【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.更多还原

【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.更多还原