Digital Twin Network: Opportunities and Challenges

TL;DR

This paper introduces the Digital Twin Network (DTN) as an innovative approach to enhance management of modern communication networks with emergent applications, leveraging machine learning to accurately simulate real networks and discussing future challenges.

Contribution

It proposes a comprehensive DTN architecture utilizing ML technologies and highlights open challenges for real-world deployment of DTNs in modern networks.

Findings

ML enables efficient and accurate network simulation

DTN architecture can improve network management

Open challenges identified for deployment

Abstract

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.