An effective method for prospective scheduling of tasks in cloud-fog computing with an energy consumption management approach based on Q-learning

The increasing energy consumption in cloud computing data centers has become a significant concern due to the expanding scale of computational demands. Efficient task scheduling is crucial to optimizing resource utilization while reducing operational costs and energy consumption. This study proposes a Multi-Agent Reinforcement Learning (MARL)-based scheduling framework that enhances system efficiency by dynamically allocating tasks based on environmental variations and workload fluctuations. Unlike conventional methods, MARL allows multiple intelligent agents to collaboratively optimize scheduling decisions, leading to superior adaptability and performance. The proposed approach consists of two steps: first, a centralized task dispatcher assigns incoming tasks to cloud servers using a queuing model. Second, an MARL-based scheduler on each server prioritizes and allocates tasks to virtual machines while continuously updating scheduling policies to maximize efficiency. The framework is evaluated using a CloudSim-based simulation environment to ensure a realistic and controlled assessment. Experimental results demonstrate that the proposed method reduces energy consumption by an average of 51.34 %, improves CPU utilization efficiency, and decreases response time by 44.35 % compared to traditional scheduling techniques, including First In-First Out (FIFO), Greedy, and Queue-based Scheduling (Q-sch). By leveraging MARL, the scheduler effectively minimizes waiting times and optimizes task completion rates, ensuring a balance between energy efficiency and system performance. This work highlights the advantages of reinforcement learning in cloud-fog computing and underscores its potential for intelligent resource management.