Utility-Based Task Offloading and Resource Allocation for Digital Twin-Assisted Edge Networks

With the emergence of the Internet of Things (IoT), mobile edge computing (MEC) effectively reduces task delay of multiple applications. The limitations of computing and storage capabilities, as well as the complex dynamic network environments, make efficient edge service computing stressful. To address this challenge, digital twin (DT) technology is a promising solution that bridges the virtual and physical worlds by creating digital representations of physical objects. DT technology can model the behaviors of physical entities through virtual mirroring and assist physical networks in making optimal network strategies. In this paper, we combine DT technology with MEC networks to develop a utility-based task offloading and resource allocation scheme, aiming to maximize the quality of experience (QoE) of user equipments (UEs) and the utility of base stations (BSs). Specifically, we construct a hierarchical Stackelberg game model, study the interaction between BSs and UEs, and prove that the UE layer is an exact potential game with Nash equilibrium (NE). To achieve Stackelberg equilibrium (SE), we propose a game-based hierarchical interaction algorithm (GHIA) and analyze it. The experimental results show that GHIA has good convergence, and the utility performance of participants is better comparing to other algorithms.