Finding Faster Configurations using FLASH

TL;DR

FLASH is a scalable, efficient sequential model-based method that significantly reduces effort in finding near-optimal configurations for software systems, outperforming existing techniques in speed and effectiveness.

Contribution

The paper introduces FLASH, a novel sequential model-based approach that leverages prior knowledge to efficiently explore configuration spaces for software optimization.

Findings

FLASH outperforms state-of-the-art methods in speed and effort reduction.

FLASH successfully scales to complex software systems.

Effort savings of up to several orders of magnitude in diverse scenarios.

Abstract

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.