Vehicular fog computing (VFC) pushes the cloud computing capability to the distributed fog nodes at the edge of the Internet, enabling compute-intensive and latency-sensitive computing services for vehicles through task offloading. However, a heterogeneous mobility environment introduces uncertainties in terms of resource supply and demand, which are inevitable bottlenecks for the optimal offloading decision. Also, these uncertainties bring extra challenges to task offloading under the oblivious adversary attack and data privacy risks. In this article, we develop a new adversarial online learning algorithm with bandit feedback based on the adversarial multi-armed bandit theory, to enable scalable and low-complexity offloading decision making. Specifically, we focus on optimizing fog node selection with the aim of minimizing the offloading service costs in terms of delay and energy. The key is to implicitly tune the exploration bonus in the selection process and the assessment rules of the designed algorithm, taking into account volatile resource supply and demand. We theoretically prove that the input-size dependent selection rule allows to choose a suitable fog node without exploring the sub-optimal actions, and also an appropriate score patching rule allows to quickly adapt to evolving circumstances, which reduce variance and bias simultaneously, thereby achieving a better exploitation-exploration balance. Simulation results verify the effectiveness and robustness of the proposed algorithm.Index Terms-Vehicular fog computing, task offloading, online learning, adversarial multi-armed bandit.Byungjin Cho received the doctoral degree in communications engineering from Aalto University, in 2016. He is currently a postdoctoral researcher with the Department of Communications and Networking, Aalto University. His research interests include resource managements in networked systems using algorithmic decision theory.Yu Xiao Yu Xiao received the doctoral degree in computer science from Aalto University, in 2012. She is currently an assistant professor with the Department of Communications and Networking, Aalto University. Her current research interests include edge computing, wearable sensing and extended reality. She is a member of the IEEE.

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 (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. 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 spatiotemporal variation in both demand and supply. Using realworld traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term.

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.

<p>Edge/fog computing is a key enabling technology in 5G and beyond for fulfilling the tight latency requirements of emerging vehicle applications, such as cooperative and autonomous driving. Vehicular Fog Computing (VFC) is a cost-efficient deployment option that complements stationary fog nodes with mobile ones carried by moving vehicles. To plan the deployment and manage the VFC resources in the real world, it is essential to take into account the spatio-temporal variations in both demand and supply of fog computing capacity and the trade-offs between achievable Quality-of-Services and potential deployment and operating costs. Concerning the complexity and the economic load of real-world measurements, simulation becomes a better option at the early research phase to validate capacity and resource management solutions in various urban environments. The existing simulation platforms cannot provide a realistic techno-economic investigation to analyze the implications of VFC deployment options, due to the simplified network models in use, the lack of support for fog node mobility, and limited testing scenarios. In this paper, we propose an open-source simulator VFogSim that allows real-world data as input for simulating the supply and demand of VFC in urban areas. It follows a modular design to evaluate the performance and cost-efficiency of different deployment scenarios under various vehicular traffic models, and the effectiveness of the diverse network and computation schedulers and prioritization mechanisms under user-defined scenarios. Compared with the existing edge/fog computing simulators, such as IFogSim, IoTSim, and EdgeCloudSim, to the best of our knowledge, our platform is the first one that supports the mobility of fog nodes and provides realistic modeling of V2X in 5G and beyond networks in the urban environment. Furthermore, we validate the accuracy of the platform using a real-world 5G measurement and demonstrate the functionality of the platform taking VFC capacity planning as an example.</p>

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.