Adaptive Q-leaming-supported Resource Allocation Model in Vehicular Fogs

Abstract

Vehicular Cloud Computing (VCC) exhibits many drawbacks with the demands of vehicular applications and intermittent network conditions. Vehicular Fog computing is a novel method for supporting and promoting the effective sharing of services and resources in urban areas. Diverse works on vehicular resource management have sought to handle the very dynamic vehicular environment using various methods, such as policy-based greedy and stochastic techniques. Nevertheless, high vehicular mobility poses many issues that compromise service consistency, efficiency, and quality. Adaptive vehicular Fogs incorporating Reinforcement Learning can deal with mobility and correctly distribute services and resources across all Fogs. Thus, we introduce an adaptive resource management model using cloudlet dwell time for resource estimation, mathematical formula for Fog selection, and reinforcement learning for iterative review and feedback mechanism for generating optimal resource allocation policy.