An Orchestration System for Digital Twin Trustworthiness in Industrial Environments

Digital twins (DTs) are increasingly used in industrial environments to mediate interactions between applications and physical assets. However, existing orchestration approaches treat DTs as conventional software components, overlooking their cyber-physical nature and the resulting trustworthiness challenges. In such systems, violations of timing, resource, or data constraints can compromise the fidelity of the DT–physical twin (PT) relationship. In this article, we propose a trustworthiness-driven orchestration system, where trustworthiness is modeled as a multidimensional runtime invariant. The system implements a continuous control loop that monitors relevant metrics and enforces corrective actions, such as resource reallocation or migration across the cloud-to-edge continuum. We validate the approach through a Kubernetes-based prototype and large-scale simulations. Results show that the system effectively detects violations and restores DT–PT entanglement under dynamic conditions, demonstrating the feasibility of invariant-driven orchestration for industrial DTs.