023年7卷第4期页码：1-12
- 作者机构：
  
  1. 网络通信与安全紫金山实验室，江苏南京 211111
  2. 北京邮电大学，北京 100876
- 作者简介：
  
  [ "周晓茂（1993- ），男，博士，网络通信与安全紫金山实验室研究员，主要研究方向为边缘计算、人工智能、算力网络、工业互联网等" ]
  [ "贾庆民（1990- ），男，博士，网络通信与安全紫金山实验室研究员，主要研究方向为算力网络、工业互联网、确定性网络、人工智能等" ]
  [ "胡玉姣（1993- ），女，博士，网络通信与安全紫金山实验室研究员，主要研究方向为信息物理融合系统、算力网络、人工智能、云边协同等" ]
  [ "郭凯（1991- ），男，博士，网络通信与安全紫金山实验室研究员，主要研究方向为算力网络、工业互联网、确定性网络、人工智能等" ]
  [ "马千飘（1993- ），男，博士，网络通信与安全紫金山实验室研究员，主要研究方向为边缘计算、联邦学习、分布式机器学习等" ]
  [ "刘辉（1983- ），男，博士，网络通信与安全紫金山实验室高级工程师，主要研究方向为边缘智能体系架构、算力网络、云计算等" ]
  [ "谢人超（1984- ），男，博士，北京邮电大学教授、博士生导师，主要研究方向为未来网络体系架构设计、信息中心网络、移动网络内容分发等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(92267301);紫金山英才-江宁百家湖计划项目;Purple Mountain Talents-Jiangning Baijia Lake Plan Program(74072203-3)
- DOI：10.11959/j.issn.2096-3750.2023.00350
  中图分类号： TN914
- 纸质出版日期：2023-12-20，
  
  网络出版日期：2023-12，
- 稿件说明：
移动端阅览
周晓茂, 贾庆民, 胡玉姣, 等. 自智算力网络：架构、技术与展望[J]. 物联网学报, 2023,7(4):1-12.

XIAOMAO ZHOU, QINGMIN JIA, YUJIAO HU, et al. Autonomous computing and network convergence:architecture, technologies, and prospects. [J]. Chinese journal on internet of things, 2023, 7(4): 1-12.
周晓茂, 贾庆民, 胡玉姣, 等. 自智算力网络：架构、技术与展望[J]. 物联网学报, 2023,7(4):1-12. DOI： 10.11959/j.issn.2096-3750.2023.00350.

XIAOMAO ZHOU, QINGMIN JIA, YUJIAO HU, et al. Autonomous computing and network convergence:architecture, technologies, and prospects. [J]. Chinese journal on internet of things, 2023, 7(4): 1-12. DOI： 10.11959/j.issn.2096-3750.2023.00350.

摘要

针对算力网络（CNC

computing and network convergence）中的新型业务场景和网络高度智能化的需求，阐述了自智算力网络（Auto-CNC

autonomous CNC）的技术发展理念，即通过将智能引入算网全生命流程的方式实现资源一体化、流程自动化、系统智能化，简要分析了算力网络的研究现状和现存问题，总结了自智算力网络应具备意图驱动的算网融合、算网闭环自治、网-算-智协同自适演进的关键特征，设计了自智算力网络的参考架构，分析了其关键技术，介绍了相关的初阶探索工作，并对自智算力网络的未来发展进行了展望。

Abstract

In view of the new service scenarios and the demand for high intelligence network in computing and network convergence (CNC)

the concept of autonomous CNC (Auto-CNC) is elaborated

where intelligence was introduced into all the aspects of CNC

including resource integration

process automation

and system intelligence.The current research directions and remaining challenges of CNC were introduced

and three key features

i.e.

intent-driven computing network

the autonomous system operation and the adaptive co-evolution of communication

computing intelligence

were summarized from the proposed Auto-CNC.Meanwhile

the reference architecture and key technologies of Auto-CNC were described

which were followed by several preliminary exploration cases.Finally

future research trends and technical advice were discussed and recommended.

关键词

自智算力网络意图驱动协同自适演进自智运行

Keywords

autonomous CNCintent-drivenadaptive co-evolutionautonomous operation

references

贾庆民, 丁瑞, 刘辉 ,等. 算力网络研究进展综述[J]. 网络与信息安全学报, 2021,7(5): 1-12.

