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摘要:
随着5G商用规模部署、下一代互联网IPv6的深化应用, 新一代网络技术的发展引发产业界的关注. 网络的智能化被认为是新一代网络发展的趋势. 网络为数字化社会的信息传输提供了基础, 而网络本身的数字化是智能化发展的先决条件. 面向数字化、智能化的新一代网络发展目标, 本文首次系统化提出了 “数字孪生网络(DTN: Digital twin network)” 的概念, 给出了系统架构设计, 分析了DTN的关键技术. 通过对DTN发展挑战的分析, 本文指出了未来 “数字孪生网络” 的发展方向.
Abstract:With the commercial deployment of 5G and the migration of internet from IPv4 to IPv6, the new development of the network technology has highly attracted the attention of the industry. The intelligentization of network is believed as the trend of the new generation of network development. The network provides the foundation for the information transmission in the digital society, and the digitalization of the network itself is the prerequisite for the intelligent development. Facing the goal of the new generation of digital and intelligent network, this paper introduces the new concept of “digital twin network (DTN)”, designs the system architecture, and analyzes the key technologies of DTN. By investigating the challenges of DTN, this paper points out the future development direction of digital twin network.
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图 6 基于本体的网元和拓扑模型构建流程
Fig. 6 The process of constructing network element and topology model based on ontology
表 1 DTN、SDN和平行网络对比
Table 1 Comparison of DTN, SDN and parallel networks
维度 数字孪生网络 DTN 软件定义网络 SDN 平行网络 物理对象 各种类型的物理网元 具备 SDN 特性的物理网元 各种类型的物理网元 架构层次 物理层、孪生层和网络应用层 物理层、控制层和管理层 物理层、人工网络 + 计算实验层 虚拟网络 物理网络的孪生镜像, 孪生层通过
统一数据建模构建N/A 基于人工系统生成物理网络对应的人工网络;
人工网络基于 SDN 架构构建虚实映射 通过功能映射模型对网络应用进行仿真和迭代
优化; 注重虚实映射的实时性和精确性N/A 通过人工网络逼近物理网络; 更加强调
计算实验和外在行为的干预分析方法 基于孪生层的共享数据仓库, 充分利用大数据
分析、人工智能技术, 通过模型化实例的迭代
仿真, 实现网络的全局动态实时控制和优化只具备基本的网络控制和管
理能力, 缺乏对于复杂网络
的动态控制和优化能力通过对人工网络(以及人工数据)进行各种实
验, 对网络行为进行分析和预测, 进而平
行执行至物理网络并根据反馈迭代优化
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