Intelligent Resource Allocation in Dynamic Fog Computing Environments

Abstract

Fog computing emerged as a new paradigm that pushes cloud applications to the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog nodes that are characterized by their complex distribution, high mobility and sporadic resources availability. This dynamic fog nodes behavior triggers new challenges in the resource management process, such as resources coordination for continuous quality-of-service satisfaction. In this paper, we propose a smart online resource allocation approach adapted for dynamic fog computing environments, aiming at maximizing the number of satisfied user requests within a predefined delay threshold. We model the fog computing environment as a Markov discrete process, where dynamic fog node behavior / mobility and resources availability are considered. Then, we present our smart deep-reinforcement-learning resource allocation algorithm. Considering real-world mobility data sets, the near-optimal performance of the proposed solution is illustrated through simulations, and its superiority over heuristic state-of-the-art approaches is exposed.