Collaborative Offloading Method for Digital Twin Empowered Cloud Edge Computing on Internet of Vehicles

Abstract

Digital twinning and edge computing are attractive solutions to support computing-intensive and service-sensitive Internet of Vehicles applications. Most of the existing Internet of Vehicles service offloading solutions only consider edge-cloud collaboration, but the collaboration between small cell eNodeB (SCeNB) should not be ignored. Service delays far lower than offloading tasks to the cloud can be obtained through reasonable collaborative computing between nodes. The proposed framework realizes and maintains the simulation of collaboration between SCeNB nodes by constructing a digital twin that maintains SCeNB nodes in the central controller, thereby realizing user task offloading positions, sub-channel allocation, and computing resource allocation. Then an algorithm named AUC-AC is proposed, based on the dominant actor-critic network and the auction mechanism. In order to obtain a better command of global information, the convolutional block attention mechanism (CBAM) is used in the digital twin of each SCeNB node to observe its environment and learn strategies. Numerical results show that our experimental scheme is better than several baseline algorithms in terms of service delay.