Abstract. Self-tuning is a feature that enables the automation of DBMS functions. The main goal is to keep a good performance while minimizing the DBA interaction with the system. This work proposes an architecture for DBMS automatic tuning. We consider here tuning as a global issue, given that changes of a single parameter can affect the performance of other operations. Besides describing the architecture details, we discuss its integration within PostgreSQL in a implementation based on software agents. This way we provide a more flexible approach to include automatic tuning features in a DBMS. Resumo. A auto-sintonia é uma característica que automatiza as operações de um SGBD visando manter um bom desempenho enquanto a interação do DBA com o sistema é reduzida. Este trabalho propõe uma arquitetura para sintonia automática de um SGBD. A tarefa de sintonia é tratada aqui como um problema global, dado que alterações de um parâmetro podem ter reflexos no desempenho de outras operações. Além da descrição dos detalhes da arquitetura, discutimos também sua integração ao PostgreSQL em uma implementação baseada em agentes de software. Dessa forma temos uma abordagem mais flexível para a inclusão de atividades de sintonia automática em um SGBD. 1.

Current businesses rely heavily on efficient access to their databases. Manual tuning of these database systems by performance experts is increasingly infeasible: For small companies, hiring an expert may be too expensive; for large enterprises, even an expert may not fully understand the interaction between a large system and its multiple changing workloads. This trend has led major vendors to offer tools that automatically and dynamically tune a database system. Many database tuning knobs concern the buffer pool for caching data and disk pages. Specifically, these knobs control the buffer allocation and thus the cache miss probability, which has direct impact on performance. Previous methods for automatic buffer tuning are based on simulation, black-box control, gradient descent, and empirical equations. This article presents a new approach, using calculations with an analytically-derived equation that relates miss probability to buffer allocation; this equation fits four buffer replacement policies, as well as twelve datasets from mainframes running commercial databases in large corporations. The equation identifies a buffer-size limit that is useful for buffer tuning and powering down idle buffers. It can also replace simulation in predicting I/O costs. Experiments with PostgreSQL