The delay-sensitive applications, such as self-driving, smart transportation, navigation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The intelligent vehicles are connected to the roadside infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), for obtaining on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network throughput. Therefore, we propose an efficient resource orchestration (ERO) algorithm to maximize the throughput by reducing the allocated resource blocks (RBs) of FNs. The ERO algorithm partitions the FN coverage region into restricted and non-restricted coverage regions. Then, it coordinates the RBs allocation among FNs by reducing RBs for the vehicles in the non-restricted coverage regions. This reduction is carried out by migrating RBs for offloading upstream services so that the overall occupied capacity of FNs is minimized. ERO constructs the minimum priority queue using the occupied capacity of FNs to perform optimal RBs migration between pairs of FNs. The ERO algorithm is evaluated, and simulation results show that the proposed algorithm performs better in terms of throughput, serviceability, availability, and service capability than existing algorithms