Analysis of Machine Learning Techniques for Knob Tuning

The global database automation market is booming due to the increasing data generated by various sectors. Consequently, organizations are aggressively pursuing solutions to improve operations and increase the efficiency of database systems. This study explores contemporary scholarly work concerning database automation and Knob tuning, focusing specifically on the critical significance of knob adjustment in attaining good performance. The results emphasize the importance of machine learning in adjusting knob settings and its efficacy in accommodating different workloads and optimizing knob settings for specific objectives, which are increased throughput and decreased latency while changing and adapting to other workloads. This work aims to offer better insights into the implementation of different knob tuners using machine-learning models by conducting a thorough review of various techniques.