Nereus: Network Resource Prediction-Based Digital Twin Dynamic Placement in Traffic Systems

Digital twin technology has emerged as a crucial paradigm for enhancing the responsiveness and management efficiency of transportation systems. A key technology to realize this vision is the real-time and accuracy twin placement. However, the limited resources and coverage of edge servers make it challenging to achieve effective twin placement in dynamic environments, where maximizing resource utilization and minimizing communication latency are crucial. To this end, we propose Nereus, which consists of four key modules: (i) a heterogeneous demand prediction module utilizes a heterogeneous graph neural network to capture complex relationships between twins and servers, facilitating accurate prediction of resource demands and loads; (ii) a feature-driven clustering module incorporates multi-dimensional twin features using principal component analysis and dynamic k-means clustering to adaptively generate twin groups; (iii) an adaptive association module exploits an attention mechanism to dynamically adjust twin group assignments; (iv) a multi-level placement optimization module employs a hierarchical actor-critic algorithm to refine twin placement strategies, enhancing resource utilization and minimizing communication latency. Experimental results demonstrate that, compared to state-of-the-art methods, Nereus achieves a 70.24% reduction in communication latency and improves computation resource matching by 52.43%.