Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatio-temporal variations of the demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios assume that the vehicular traffic follows certain spatio-temporal patterns, which may change in different seasons, and create capacity plans accordingly. In other words, they consider long-term capacity planning, leaving the adaptation to temporary changes or unexpected variations out of scope. In this work, we propose an integer linear programming (ILP) based framework to optimize the routing strategy of vehicular fog nodes (VFNs) in order to maximize the profit received by the service provider, taking into account the quality of service (QoS) received by the users and service level agreement (SLA) of various applications. To adapt to the temporal variations in demand, we predict the traffic flow and resource consumption from the users with feedback from service evaluation. To reduce the computational time and enable parallel processing, we create the capacity plan in two steps, namely global planning and regional planning. Through simulations, we show that the proposed solution achieves an 85% higher profit and a 20% higher service rate compared to the strategy where the VFNs randomly travel and serve the surrounding users without demand prediction. It achieves similar network latency compared to the strategy using only stationary fog nodes, but with a higher cost-efficiency. We also evaluate the impacts of number of VFNs, cost parameters, and regional size on the capacity plan. We find that a high number of VFNs, a small regional size, a high penalty cost, and low traveling and rental costs will lead to a high service rate; while a large regional size and low traveling, rental, and penalty costs will result in a high profit.

Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of compute-intensive vehicular applications such as cooperative driving. Concerning the spatio-temporal variation in the vehicular traffic flows and the demand for edge computing capacity generated by connected vehicles, vehicular fog computing (VFC) has been proposed as a cost-efficient deployment model that complements stationary fog nodes with mobile ones carried by moving vehicles. Accessing the feasibility and the applicability of such hybrid topology, and further planning and managing the networking and computing resources at the edge, require deep understanding of the spatio-temporal variations in the demand and the supply of edge computing capacity as well as the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. To meet such requirements, we propose in this paper an open platform for simulating the VFC environment and for evaluating the performance and cost efficiency of capacity planning and resource allocation strategies under diverse physical conditions and business strategies. Compared with the existing edge/fog computing simulators, our platform supports the mobility of fog nodes and provides a realistic modeling of vehicular networking with the 5G and beyond network in the urban environment. We demonstrate the functionality of the platform using city-scale VFC capacity planning as example. The simulation results provide insights on the feasibility of different deployment strategies from both technical and financial perspectives.

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, Vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities. In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles. Previous works of VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much capacity to deploy, remains an open and challenging issue. The complexity of this problem comes from the mobility of vehicles, the spatio-temporal dynamics of vehicular traffic, and the computing resource demand generated by varying vehicular applications. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in computing demand. Through real-world experiments, we analyze the cost efficiency potential of VFC in long term and demonstrate that the performance loss of VFC is below $6\%$ compared to stationary deployment with equal network capacity. We also analyze the impacts of traffic patterns on the potential cost saving. The results show when the traffic density is higher, more operational costs will be saved in the long run due to more dense deployment of mobile fog nodes.