A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing

TL;DR

This paper introduces a Q-learning based method for dynamic resource management in vehicular fog computing, enabling adaptive and efficient resource allocation for intelligent vehicles.

Contribution

It presents a novel reinforcement learning approach to optimize resource allocation in a three-tier vehicular fog computing system, improving performance and adaptability.

Findings

Q-learning effectively predicts resource needs based on past data.

The method reduces resource consumption while maintaining system performance.

It improves task processing time compared to existing approaches.

Abstract

In this paper, a method for predicting the resources required for an intelligent vehicle client using a three-layer vehicular computing architecture is proposed. This method leverages Q-Learning to optimize resource allocation and enhance overall system performance. This approach employs reinforcement learning capabilities to provide a dynamic and adaptive strategy for resource management in a fog computing environment. The key findings of this study indicate that Q-learning can effectively predict the appropriate allocation of resources by learning from past experiences and making informed decisions. Through continuous training and updating of the Q-learning agent, the system can adapt to changing conditions and make resource allocation decisions based on real-time information. The experimental results demonstrate the effectiveness of the proposed method in optimizing resource allocation. The Q-learning agent predicts the optimal values for memory, bandwidth, and processor. These predictions not only minimize resource consumption but also meet the performance requirements of the fog system. Implementations show that this method improves the average task processing time in compared to other methods evaluated in this study