Generalized Deterministic Identification Networks

YANG Dong; CHENG Zong-rong; TIAN Wei-kang; WANG Hong-chao; ZHANG Hong-ke; TAN Bin; ZHAO Zhi-yong

doi:10.12263/DZXB.20230603

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Generalized Deterministic Identification Networks

PAPERS | 更新时间：2026-04-10

- Generalized Deterministic Identification Networks
- ACTA ELECTRONICA SINICA Vol. 52, Issue 1, Pages: 1-18(2024)
- 作者机构：
  
  1.北京交通大学电子信息工程学院,北京 100044
  2.中兴通讯股份有限公司,上海 201203
  3.中兴通讯股份有限公司,北京 100020
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFB2901302)
- DOI：10.12263/DZXB.20230603
  CLC： TN913.21;
- Received：27 June 2023，
  
  Revised：2023-11-07，
  
  Published：25 January 2024
- 稿件说明：
移动端阅览
杨冬,程宗荣,田伟康等.广义确定性标识网络[J].电子学报,2024,52(01):1-18.

YANG Dong,CHENG Zong-rong,TIAN Wei-kang,et al.Generalized Deterministic Identification Networks[J].ACTA ELECTRONICA SINICA,2024,52(01):1-18.
杨冬,程宗荣,田伟康等.广义确定性标识网络[J].电子学报,2024,52(01):1-18. DOI： 10.12263/DZXB.20230603.

YANG Dong,CHENG Zong-rong,TIAN Wei-kang,et al.Generalized Deterministic Identification Networks[J].ACTA ELECTRONICA SINICA,2024,52(01):1-18. DOI： 10.12263/DZXB.20230603.

摘要

随着智能制造、智能交通等重大国家战略实施，确定性成为信息网络尤其是行业专网的新焦点.现有确定性网络技术始终关注网络传输要素（带宽、时隙等）来保障数据流的确定性传输.然而，仅靠保障传输要素无法支撑新兴行业应用的多样化需求.例如，在算网融合场景，智算任务要求同时保障传输与计算要素的确定性来实现高性能通信；在绿色通信场景，需要考虑节点能量要素的确定性以维持网络稳定运行.针对上述需求，本文基于前期提出的标识网络技术，研究面向传输、计算、存储、能量等多要素的广义确定性网络.首先提出广义确定性标识网络架构，包括差异化服务层、异构融合网络层和智慧化适配层.差异化服务层和异构融合网络层，分别实现差异化确定性应用需求和异构化确定性网络要素的统一标识和描述，并通过标识解析映射实现确定性信息向智慧化适配层的统一封装和传递；智慧化适配层完成差异化确定性应用需求和异构化确定性网络要素的适配.现有确定性资源适配方法，即使仅考虑单一网络内的基本确定性要素，仍面临计算时间长、求解复杂性高、灵活度低等问题，为了支持更加复杂的多确定性要素、多种异构网络的协同适配，设计了基于深度强化学习的端到端的确定性调度（End-to-end Deterministic resource scheduling，E2eDet）算法，该算法可统一化、端到端地为混合数据流协同分配多种确定性网络资源，满足不同应用的差异化确定性需求.实验表明，E2eDet比DeepCQF和Random算法分别提升了28.4%和6.38倍数据流调度数量，同时E2eDet可以较好地权衡计算时间和调度能力.

Abstract

With the implementation of major national strategies in industries such as intelligent manufacturing and transportation

determinism has become a new focus of information networks

especially industry-specific networks. Existing deterministic network technologies provide deterministic guarantees based on network transmission elements (e.g.

bandwidth or time slots). However

relying solely on network transmission elements does not support the diverse needs of emerging industry applications. For example

in computing network integration scenarios

intelligent computing tasks require the determinism of transmission and computing elements to achieve high-performance communication. In green communication scenarios

the determinism of node energy elements needs to be considered to maintain network operation stability. In response to the above requirements

this paper studies generalized deterministic identification networks with respect to multiple elements such as transmission

computing

storage

and energy based on a previously proposed network identification technology. First

a generalized deterministic identification network architecture is proposed that includes a differentiated service layer

a heterogeneous network layer

and an intelligent adaptation layer. The differentiated service and heterogeneous network layers uniformly identify the deterministic applications and networks. The intelligent adaptation layer schedules the network resources in units of flow. Existing deterministic resource scheduling methods

even if they only consider the basic deterministic elements in a single network

still face problems such as long computational time

high complexity

and low flexibility. To support a more complex collaborative adaptation of multiple deterministic elements

the end-to-end deterministic resource scheduling (E2eDet) algorithm

which is based on deep reinforcement learning

is designed. To meet the various deterministic requirements of different applications

E2eDet uniformly and collaboratively allocates multiple deterministic network resources for mixed data streams from end to end. Experimental results show that E2eDet increases the amount of data flow scheduling by 28.4% and 6.38× when compared with the DeepCQF and Random algorithms

respectively. Moreover

E2eDet can better balance the computational time and scheduling ability.

关键词

Keywords

references

BELLO L LO , STEINER W . A perspective on IEEE time-sensitive networking for industrial communication and automation systems [J]. Proceedings of the IEEE , 2019 , 107 ( 6 ): 1094 - 1120 .

