自智算力网络：架构、技术与展望

doi:10.11959/j.issn.2096-3750.2023.00350

Abstract

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.

Key words: autonomous CNC, intent-driven, adaptive co-evolution, autonomous operation

CLC Number:

TN914

Xiaomao ZHOU, Qingmin JIA, Yujiao HU, Kai GUO, Qianpiao MA, Hui LIU, Renchao XIE. Autonomous computing and network convergence:architecture, technologies, and prospects[J]. Chinese Journal on Internet of Things, 2023, 7(4): 1-12.

Figures/Tables 6

References 45

[1]	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.
[2]	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.
[3]	NOUR B , MASTORAKIS S , MTIBAA A . Compute-less networking:perspectives,challenges,and opportunities[J]. IEEE Network, 2020,34(6): 259-265.
[4]	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.
[5]	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.
[6]	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.
[7]	中兴通信. 自智网络白皮书[R]. 2022.
	ZTE. White paper on autonomous network[R]. 2022.
[8]	IETF. Computing in the network research group (coinrg)[EB]. 2019.
[9]	IETF. Routing area working group (rtgwg)[EB]. 2019.
[10]	IETF. Computing in network research group,requirement of computing in network draft-liu-coinrg-requirement-02[R]. 2020.
[11]	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.
[12]	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.
[13]	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.
[14]	ZHANG S , CAO C , TANG X . Computing power network technology architecture based on SRv6[J]. ZTE Technology Journal, 2022,28(1): 11-15.
[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.
[16]	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.
[17]	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.
[18]	ITU-T. Focus group on machine learning for future networks including 5G[S]. 2020.
[19]	ITU-T. Framework for evaluating intelligence levels of future networks including IMT-2020:ITU-T Y.3173[S]. 2020.
[20]	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.
[21]	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.
[22]	ZHANG R , MAO Y , ZHAO W . Knowledge graphs completion via probabilistic reasoning[J]. Information Sciences, 2020(521): 144-159.
[23]	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.
[24]	WEI Y , PENG M , LIU Y . Intent-based networks for 6G:insights and challenges[J]. Digital Communications and Networks, 2020,6(3): 270-280.
[25]	Gartner. Innovation insight:intent-based networking systems[EB]. 2017.
[26]	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.
[27]	PéREZ-SOLER S , GUERRA E , DE LARA J . Model-driven chatbot development[M]. Conceptual Modeling. Cham: Springer International Publishing, 2020: 207-222.
[28]	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.
[29]	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.
[30]	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.
[31]	CHEN X , JIA S , XIANG Y . A review:knowledge reasoning over knowledge graph[J]. Expert Systems With Applications, 2020(141): 112948.
[32]	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.
[33]	SARKER I H . Deep learning:a comprehensive overview on techniques,taxonomy,applications and research directions[J]. SN Computer Science, 2021,2(6): 420.
[34]	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.
[35]	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.
[36]	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.
[37]	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.
[38]	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.
[39]	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.
[40]	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.
[41]	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.
[42]	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.
[43]	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.
[44]	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.
[45]	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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Autonomous computing and network convergence:architecture, technologies, and prospects

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References 45

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