/2023_KCC

CCDT(Configuration Clustering Database Tuning)

Primary LanguageJupyter Notebook

2023_KCC Model

This is the code of the Korea Computer Congress 2023 (KCC 2023) paper

Please check the 'testing_tpcc.ipynb & testing_twitter.ipynb'

'A Study about Search Space of Knob Range Reduction for Database Tuning'.

This study proposes a method to reduce the search space as an optimization method that can improve the performance of database parameters (knobs).

- MySQL ver. 5.7

- Num of Parameters = 139

- Num of Config = 200

- Workload : TPCC , Twitter

Firstly, we randomly generate 200 samples via Latin Hypercube Sampling (LHS).

Secondly, we select 10 knobs that have a significant impact on database performance by a knob ranking algorithm.

Thirdly, 10 configurations within the generated samples are selected based on their measured database performance, where we calculated score (throughput/latency) to compare multiple configurations.

Then, we find the used value range of each selected knob from the selected configurations.

With these newly defined knob ranges, the optimization algorithm can search knob values within a narrower range than its default range.

Paper

Below is link of 'A Study about Search Space of Knob Range Reduction for Database Tuning' paper
Paper link