ATTuneDB: uma ferramenta de apoio à sintonia de SGBDs baseada na identificação do regime de operação através de modelo probabilístico

Abstract

The performance of a DBMS is a critical factor to be considered while using it. Several techniques are currently employed in an attempt to increase the performance of a DBMS. This research integrates agent technologies and data mining for building probabilistic decision models (Bayesian) able to assist the performance improvement process of a DBMS. This model is used to build the ATTuneDB DBMS fine-tuning tool. Receiving information about the real workload being submitted to a PostgreSQL DBMS, and using the probabilistic model, the tool is able to identify the type of the workload, and find the best set of value for the parameters of this DBMS, thus, supporting the DBA on the task of optimizing the DBMS performance.