Implementasi Self-Tuning Pada Sistem Database Untuk Meningkatkan Kinerja Query Dengan Menggunakan Metode Gradient Descent - Zaky Farsi; Agung Setia Budi; Rakhmadhany Primananda (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Self-tuning the database system is a database performance improvement process that focuses on the timing of queries performed by the database. Self-tuning determines the parameter value to be used by the database based on the query time generated by the database. Self-tuning is known as a system that helps improve performance with human assistance or without human intervention. Self-tuning itself can only be used on systems whose performance can be improved, one of which is the database. Therefore, in this study we propose a new self-tuning system using the Gradient Descent method. In the first stage, we determine the parameters that can affect the performance of the database by paying attention to the response time value of the query when changes to the parameters are made. In this self-tuning database research with the Gradient Descent method, the parameters used are buffer_pool_size, read_io_threads, write_io_threads and io_capacity. Each of these parameters will be processed in the self-tuning system and will output the query time and will be processed in the Gradient Descent method to determine the new parameter values. Self-tuning will be carried out on the MariaDB DBMS by using test data with a different number of iterations in each self-tuning process. The results of the self-tuning test with the Gradient Descent method get an increase in the value of each parameter with a number of iterations of 20.40, 60 and 100. From the test results, the increase in database performance itself shows an increase of 67.92% with an average query time of 20 seconds. at 80-100 iterations.