Vehicular-fog system consists of vehicles with computing resources that are mostly under-utilized. Therefore, an edge system may offload some workloads for remote execution at nearby vehicular- fogs. Whether this is cost-effective depends on not only the costs and computation capacities of vehicles but also the amount of workloads and associated latency constraint. In this paper, we consider a two-tier federated Edge and Vehicular- Fog (EVF) architecture and aim to minimize overall cost while meeting latency constraint by setting up an appropriate offloading configuration. We model this to a singleobjective mixed integer programming problem. To solve this mixed integer problem in real time we propose an iterative greedy algorithm using the queuing model. The results show, our proposed architecture reduces the cost of vehicular-fogs by 40â 45% and the total cost by 35â 40% compared to the existing architecture and help the edge to provide services beyond its capacity with specified latency constraint.