发表机构
University of Birmingham（伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出MFTune调优器，利用非保真度环境以更少预算优化可配置系统，在多数案例中性能优于现有调优器，且节省了大量预算。
AI 中文摘要
配置调优对于在给定环境下优化高度可配置系统的性能（如吞吐量或运行时间）至关重要。然而，这一过程颇具挑战性，因为存在大量待调优选项，且配置测量通常成本高昂。本文论证了“少即是多”现象：通过在非保真度环境下进行部分调优，可大幅提升预算利用率，该环境与需调优系统所处的目标保真度环境相似，但测量成本更低。我们整理了可配置系统的保真度概念框架，基于此提出了MFTune调优器，该调优器主动探索超过10^4种可能的非保真度设置空间，以近似出一种兼具调优广度的有用设置，为目标保真度环境生成高质量种子，进而确保调优深度。针对10种最先进调优器的实验在运行各类真实系统19个月（每周7天、每天24小时）后开展，结果显示MFTune在83.33%的案例中表现显著更优，提升幅度最高达19.34%，且通常能节省数小时的预算。
英文摘要
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.
CommentsAccepted by the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
