Cooperative Task Offloading for Internet of Vehicles in Cloud-edge Computing

Abstract

In order to support computing-intensive and service-sensitive Internet of Vehicles (IoV) applications, Digital Twin and edge computing are attractive solutions. Most of the existing IoV service offloading solutions only consider edge-cloud collaboration, but the cooperation between small cell eNodeB(SCeNB) nodes should not be ignored. Through reasonable cooperative computing between nodes, service delays far lower than offloading tasks to the cloud can be obtained. We generate and maintain the simulation of cooperation between SCeNB nodes by constructing a Digital twin to simulate SCeNB nodes in the small cell manager (SCM), thereby realizing user task offloading positions, sub-channel allocation and computing resource allocation. After that, we proposed an algorithm AUC-AC based on the dominant actor critic network and the auction-mechanism. In the DT of each SCeNB node, the global strategy is learned via deep reinforcement learning model. Numerical results show that our proposed experimental scheme is better than several baseline algorithms in terms of service delay.