Abstract

Vehicle-to-everything (V2X) applications are an integral part of the realization of autonomous and connected vehicles. Due to the large amount of data generated by the vehicles, the stringent delay requirements, and the sheer size of V2X applications, these applications are decomposed into several components that need to be placed in close proximity to requesting vehicles. Mobile-edge Computing (MEC) has been envisioned as a promising solution for vehicular task offloading. Despite the desirable properties of MEC, which can potentially satisfy the delay requirements, edge servers have limited computational capability and have a high cost. They also have availability concerns that should be taken into account. In this regard, Application Service Providers (ASPs) strive for a solution that can both satisfy the delay requirements of V2X applications and cut their expenses, which is reflected by the utilization of edge resources. To that end, this work formulates a Binary Linear Program (BLP) that minimizes the ratio of the experienced to the tolerable delay threshold and the utilization of edge resources under resource and V2X applications' availability constraints. To investigate the trade-off between these two objectives, they are parametrized by a coefficient that reflects their relative priorities. Additionally, the delay and the resource objectives are studied in light of different V2X applications' availability constraints. The experimental results show the effectiveness of our approach. On average, the delays of all V2X applications are satisfied with an acceptable level of edge server utilization.

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