Forecasting Cloud Workload through Reinforcement Learning-Based Ensemble Model

The escalating demand for cloud computing has intensified the need for accurate workload forecasting to optimize resource allocation and maintain service quality. This paper presents a novel approach employing a reinforcement learning-based ensemble model to enhance the accuracy of workload predictions in cloud environments. The study addresses the challenges posed by complex and dynamic workload patterns, leveraging deep learning techniques to improve prediction outcomes. The proposed model dynamically selects the optimal subset of predictors, demonstrating superior performance on the Google Cluster dataset. The research aims to contribute to more efficient and intelligent resource management in cloud computing.