Less Is More: Tuning Configurable Systems with Imperfect Fidelity

2026-08-01

AI Summary

This work addresses the challenge of configuration tuning in highly configurable systems, where the vast number of options and high cost of performance measurement hinder efficient optimization. The authors propose a “less-is-more” tuning paradigm and introduce MFTune, a novel tuner that operates effectively under imperfect fidelity conditions. By integrating fidelity modeling, active exploration, and seed-guided optimization, MFTune efficiently navigates large configuration spaces to identify near-optimal configurations. Over 19 months of continuous experimentation, MFTune outperformed ten state-of-the-art tuners in 83.33% of scenarios, achieving up to a 19.34% performance improvement while substantially reducing tuning budget requirements.

Abstract

Configuration tuning is essential for optimizing the performance of highly configurable systems, e.g., throughput or runtime, under a given environment. Yet, this is a challenging process as there can be many options to tune, and configuration measurement is often highly expensive. In this paper, we demonstrate the phenomenon of ``less can be more'': system configuration tuning can be greatly improved with much superior budget utilization by partially tuning under the imperfect-fidelity---an environment that is similar, but cheaper to measure, compared with the concerned perfect-fidelity of environment under which the system should be tuned. We codify a conceptual framework of fidelity for configurable systems, drawing on which allows us to propose MFTune, a tuner that proactively explores in the space of $>10^4$ possible imperfect-fidelity settings to approximate a useful one, which strikes for the wideness of tuning. This creates high-quality seeds for the perfect-fidelity, which in turn ensures the tuning depth. Experiment results against $10$ state-of-the-art tuners, obtained from running diverse real-world systems for $19$ months $24 \times 7$, show that MFTune performs considerably better on $83.33$\% cases with up to $19.34\%$ improvement while achieving hours of budget saving in general.

Problem

• configuration tuning
• imperfect fidelity
• budget efficiency
• highly configurable systems
• performance optimization

Innovation

• imperfect-fidelity
• configuration tuning
• MFTune
• fidelity framework
• budget-efficient optimization