On the resource consumption of XR-enabled Digital Twin Applications in the edge-cloud continuum

In this work, we present an experimental study on the resource consumption requirements and practical deployment considerations for a fully functional, ROS-compatible digital twin (DT) system of a robotic arm, featuring an extended reality (XR)- enabled graphical user interface (GUI). Our evaluation measures network, CPU, RAM and GPU usage across all principal system components that include both physical and virtualized robot controllers, deployed in Multi-access Edge Computing (MEC) infrastructures. The XR interface, developed for remote monitoring, optimization, and control in emerging Industry 4.0/5.0 scenarios, supports immersive supervision and interactive operation. The results provide practical insights for designing efficient,
scalable, and XR-enabled digital twin applications towards the next-generation of manufacturing and robotic systems.