Chanho Yeom  Jieun Lee  Sanghyun Park

10.5626/JOK.2022.49.11.1026

database Deep Learning Machine Learning Attention Mechanism soft orthogonality regulariztion

Various parameters in a database can be modified, which are called knobs. Since the performance of the database varies according to the settings of the knobs, it is important to tune the knobs of the database. And when tuning, a model that can reliably and quickly predict database performance according to the knob setting is needed. However, even when the knob setting is the same, the results may be different if the workload performing the benchmark is different. Therefore, in this paper, we propose an OANet using the attention mechanism so that the relationship between the knob and the workload can also be considered. Through experiments, the performance prediction results of the database were compared to various machine learning techniques, and the superiority of the model was confirmed by showing the highest score.