Automated Hyperparameter Tuning for Adaptive Cloud Workload Prediction

Efficient workload prediction is essential for enabling timely resource provisioning in cloud computing environments. However, achieving accurate predictions, ensuring adaptability to changing conditions, and minimizing computation overhead pose significant challenges for workload prediction models. Furthermore, the continuous streaming nature of workload metrics requires careful consideration when applying machine learning and data mining algorithms, as manual hyperparameter optimization can be time-consuming and suboptimal. We propose an automated parameter tuning and adaptation approach for workload prediction models and concept drift detection algorithms utilized in predicting future workload. Our method leverages a pre-built knowledge-base based on historical data statistical features, enabling automatic adjustment of model weights and concept drift detection parameters. Additionally, model adaptation is facilitated through a transfer learning approach. We evaluate the effectiveness of our automated approach by comparing it with static approaches using synthetic and real-world datasets. By automating the parameter tuning process and integrating concept drift detection, in our experiments the proposed method enhances the accuracy and efficiency of workload prediction models by 50%.