中华人民共和国工业和信息化部 . 工业互联网时间敏感网络需求及场景 : YD/T 4134—2022 [S]. 北京 : 人民邮电出版社 , 2022 : 2 - 3 .

Ministry of Industry and Information of the People’s Republic of China . Industrial Internet Time Sensitive Network Requirements and Scenarios : YD/T 4134—2022 [S]. Beijing : Posts & Telecom Press , 2022 : 2 - 3 . (in Chinese)

杨明亮 , 吴春明 , 沈丛麒 , 等 . 基于IEEE 802.1的TSN交换机队列调度技术研究 [J]. 电子学报 , 2022 , 50 ( 9 ): 2090 - 2095 .

YANG M L , WU C M , SHEN C Q , et al . Research on queue scheduling strategy on IEEE 802.1 based TSN switch [J]. Acta Electronica Sinica , 2022 , 50 ( 9 ): 2090 - 2095 . (in Chinese)

FINN N . Introduction to time-sensitive networking [J]. IEEE Communications Standards Magazine , 2018 , 2 ( 2 ): 22 - 28 .

YANG D , ZHANG W T , YE Q , et al . DetFed: Dynamic resource scheduling for deterministic federated learning over time-sensitive networks [J/OL]. IEEE Transactions on Mobile Computing . 2023 . https://ieeexplore.ieee.org/document/10210522 https://ieeexplore.ieee.org/document/10210522 .

YANG D , CUI E F , WANG H C , et al . EH-Edge: An energy harvesting-driven edge IoT platform for online failure prediction of rail transit vehicles: A case study of a cloud, edge, and end device collaborative computing paradigm [J]. IEEE Vehicular Technology Magazine , 2021 , 16 ( 2 ): 95 - 103 .

张宏科 , 冯博昊 , 权伟 . 智融标识网络基础研究 [J]. 电子学报 , 2019 , 47 ( 5 ): 977 - 982 .

ZHANG H K , FENG B H , QUAN W . Fundamental research on smart integration identifier networking [J]. Acta Electronica Sinica , 2019 , 47 ( 5 ): 977 - 982 . (in Chinese)

YANG D , GONG K , REN J , et al . TC-flow: Chain flow scheduling for advanced industrial applications in time-sensitive networks [J]. IEEE Network , 2022 , 36 ( 2 ): 16 - 24 .

聂宏蕊 , 李绍胜 , 刘勇 . 时间敏感网络中基于IEEE 802 .1Qch标准的优化调度机制[J]. 通信学报 , 2022, 43 ( 9 ): 12 - 26 .

NIE H R , LI S S , LIU Y . Optimized scheduling mechanism based on IEEE 802.1Qch standard in time-sensitive networking [J]. Journal on Communications , 2022 , 43 ( 9 ): 12 - 26 . (in Chinese)

YANG D , CHENG Z R , ZHANG W T , et al . Burst-aware time-triggered flow scheduling with enhanced multi-CQF in time-sensitive networks [J/OL]. IEEE/ACM Transactions on Networking . 2023 . https://ieeexplore.ieee.org/document/10101832 https://ieeexplore.ieee.org/document/10101832 .

YUAN Y Z , CAO X , LIU Z X , et al . Adaptive priority adjustment scheduling approach with response-time analysis in time-sensitive networks [J]. IEEE Transactions on Industrial Informatics , 2022 , 18 ( 12 ): 8714 - 8723 .

JIANG X F , HUANG Y H , LI J J , et al . Spatio-temporal routing, redundant coding and multipath scheduling for deterministic satellite network transmission [J]. IEEE Transactions on Communications , 2023 , 71 ( 5 ): 2860 - 2875 .

ROST P M , KOLDING T . Performance of integrated 3GPP 5G and IEEE TSN networks [J]. IEEE Communications Standards Magazine , 2022 , 6 ( 2 ): 51 - 56 .

YANG L , WEI Y F , YU F R , et al . Joint routing and scheduling optimization in time-sensitive networks using graph-convolutional-network-based deep reinforcement learning [J]. IEEE Internet of Things Journal , 2022 , 9 ( 23 ): 23981 - 23994 .

CHENG Z R , YANG D , ZHANG W T , et al . DeepCQF: Making CQF scheduling more intelligent and practicable [C]// ICC 2022 - IEEE International Conference on Communications . Piscataway : IEEE , 2022 : 1 - 6 .

姜旭艳 , 严锦立 , 全巍 , 等 . SSA: 一种面向CQF模型的TSN资源调度算法 [J]. 东北大学学报(自然科学版) , 2020 , 41 ( 6 ): 784 - 791 .

JIANG X Y , YAN J L , QUAN W , et al . SSA: CQF-oriented scheduling algorithm in time-sensitive networking [J]. Journal of Northeastern University (Natural Science) , 2020 , 41 ( 6 ): 784 - 791 . (in Chinese)

Report ITU . Guidelines for Evaluation of Radio Interface Technologies for IMT-2020 [R]. Geneva : ITU Press , 2017 .

ADAME T , CARRASCOSA M , BELLALTA B . The TMB path loss model for 5 GHz indoor WiFi scenarios: On the empirical relationship between RSSI, MCS, and spatial streams [C]// 2019 Wireless Days (WD) . Piscataway : IEEE , 2019 : 1 - 8 .

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