一句话:提出 MFTune,利用低成本非完美保真度环境为高成本完美保真度调优生成优质种子,提升预算利用率。
可配置系统调优对吞吐、运行时等性能至关重要,但选项多、配置测量昂贵。论文提出“少即是多”现象:在相似但测量更廉价的非完美保真度环境中部分调优,可显著改善预算利用。作者形式化可配置系统的保真度概念框架,并实现 MFTune,主动在超过一万种非完美保真度设置中探索,以近似有用设置,从而兼顾调优广度,并为完美保真度调优生成高质量种子以保证深度。对 10 个先进调优器、多种真实系统连续 19 个月 24×7 的实验中,MFTune 在 83.33% 情形更优,最高提升 19.34%,且通常节省数小时预算。
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.