Efficient retraining of machine learning algorithms in cloud management systems

Cloud management systems performing capacity autoscaling, application orchestration, server consolidation, and service differentiation increasingly rely on machine learning (ML) models for predictive decision-making. However, changes in user behavior, software updates, and hardware upgrades cause monitoring data to deviate from the training distribution, leading to model performance degradation. This phenomenon—known as concept drift poses a significant challenge to maintaining prediction accuracy in dynamic cloud environments. In this work, we propose a hybrid concept drift detection approach that combines statistical data monitoring with model performance metrics to inform efficient model retraining. The proposed method minimizes false positives, reduces adaptation delays, and avoids unnecessary retraining during stable periods. We conduct extensive experiments on both synthetic and real-world cloud workload datasets collected from multiple data centers, evaluating the hybrid approach against five established drift detection algorithms. To ensure statistical rigor, all experiments are repeated ten times, and the results are reported with 95% confidence intervals and significance tests. The results show that our proposed approach and implemented methods improve drift detection efficiency by eliminating unnecessary retraining and lead to more than 60% improvements over the baseline prediction accuracy. We Edited the abstract and removed the inconsistencies