The performance of modern database management systems highly relies on hundreds of adjustable knobs. Traditionally, these knobs are manually adjusted by database administrators, a process that is both inefficient and ineffective for tuning large-scale databases in cloud environments. Recent research has explored the use of machine learning techniques to enable the automatic tuning of database configurations. Although most existing learning-based methods achieve satisfactory results on static workloads, they often experience performance degradation and low sampling efficiency in real-world environments. According to our study, this is primarily due to a lack of safety guarantees during the configuration sampling process. To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. Our core idea is to filter out a large number of configurations with potential risks during the configuration sampling process. We employ a two-stage filtering approach: The first stage utilizes a semi-supervised outlier ensemble with feature learning to achieve high-quality feature representation. The second stage employs a ranking-based classifier to refine the filtering process. In addition, to alleviate the cold-start problem, we leverage the historical tuning experience to provide high-quality initial samples during the initialization phase. We conducted comprehensive evaluations on static and dynamic workloads. In comparison to offline baseline methods, SafeTune reduces 95.6%-98.6% unsafe configuration suggestions. In contrast with state-of-the-art methods, SafeTune has improved cumulative performance by 10.5%-46.6% and tuning speed by 15.1%-35.4%.

A reliable autonomous driving system should take safe and efficient actions in constantly changing traffic. This requires the trajectory prediction model to continuously learn from incoming data and adapt to new scenarios. In the context of rapidly growing data volume, existing trajectory prediction models must retrain on all datasets to avoid forgetting previously learned knowledge when facing additional data from new environments. In contrast, the paradigm of continual learning solely necessitates training on new data, saving a significant amount of training overhead. Therefore, it is crucial to equip the trajectory prediction model with the ability of continual learning. In this paper, inspired by rehearsal and pseudo-rehearsal methods in continual learning, we propose a continual trajectory prediction framework with uncertainty-aware generative memory replay, CTP-UGR. Our framework effectively avoids excessive memory space requirements while generating trajectory data that is authentic, representative and discriminative for continual learning. Extensive experiments on two real-world datasets demonstrate our proposed CTP-UGR significantly outperforms other baselines in terms of both accuracy and catastrophic forgetting. Besides, our framework can be combined with other state-of-the-art trajectory prediction models to achieve better performance.