Digital Twin Empowered Task Offloading for Vehicular Edge Computing
Chaogang Tang, Huaming Wu, Chunsheng Zhu, Shuo Xiao · 2023
Vehicular edge computing (VEC) as a promising computing paradigm has accelerated the reformation of existing dominating computing infrastructures, enabling resource provisioning in close proximity to resource requestors. However, several challenges still exist, including efficient resource scheduling and management, dynamic wireless channel state, and limited bandwidth usage. To address these issues, we introduce the digital twin (DT) technology into VEC, enabling DTs of physical entities in VEC to achieve real-time offloading decision-making in the DT simulation cycle. In particular, we propose a DT-empowered VEC (DT-VEC) architecture, aiming to achieve efficient task offloading while considering extra latency incurred by task migration. We further put forward an efficient algorithm to minimize the response latency for all the tasks in the optimization period. The simulation results have proven that our approach outperforms the other two greedy approaches.