Comprehensible models for reconfiguring enterprise relational databases to avoid incidents

Abstract

Configuring enterprise database management systems is a notoriously hard problem. The combinatorial parameter space makes it intractable to run and observe the DBMS behavior in all scenarios. Thus, the database administrator has the difficult task of choosing DBMS configurations that potentially lead to critical incidents, thus hindering its availability or performance. We propose using machine learning to understand how configuring a DBMS can lead to such high risk incidents. We collect historical data from three IT environments that run both IBM DB2 and Oracle DBMS. Then, we implement several linear and non-linear multivariate models to identify and learn from high risk configurations. We analyze their performance, in terms of accuracy, cost, generalization and interpretability. Results show that high risk configurations can be identified with extremely high accuracy and that the database administrator can potentially benefit from the rules extracted to reconfigure in order to prevent incidents.