Robust optimization for performance tuning of modern database systems

Issues regarding design and management of database systems have been studied by applying operations research (OR) techniques. The purpose of this study is to propose a new alternative towards database performance tuning for query-processing needs of modern database systems from the perspective of operations research using robust optimization. We use a query-driven approach to specify database structures (schema) so that they are robust to uncertainty and dynamics of queries in a changing environment and allow fast and timely information retrieval and exchange. Instead of applying hardware tuning or traditional database tuning techniques, we examine queries by their types and properties to derive database structures that are robust at efficiently processing future queries of any type. This query-driven approach improves the efficiency of processing queries by setting up database structures based on the queries' information needs. This new methodology provides a new approach of tuning database performance that is robust to unexpected changes and dynamics. To further demonstrate the idea, we develop a robust optimization model using a non-linear von Neumann–Morgenstern expected utility function and present two computational examples.