Low-Overhead Scheduling for Synchronization in Large-Scale Heterogeneous Digital Twin Systems

Abstract

Digital Twin (DT) creates a twin world of physical systems, enabling intelligent decision-making through predictive simulation and data analysis. Its effectiveness relies on precise synchronization between physical and twin worlds through up-to-date data transmission, which is challenging in large-scale deployments. This work explores a large-scale DT system where heterogeneous physical entities share a common network resource to update state. We quantify synchronization error using Version Age of Information (VAoI), tracking the number of unsynchronized updates. Baseline policies suffer from either poor synchronization or high communication overhead. As such, we propose a low-overhead Threshold-based VAoI-aware Power-of-d (TV-PoD) policy, wherein the scheduler randomly samples d entities, among which selects those with VAoI exceeding the threshold, and schedules the one with the maximum VAoI for state update. Analyzing TV-PoD is challenging due to the coupled VAoI processes across entities. In the large-system limit, such coupling becomes negligible, allowing us to develop a VAoI-oriented mean-field framework that characterizes system-level VAoI dynamics in heterogeneous settings, and quantifies TV-PoD’s performance. Simulations validate our theoretical results and demonstrate that TV-PoD enables substantial overhead reduction while preserving synchronization quality. DT prototyping confirms the practical feasibility of our policy. To facilitate research in DT networking, we open-source the prototype [1].