Tun-OCM: A model-driven approach to support database tuning decision making

Highlights：

• The development of a DSS depends on issues that affect DB performance, which is addressed by DB tuning decisions.

• DB tuning is essentially a configuration management (CM) task and a given configuration decision may impact others.

• The Tun-Ocm model provides formal and platform-independent knowledge that fosters interoperability among different DBMS.

• Tun-Ocm increases auditability and enables predictive analysis of the impact of tuning actions on existing structures.

• An ontology pattern language for CM can be applied to any DSS domain where part of the purpose is a configuration problem.

摘要

Database tuning is a task executed by Database Administrators (DBAs) based on their practical experience and on tuning systems, which support DBA actions towards improving the performance of a database system. It is notoriously a complex task that requires precise domain knowledge about possible database configurations. Ideally, a DBA should keep track of several Database Management Systems (DBMS) parameters, configure data structures, and must be aware about possible interferences among several database (DB) configurations. We claim that an automatic tuning system is a decision support system and DB tuning may also be seen as a configuration management task. Therefore, we may characterize it by means of a formal domain conceptualization, benefiting from existing control practices and computational support in the configuration management domain. This work presents Tun-OCM, a conceptual model represented as a well-founded ontology, that encompasses a novel characterization of the database tuning domain as a configuration management conceptualization to support decision making. We develop and represent Tun-OCM using the CM-OPL methodology and its underlying language. The benefits of Tun-OCM are discussed by instantiating it in a real scenario.