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A Survey of Machine Learning Methods for Relational Database Tuning

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Title: A Survey of Machine Learning Methods for Relational Database Tuning
Author(s): Nygren, Saara
Contributor: University of Helsinki, Faculty of Science, Tietojenkäsittelytieteen osasto
Discipline: Tietojenkäsittelytiede
Language: English
Acceptance year: 2020
A relational database management system’s configuration is essential while optimizing database performance. Finding the optimal knob configuration for the database requires tuning of multiple interdependent knobs. Over the past few years, relational database vendors have added machine learning models to their products and Oracle announced the first autonomous (i.e self-driving) database in 2017. This thesis clarifies the autonomous database concept and surveys the latest research on machine learning methods for relational database knob tuning. The study aimed to find solutions that can tune multiple database knobs and be applied to any relational database. The survey found three machine learning implementations that tune multiple knobs at a time. These are called OtterTune, CDBTune, and QTune. Ottertune uses traditional machine learning techniques, while CDBTune and QTune rely on deep reinforcement learning. These implementations are presented in this thesis, along with a discussion of the features they offer. The thesis also presents an autonomic system’s basic concepts like self-CHOP and MAPE-K feedback loop and a knowledge model to define the knowledge needed to implement them. These can be used in the autonomous database contexts along with Intelligent Machine Design and Five Levels of AI-Native Database to present requirements for the autonomous database.
Keyword(s): autonomous database performance tuning machine learning survey

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