  Managing the configurations of a database system poses significant challenges
due to the multitude of configuration knobs that impact various system
aspects.The lack of standardization, independence, and universality among these
knobs further complicates the task of determining the optimal settings.To
address this issue, an automated solution leveraging supervised and
unsupervised machine learning techniques was developed.This solution aims to
identify influential knobs, analyze previously unseen workloads, and provide
recommendations for knob settings.The effectiveness of this approach is
demonstrated through the evaluation of a new tool called OtterTune [1] on three
different database management systems (DBMSs).The results indicate that
OtterTune's recommendations are comparable to or even surpass the
configurations generated by existing tools or human experts.In this study, we
build upon the automated technique introduced in the original OtterTune paper,
utilizing previously collected training data to optimize new DBMS
deployments.By employing supervised and unsupervised machine learning methods,
we focus on improving latency prediction.Our approach expands upon the methods
proposed in the original paper by incorporating GMM clustering to streamline
metrics selection and combining ensemble models (such as RandomForest) with
non-linear models (like neural networks) for more accurate prediction modeling.
