论文标题
数字双胞胎网络：机遇和挑战
Digital Twin Network: Opportunities and Challenges
论文作者
论文摘要
新兴网络应用程序的扩散（例如AR/VR，伸缩，实时通信）正在增加管理现代通信网络的困难。这些应用程序通常具有严格的要求（例如，超低确定性延迟），使网络运营商更难有效地管理其网络资源。在本文中，我们建议数字双网络（DTN）作为现代网络有效网络管理的关键推动者。我们描述了DTN的一般体系结构，并认为机器学习的最新趋势（ML）使构建DTN有效，准确地模仿了现实世界网络。此外，我们探索了能够开发DTN体系结构组件的主要ML技术。最后，我们描述了研究界在接下来的几年中必须应对的公开挑战，以便在现实世界情景中部署DTN。
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. These applications typically have stringent requirements (e.g., ultra-low deterministic latency), making it more difficult for network operators to manage their network resources efficiently. In this article, we propose the Digital Twin Network (DTN) as a key enabler for efficient network management in modern networks. We describe the general architecture of the DTN and argue that recent trends in Machine Learning (ML) enable building a DTN that efficiently and accurately mimics real-world networks. In addition, we explore the main ML technologies that enable developing the components of the DTN architecture. Finally, we describe the open challenges that the research community has to address in the upcoming years in order to enable the deployment of the DTN in real-world scenarios.
