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Mojtabaei Ranani, Bahar, Ahmadi, Mahmood, Ahmadian, Sajad. (1404). A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing. , 12(2), 107-128. doi: 10.22108/jcs.2026.147193.1184
Bahar Mojtabaei Ranani; Mahmood Ahmadi; Sajad Ahmadian. "A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing". , 12, 2, 1404, 107-128. doi: 10.22108/jcs.2026.147193.1184
Mojtabaei Ranani, Bahar, Ahmadi, Mahmood, Ahmadian, Sajad. (1404). 'A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing', , 12(2), pp. 107-128. doi: 10.22108/jcs.2026.147193.1184
Mojtabaei Ranani, Bahar, Ahmadi, Mahmood, Ahmadian, Sajad. A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing. , 1404; 12(2): 107-128. doi: 10.22108/jcs.2026.147193.1184

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

Journal of Computing and Security
مقاله 7، دوره 12، شماره 2، آذر 2025، صفحه 107-128    اصل مقاله (4.01 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2026.147193.1184
نویسندگان
Bahar Mojtabaei Ranani1؛ Mahmood Ahmadi* 1؛ Sajad Ahmadian2
1Department of Computer Engineering and Information Technology, Razi University, Iran.
2Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran.
چکیده
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 compared to other methods evaluated in this study.
کلیدواژه‌ها
Task scheduling؛ Vehicular fog computing؛ Q-learning؛ resource allocation
سایر فایل های مرتبط با مقاله
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