A Cloud-Fog Computing Architecture for Real-Time Digital Twins

TL;DR

This paper evaluates a cloud-fog computing architecture for real-time Digital Twins, demonstrating that distributing components closer to the edge reduces latency and meets real-time constraints.

Contribution

It presents a realistic implementation showing how fog computing can effectively support real-time Digital Twins by reducing response times.

Findings

Fog architecture reduces Digital Twin response times

Distributed setup meets real-time requirements

Improves data processing efficiency

Abstract

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.