  Digital Twin systems are designed as two interconnected mirrored spaces, one
real and one virtual, each reflecting the other, sharing information, and
making predictions based on analysis and simulations. The correct behavior of a
real-time Digital Twin depends not only on the logical results of computation
but also on the timing constraints. To cope with the large amounts of data that
need to be stored and analyzed, modern large scale Digital Twin deployments
often rely on cloud-based architectures. A significant portion of the overall
response time of a Digital Twin is spent on moving data from the edge to the
cloud. Therefore, implementing Digital Twins using cloud-fog architectures
emerges as an alternative to bring computing power closer to the edge, reducing
latency and allowing faster response times. This paper studies how suitable the
use of a cloud-fog architecture is to handle the real-time requirements of
Digital Twins. Based on a realistic implementation and deployment of Digital
Twin software components, it is possible to conclude that the distribution of
Digital Twins in a fog computing setup can reduce response times, meeting its
real-time application requirements.
