数字孪生网络：机遇与挑战
Digital Twin Network: Opportunities and Challenges
摘要：
新兴网络应用的激增（例如，AR/VR、手术、实时通信）正在增加管理现代通信网络的难度。这些应用通常具有严格的要求（例如，超低的确定性延迟），使得网络运营商更难以有效地管理其网络资源。在这篇文章中，我们提出了数字孪生网络（DTN）作为现代网络中高效网络管理的关键使能器。我们描述的DTN的一般架构，并认为，最近的趋势在机器学习（ML），使建立一个DTN，有效地和准确地模仿现实世界的网络。此外，我们还探讨了能够开发DTN架构组件的主要ML技术。最后，我们描述了开放的挑战，研究界必须解决在未来几年中，以使DTN在现实世界中的场景部署。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
