Adaptive Deep Reinforcement Learning for Efficient Task Scheduling in Green Energy Powered Cloud Data Center

Abstract

Cloud computing has emerged as a dominant force in tackling dynamic, unpredictable, and adaptable computing demands. However, the vast scale of cloud computing, the complexities of diverse scenarios, and the unpredictable nature of user requests pose significant challenges to achieving efficient and effective cloud computing scheduling. To address these issues and enhance computational efficiency while reducing energy consumption in cloud computing systems, we introduce a workload-adaptive deep reinforcement learning algorithm that dynamically adjusts the discount factor in accordance with real-time workload changes. To demonstrate the effectiveness of our proposed method, we utilize a real dataset from the Google Cloud computing center, which encompasses workloads from 12,000 machines running over 670,000 applications and 40 million jobs. Simulation results reveal that our method achieves a 9.93% reduction in task rejection rate and a 5.01% decrease in energy consumption cost compared to state-of-the-art solutions.