  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.
