Challenges and Insights from Optimizing Configurable Software Systems

Abstract

Configuring a software system to optimize non-functional properties is a hard task. There are dozens to thousands of configuration options that can affect performance, energy consumption, and other attributes of the resulting program. Even worse, options may interact, such that their combined presence (or absence) has an influence on a non-functional property. In this talk, I report on our experiences with learning different performance models based on a multitude of sampling techniques. The goal is to raise awareness of the distinct challenges in this domain: constraints among options, the exponential search space, and suitable sampling and learning techniques. I show a variety of approaches including their strengths and weaknesses and close the talk with new challenges relevant for our community: changing environments and reproducibility.