Reinforcement Learning for Resource Allocation in Vehicular Multi-Fog Computing

TL;DR

This paper explores how reinforcement learning can optimize resource allocation in multi-fog computing for vehicles, improving latency and workload management amidst dynamic vehicular environments.

Contribution

It formulates resource allocation in MFC as an MDP and applies RL algorithms like Q-learning, DQN, and Actor-Critic, demonstrating their effectiveness.

Findings

Reduced latency in resource allocation

Improved workload balance among fog nodes

Higher task success rate

Abstract

The exponential growth of Internet of Things (IoT) devices, smart vehicles, and latency-sensitive applications has created an urgent demand for efficient distributed computing paradigms. Multi-Fog Computing (MFC), as an extension of fog and edge computing, deploys multiple fog nodes near end users to reduce latency, enhance scalability, and ensure Quality of Service (QoS). However, resource allocation in MFC environments is highly challenging due to dynamic vehicular mobility, heterogeneous resources, and fluctuating workloads. Traditional optimization-based methods often fail to adapt to such dynamics. Reinforcement Learning (RL), as a model-free decision-making framework, enables adaptive task allocation by continuously interacting with the environment. This paper formulates the resource allocation problem in MFC as a Markov Decision Process (MDP) and investigates the application of RL algorithms such as Q-learning, Deep Q-Networks (DQN), and Actor-Critic. We present experimental results demonstrating improvements in latency, workload balance, and task success rate. The contributions and novelty of this study are also discussed, highlighting the role of RL in addressing emerging vehicular computing challenges.