JIA Q M, DING R, LIU H ,et al. Survey on research progress for compute first networking[J]. Chinese Journal of Network and Information Security, 2021,7(5): 1-12.

KROEL M, MASTORAKIS S, ORAN D ,et al. Compute first networking:distributed computing meets ICN[C]// The 6th ACM Conference on Information-Centric Networking. New York:ACM Press, 2019: 67-77.

NOUR B, MASTORAKIS S, MTIBAA A . Compute-less networking:perspectives,challenges,and opportunities[J]. IEEE Network, 2020,34(6): 259-265.

曹畅, 唐雄燕 . 算力网络关键技术及发展挑战分析[J]. 信息通信技术与政策, 2021,47(3): 6-11.

CAO C, TANG X Y . Analysis on key technologies and development challenges of computing network[J]. Information and Communications Technology and Policy, 2021,47(3): 6-11.

中国联通网络技术研究院. 中国联通算力网络白皮书[R]. 2019.

China Unicom Network Technology Research Institute. White paper on China Unicom computing power network[R]. 2019.

中国移动研究院. 算力感知网络技术白皮书[R]. 2019.

China Mobile Research Institute. White paper on computing-aware network technology[R]. 2019.

中兴通信. 自智网络白皮书[R]. 2022.

ZTE. White paper on autonomous network[R]. 2022.

IETF. Computing in the network research group (coinrg)[EB]. 2019.

IETF. Routing area working group (rtgwg)[EB]. 2019.

IETF. Computing in network research group,requirement of computing in network draft-liu-coinrg-requirement-02[R]. 2020.

DI Z, CAO Y, QIU C ,et al. New computing power network architecture and application case analysis[J]. Journal of Computer Applications, 2022,42(6): 1656-1662.

HUANG G, SHI W, TAN B . Computing power network resources based on SRv6 and its service arrangement and scheduling[J]. ZTE Technology Journal, 2021,27(3): 23-28.

TANG X Y, CAO C, WANG Y X ,et al. Computing power network:the architecture of convergence of computing and networking towards 6G requirement[J]. China Communications, 2021,18(2): 175-185.

ZHANG S, CAO C, TANG X . Computing power network technology architecture based on SRv6[J]. ZTE Technology Journal, 2022,28(1): 11-15.

梁冰, 纪雯 . 基于次模优化的边云协同多用户计算任务迁移方法[J]. 通信学报, 2020,41(10): 25-36.

LIANG B, JI W . Multiuser computation offloading for edge-cloud collaboration using submodular optimization[J]. Journal on Communications, 2020,41(10): 25-36.

胡玉姣, 贾庆民, 孙庆爽 ,等. 融智算力网络及其功能架构[J]. 计算机科学, 2022,49(9): 249-259.

HU Y J, JIA Q M, SUN Q S ,et al. Functional architecture to intelligent computing power network[J]. Computer Science, 2022,49(9): 249-259.

通信世界. 中国移动牵头 3GPP 成立自治网络分级标准项目[EB]. 2020.

Communication World Network. China Mobile leads 3GPP to establish autonomous network classification standard project[EB]. 2020.

ITU-T. Focus group on machine learning for future networks including 5G[S]. 2020.

ITU-T. Framework for evaluating intelligence levels of future networks including IMT-2020:ITU-T Y.3173[S]. 2020.

XU X X, SUN J, WANG C Y ,et al. A novel hybrid CNN-LSTM compensation model against DoS attacks in power system state estimation[J]. Neural Processing Letters, 2022,54(3): 1597-1621.

CHEN W X, XU H W, LI Z Y ,et al. Unsupervised anomaly detection for intricate KPIs via adversarial training of VAE[C]// Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 1891-1899.

ZHANG R, MAO Y, ZHAO W . Knowledge graphs completion via probabilistic reasoning[J]. Information Sciences, 2020(521): 144-159.

GUO S Y, DAI Y, XU S Y ,et al. Trusted cloud-edge network resource management:DRL-driven service function chain orchestration for IoT[J]. IEEE Internet of Things Journal, 2020,7(7): 6010-6022.

WEI Y, PENG M, LIU Y . Intent-based networks for 6G:insights and challenges[J]. Digital Communications and Networks, 2020,6(3): 270-280.

Gartner. Innovation insight:intent-based networking systems[EB]. 2017.

