Digital Twin for Networking: A Data-driven Performance Modeling Perspective

TL;DR

This paper explores the use of data-driven digital twin networks to improve performance evaluation in complex, heterogeneous networks, emphasizing fidelity, efficiency, and flexibility.

Contribution

It compares data-driven methods for digital twin networks, discusses their challenges, and outlines future research directions for performance modeling.

Findings

Data-driven methods offer promising performance evaluation capabilities.

Significant challenges exist in modeling diverse inputs with limited data.

Opportunities lie in improved data collection and model development.

Abstract

Emerging technologies and applications make the network unprecedentedly complex and heterogeneous, leading physical network practices to be costly and risky. The digital twin network (DTN) can ease these burdens by virtually enabling users to understand how performance changes accordingly with modifications. For this "What-if" performance evaluation, conventional simulation and analytical approaches are inefficient, inaccurate, and inflexible, and we argue that data-driven methods are most promising. In this article, we identify three requirements (fidelity, efficiency, and flexibility) for performance evaluation. Then we present a comparison of selected data-driven methods and investigate their potential trends in data, models, and applications. Although extensive applications have been enabled, there are still significant conflicts between models' capacities to handle diversified inputs and limited data collected from the production network. We further illustrate the opportunities for data collection, model construction, and application prospects. This survey aims to provide a reference for performance evaluation while also facilitating future DTN research.