Finding Good Configurations in High-Dimensional Spaces: Doing More With Less

Manually tuning tens to hundreds of configuration parameters in a complex software system like a database or an application server is an arduous task. Recent work has looked into automated approaches for recommending good configuration settings that adaptively search the full space of possible configurations. These approaches are based on conducting experiments where each experiment runs the system with a selected configuration to observe the resulting performance. Experiments can be time-consuming and expensive, so only a limited number of experiments can be done even in systems with hundreds of configuration parameters. In this paper, the authors consider the problem of finding good configurations under the two constraints of high dimensionality (i.e., many parameters) and few experiments.