QIAO F F, ZHU X J . Domain intelligent Q＆A user intention recognition based on keyword separation[C]// Proceedings of 2020 International Conference on Culture-oriented Science ＆ Technology (ICCST). Piscataway:IEEE Press, 2020: 224-229.

PÉREZ-SOLER S, GUERRA E, DE LARA J . Model-driven chatbot development[M]. Conceptual Modeling. Cham: Springer International Publishing, 2020: 207-222.

WU S F . Internet public information an text data mining and intelligence influence analysis for user intent understanding[J]. Journal of Intelligent ＆ Fuzzy Systems, 2020,38(1): 487-494.

WANG Y, FORBES R, CAVIGIOLI C ,et al. Network management and orchestration using artificial intelligence:overview of ETSI ENI[J]. IEEE Communications Standards Magazine, 2018,2(4): 58-65.

GU Z Y, SHE C Y, HARDJAWANA W ,et al. Knowledge-assisted deep reinforcement learning in 5G scheduler design:from theoretical framework to implementation[J]. IEEE Journal on Selected Areas in Communications, 2021,39(7): 2014-2028.

CHEN X, JIA S, XIANG Y . A review:knowledge reasoning over knowledge graph[J]. Expert Systems With Applications, 2020(141): 112948.

邓灵莉, 顾宁伦, 袁向阳 ,等. 网络智能化标准、开源与产业研究[J]. 电信科学, 2021,37(10): 12-21.

DENG L L, GU N L, YUAN X Y ,et al. Network intelligence standards,open source and industry research[J]. Telecommunications Science, 2021,37(10): 12-21.

SARKER I H . Deep learning:a comprehensive overview on techniques,taxonomy,applications and research directions[J]. SN Computer Science, 2021,2(6): 420.

段晓东, 姚惠娟, 付月霞 ,等. 面向算网一体化演进的算力网络技术[J]. 电信科学, 2021,37(10): 76-85.

DUAN X D, YAO H J, FU Y X ,et al. Computing force network technologies for computing and network integration evolution[J]. Telecommunications Science, 2021,37(10): 76-85.

THAKKAR H K, SAHOO P K, VEERAVALLI B . RENDA:resource and network aware data placement algorithm for periodic workloads in cloud[J]. IEEE Transactions on Parallel and Distributed Systems, 2021,32(12): 2906-2920.

HAGHNEGAHDAR L, JOSHI S S, DAHOTRE N B . From IoT-based cloud manufacturing approach to intelligent additive manufacturing:industrial internet of things—an overview[J]. The International Journal of Advanced Manufacturing Technology, 2022,119(3): 1461-1478.

SUN Z J, YANG H, LI C ,et al. Cloud-edge collaboration in industrial internet of things:a joint offloading scheme based on resource prediction[J]. IEEE Internet of Things Journal, 2022,9(18): 17014-17025.

DE S, DEY S, BHATIA S ,et al. An introduction to data mining in social networks[J]. Advanced Data Mining Tools and Methods for Social Computing, 2022: 1-25.

VIJAYABASKAR KUMANAN T . Link quality and energy-aware metric-based routing strategy in WSNS[M]. Intelligent Computing in Engineering. Singapore: Springer Singapore, 2020: 533-539.

SAMAL T, KABAT M R . Energy-efficient time-sharing multichannel MAC protocol for wireless body area networks[J]. Arabian Journal for Science and Engineering, 2022,47(2): 1791-1804.

LI C, TANG J, TANG H, LUO Y . Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment[J]. Future Generation Computer Systems, 2019(95): 249-264.

刘泽宁, 李凯, 吴连涛 ,等. 多层次算力网络中代价感知任务调度算法[J]. 计算机研究与发展, 2020,57(9): 1810-1822.

LIU Z N, LI K, WU L T ,et al. CATS:cost aware task scheduling in multi-tier computing networks[J]. Journal of Computer Research and Development, 2020,57(9): 1810-1822.

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.

ZHOU X, JIA Q, XIE R . NestFL:efficient federated learning through progressive model pruning in heterogeneous edge computing[C]// The 28th Annual International Conference on Mobile Computing and Networking.[S.l.:s.n.], 2022: 817-819.

SHUAI L, WANG K, ZHANG L ,et al. Global-local integration for GNN-based anomalous device state detection in industrial control systems[J]. Expert Systems With Applications, 2022(209): 118345.

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