A First Look at the Impact of Measurement on Orchestrating Digital Twin Network

Digital twin network are emerging as key drivers for future automated and high-performance networks. Digital twin networks create virtual representations of physical networks, enabling real-time monitoring, simulation, and optimization. The accuracy and timeliness of data are crucial for building a precise digital twin network. However, constructing an accurate digital twin network faces significant challenges due to the difficulties in data collection. In network measurement and data collection, data uncertainty is often unavoidable due to observer effects, where the act of measurement itself imposes an impact on the system. This phenomenon can introduce biases or perturbations that compromise the accuracy of digital twin network models, leading to less precise representations of the network's actual state and behavior. This paper systematically reviews existing measurement schemes and proposes a new classification. We evaluate these measurement methods within a simple network environment, analyzing their impact on system performance and delving into the underlying causes of performance degradation. These insights contribute to the development of a more accurate and efficient digital twin network.