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Vehicular edge computing (VEC) has emerged as a promising paradigm for efficient processing of computation-intensive and delay-sensitive tasks by coordinating service placement and task offloading. Existing research mainly focused on edge-edge and edge-cloud collaborations to enhance system performance and resource utilization. However, the potential of vehicle-vehicle collaboration remains under-explored. To bridge this gap, we propose a novel three-layer VEC architecture integrating vertical collaboration across different layers with horizontal collaboration within the same layer (vehicle-edge-cloud collaboration). Recognizing the dynamic nature of the Internet of Vehicles, we introduce link duration constraints to quantify the impact of vehicles’ mobility on wireless communications. We formulate a mixed-integer nonlinear programming problem for joint service placement, task offloading, and computing resource allocation to minimize the total task completion delay of vehicles. To solve it, a two-stage heuristic algorithm is designed, including a semidefinite relaxation-based approximation method for the task offloading and computing resource allocation problem without storage capacity constraints and a heuristic approach for the service placement problem. Extensive simulations conducted on synthetic and realistic road topologies demonstrate that the proposed algorithm can obtain feasible solutions and achieve significantly lower delay than five benchmark methods.
Huang et al. (Wed,) studied this question.