广义确定性标识网络  

Generalized Deterministic Identification Networks

作　　者：杨冬[1] 程宗荣 田伟康 王洪超[1] 张宏科[1] 谭斌[2] 赵志勇[3] YANG Dong;CHENG Zong-rong;TIAN Wei-kang;WANG Hong-chao;ZHANG Hong-ke;TAN Bin;ZHAO Zhi-yong(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;ZTE Corporation,Shanghai 201203,China;ZTE Corporation,Beijing 100020,China)

机构地区：[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

出　　处：《电子学报》2024年第1期1-18,共18页Acta Electronica Sinica

基　　金：国家重点研发计划(No.2022YFB2901302)。

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.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.Exist-ing deterministic network technologies provide deterministic guarantees based on network transmission elements(e.g.,band-width or time slots).However,relying solely on network transmission elements does not support the diverse needs of emerg-ing 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 communica-tion 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 mul-tiple 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 heteroge-neous network layers uniformly identify the deterministic applications and networks.The intelligent adaptation layer sched-ules 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 de-terministic ne

关 键 词：广义确定性网络 完备标识空间 网络体系架构 深度强化学习 网络资源调度 

分 类 号：TN913.21[电子电信—通信与信息系统]