عنوان	A Q-Learning Approach for Dynamic Resource Management in Three-Tier Vehicular Fog Computing
نوع پژوهش	مقاله چاپ‌شده در مجله
کلیدواژه‌ها	Task Scheduling, Vehicular Fog Computing, Q-learning, Resource Allocation
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 ndings of this study indicate that Q-learning can e ectively 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 e ectiveness 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.
پژوهشگران	بهار مجتبایی رنانی (نفر اول)، محمود احمدی (نفر دوم)، سجاد احمدیان (نفر سوم)