Adaptive Task Scheduling in Digital Twin Empowered Cloud-Native Vehicular Networks
Xiaobin Tan, Mingyang Wang, Tao Wang, Quan Zheng, Jun Wu, Jian Yang
Abstract
Intelligent driving has advanced significantly in recent decades, paving the path for the transportation of the future. Digital twin (DT) technology, which can bridge the physical and virtual space gaps in real time, plays an important role in the collaboration of vehicles and roads for intelligent driving. In this paper, we design the Digital Twin empowered Cloud-native Vehicular Networks (DT-CVN) architecture as well as the workflow for task execution aiming at executing intelligent driving tasks efficiently and reliably. In DT-CVN, we propose a design and implementation scheme for digital twins, in which different modules of the same digital twin entity can be deployed in different network locations in a distributed manner by taking advantage of the distributed features of microservices based on cloud-native technology. Furthermore, we design the modules reuse and requests aggregation mechanisms of digital twins invocation for task scheduling in DT-CVN, which can improve its efficiency even further. Then we model the task scheduling in DT-CVN into a combinational optimization problem and propose a deep reinforcement learning (DRL) based adaptive task scheduling algorithm. Simulation results show that the proposed scheme can improve the efficiency of task scheduling while reducing energy consumption.