  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.
