A dynamic resource management algorithm for maximizing service capability in fog-empowered vehicular ad-hoc networks

The new computing paradigm, fog computing, enables advanced services, such as navigation, self-driving cars, and augmented reality, by integrating with vehicular networks to provide smart transportation solutions. This emerges fog-empowered vehicular ad-hoc networks (FEVANETs), which enable smart vehicles to communicate with fog nodes (FNs) using association policy, such as signal strength and/or favorite contents. However, a deluge arrival of vehicles to the network can cause a load imbalance among FNs. This impacts the severe reduction in the network service capability and resource utilization efficiency. To address this problem, we propose an algorithm, dynamic resource management (DRM), for assigning the resources of FNs to smart vehicles by migrating services among FNs. The problem is formulated as integer linear programming (ILP) and determines its NP-hardness by reducing it from Seminar Assignment Problem. A polynomial-time algorithm is presented by formulating the problem as a graph in which vertices represent the FNs and edges represent the vehicles present in the overlapped region of the pairs of FNs. The proposed algorithm considers the set of vehicles that are in overlapped coverage regions of FNs and communicates with those corresponding FNs. Then it migrates the resource blocks (RBs) of the set of vehicles between pairs of FNs to minimize the allocated RBs. The DRM is simulated extensively, and the simulation outcomes show that the DRM enhances service capability, serviceability, availability, throughput, and resource utilization efficiency compared to the four existing algorithms.