Risk-Aware Learning for Digital Twin Placement in Non-Stationary Hierarchical Edge Computing

Abstract

A Digital Twin (DT) is a virtual replica of a physical system (PS), such as an Internet-of-Things (IoT) device, enabling the analysis and prediction of the PS's dynamics. To maintain accuracy, a DT frequently synchronizes with its PS, by processing status updates sent from the PS. Therefore, the incurred synchronization latency is impacted by both, communication and computation resources. Due to being computationally demanding, DTs are commonly hosted on edge servers (ESs) in Mobile Edge Computing (MEC). To increase scalability and flexibility of MEC systems, hierarchical architectures distribute computation resources across multiple tiers of ESs between base stations and cloud. Hierarchical MEC gives rise to the DT placement problem, i.e., the selection of an ES for hosting the DT. The selection must consider the synchronization latency, which should remain below critical thresholds to ensure DT accuracy and thus, successful synchronization. DT placement is challenging, because the synchronization latency is impacted by the dynamic and statistically non-stationary MEC system characteristics, such as communication channels and server loads. Furthermore, additional overhead is caused by migrating DTs between ESs. To address these challenges, we propose a novel risk-aware Multi-Armed Bandit algorithm that adapts to piece-wise stationary environments. Our approach minimizes synchronization cost, defined as a weighted sum of synchronization latency and energy consumption, while reducing the risk of synchronization failures and limiting DT migration overhead. Simulation results demonstrate that our proposed algorithm achieves a similar synchronization cost, compared to state-of-the-art baselines, while achieving a significantly smaller synchronization failure rate and migration overhead.