The typical workload in a database system consists of a mixture of multiple queries of different types, running concurrently and interacting with each other. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this paper, we use such a reasoning approach to develop a query scheduler. We treat the database system as a black box and experimentally build a model to estimate the performance of different query mixes. Our scheduler uses this model to decide which query mixes to schedule, with the goal of maximizing throughput. We experimentally demonstrate the effectiveness of our scheduler using queries from the TPC-H benchmark on DB2. © 2008 IEEE.