Predicting database workloads through mining periodic patterns in database audit trails
Information about the periodic changes of intensity and structure of database workloads plays an important role in performance tuning of functional components of database systems. Discovering the patterns in workload information, such as audit trails, traces of user applications, and sequences of dynamic performance views, is a complex and time-consuming task. This work investigates a new approach to analysis of information included in the database audit trails. In particular, it describes the transformations of information included in the audit trails into a format that can be used for discovering the periodic patterns in the fluctuations of database workloads. It presents an algorithm that finds elementary periodic patterns through nested iterations over a four-dimensional space of execution plans of SQL statements and positional parameters of the patterns. It proposes a collection of composition rules for the derivations of complex periodic patterns from the elementary and other complex patterns and it shows how to use such rules to predict the future workload levels.
periodic pattern audit trail automated performance tuning online database design