Inference Service Fidelity Maximization in DT-Assisted Edge Computing

作者：Jing Li, Jianping Wang, Weifa Liang, Xiaohua Jia, Albert Y. Zomaya · 发表于：IEEE Transactions on Mobile Computing · 年份：2025 · DOI：10.1109/tmc.2025.3600390 · 被引用次数：6 · 研究领域：Cloud Computing and Resource Management、Distributed and Parallel Computing Systems、IoT and Edge/Fog Computing

Digital twin (DT) technology enables smooth integrations of cyber and physical worlds in alignment with the Industry 4.0 initiative. DTs are virtual presentations of physical objects. Through synchronizations with physical objects in real-time, DTs can reflect the states of their objects with high fidelity. Orthogonal to the DT technology, mobile edge computing (MEC) is a promising computing paradigm that shifts computing power to the edge network, which is appropriate for delay-sensitive intelligent services. In this paper, we study fidelity-aware inference services in a DT-assisted MEC environment, where machine learning-based inference models must be continuously retrained using updated DT data in order to provide high-fidelity services for consumers. To this end, we first formulate two novel optimization problems: the initial DT and model placement problem with the aim of minimizing the total cost of various resources consumed, and the cumulative fidelity maximization problem to maximize the long-term cumulative fidelity of service models while minimizing the cost of resource consumption on service model fidelity enhancements over a given time horizon, through jointly scheduling mobile devices to upload their update data to synchronize with their DTs and determining whether DTs and/or models to be migrated at each time slot. We then develop an efficient algorithm for the initial DT and model placement problem, through a reduction to a series of minimum-cost maximum matchi...