Network Digital Twin: Context, Enabling Technologies and Opportunities

TL;DR

This paper introduces the concept of Network Digital Twin (NDT), a real-time, data-driven network modeling tool leveraging machine learning to improve management and optimization of modern communication networks with complex requirements.

Contribution

It presents the architecture of NDT, demonstrates its application in network performance evaluation and routing optimization, and discusses open challenges for deployment.

Findings

ML-based NDT can accurately model network performance

NDT enables real-time network management and optimization

Open challenges include deployment and scalability

Abstract

The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail stringent network requirements (e.g., ultra-low deterministic latency), which hinders network operators to manage their resources efficiently. In this article, we introduce the network digital twin (NDT), a renovated concept of classical network modeling tools whose goal is to build accurate data-driven network models that can operate in real-time. We describe the general architecture of the NDT and argue that modern machine learning (ML) technologies enable building some of its core components. Then, we present a case study that leverages a ML-based NDT for network performance evaluation and apply it to routing optimization in a QoS-aware use case. Lastly, we describe some key open challenges and research opportunities yet to be explored to achieve effective deployment of NDTs in real-world networks.