Finding Faster Configurations using FLASH
This addresses the issue for software engineers who often use sub-optimal configurations, leading to inadequate performance, and it is incremental as it builds on prior model-based methods with improvements in speed and scalability.
The paper tackles the problem of finding optimal configurations for software systems, which is challenging due to the large number of options, and introduces FLASH, a sequential model-based method that reduces the effort required by up to several orders of magnitude compared to state-of-the-art techniques.
Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the (near) optimal configuration, this paper introduces FLASH, a sequential model-based method, which sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this paper is to use the prior knowledge (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the (near) optimal configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to the state of the art techniques.