LAUSR

In this paper, the computation resource configuration for vehicular edge computing is investigated in this study. Dissimilar to a large portion of the current literature, we center around the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long-term management profit of the network operator (i.e., the road-side unit of the vehicular network) under the randomness of vehicular traffics and task processing. A multi-type management framework is used to characterize the heterogeneities among different vehicles in terms of their edge computing quality-of-service (QoS) requirements. A novel fluid model is proposed that facilitates the formulation of the corresponding resource optimization problem by taking into account the system’s steady state characteristics with dynamic evolutions. In addition, rather than considering fixed QoS requirements in long-run, we explore the impact of the resulted service quality on the QoS requirements determined by vehicles. The QoS requirements of each vehicle is allowed to change adaptively according to the service quality fed back by the system. Based on this, we propose a simple but efficient approach, called threshold-based computation resource configuration scheme (TCRCS). The proposed solution’s performance is assessed by theoretical analysis and simulations, which show that it outperforms competitors.