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

Bahar Mojtabaei Ranani, Mahmood Ahmadi, Sajad Ahmadian

TL;DR

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.

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

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

TL;DR

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.

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
Paper Structure (18 sections, 9 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 18 sections, 9 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Based on offloading computation to vehicle fog computing nodes
  4. Based on machine learning in vehicular fog computing
  5. Based on predicting the resource requirements in vehicular fog computing
  6. Proposed Method
  7. Problem modeling
  8. State space
  9. Action space
  10. Reward function
  11. QL reinforcement learning agent training
  12. Evaluation Results
  13. Dataset
  14. Evaluation metrics
  15. Simulation environment
  16. ...and 3 more sections

Figures (5)

  • Figure 1: A general architecture VFC and its implementation (a) A general VFC architecture. (b)An VFC implementation.
  • Figure 2: Performance comparison for different scenarios.
  • Figure 3: Performance comparison for different scenarios.
  • Figure 4: Performance comparison under different task entry probabilities.
  • Figure 5: Performance comparison under different task entry probabilities.