This paper presents a novel approach for computing resource management of
edge servers in vehicular networks based on digital twins and artificial
intelligence (AI). Specifically, we construct two-tier digital twins tailored
for vehicular networks to capture networking-related features of vehicles and
edge servers. By exploiting such features, we propose a two-stage computing
resource allocation scheme. First, the central controller periodically
generates reference policies for real-time computing resource allocation
according to the network dynamics and service demands captured by digital twins
of edge servers. Second, computing resources of the edge servers are allocated
in real time to individual vehicles via low-complexity matching-based
allocation that complies with the reference policies. By leveraging digital
twins, the proposed scheme can adapt to dynamic service demands and vehicle
mobility in a scalable manner. Simulation results demonstrate that the proposed
digital twin-driven scheme enables the vehicular network to support more
computing tasks than benchmark schemes.Comment: 6 pages, 4 figures, accepted by 2022 IEEE GLOBECO