An Online Optimization of Prediction-Enhanced Digital Twin Migration over Edge Computing with Adaptive Information Updating

This paper investigates mobility-aware online optimization for digital twin (DT)-assisted task execution in edge computing environments. In such systems, DTs, hosted on edge servers (ESs), require proactive migration to maintain proximity to their mobile physical twin (PT) counterparts. To minimize task response latency under a stringent energy consumption constraint, we jointly optimize three key components: the status data uploading frequency from the PT, the DT migration decisions, and the allocation of computational and communication resources. To address the asynchronous nature of these decisions, we propose a novel two-timescale mobility-aware online optimization (TMO) framework. The TMO scheme leverages an extended two-timescale Lyapunov optimization framework to decompose the long-term problem into sequential subproblems. At the larger timescale, a multi-armed bandit (MAB) algorithm is employed to dynamically learn the optimal status data uploading frequency. Within each shorter timescale, we first employ a gated recurrent unit (GRU)-based predictor to forecast the PT’s trajectory. Based on this prediction, an alternate minimization (AM) algorithm is then utilized to solve for the DT migration and resource allocation variables. Theoretical analysis confirms that the proposed TMO scheme is asymptotically optimal. Furthermore, simulation results demonstrate its significant performance gains over existing benchmark methods.