一句话：提出基于锦标赛的性能调优方法 DarwinGame，在噪声云环境中比较参数配置，显著减少执行时间。
该论文关注共享且易受干扰的云计算环境中的应用性能调优问题。现有调优器在调优过程中无法考虑干扰，导致设计上显著次优。DarwinGame 采用基于锦标赛的设计，系统性地比较不同可调参数配置下的应用执行，从而在噪声环境中识别不同参数配置的相对性能。评估表明，与现有方案相比，DarwinGame 将执行时间减少超过 27%，性能波动低于 0.5%。它是首个帮助开发者在共享、易受干扰的云环境中调优应用的性能调优器。
This work introduces a new subarea of performance tuning -- performance tuning in a shared interference-prone computing environment. We demonstrate that existing tuners are significantly suboptimal by design because of their inability to account for interference during tuning. Our solution, DarwinGame, employs a tournament-based design to systematically compare application executions with different tunable parameter configurations, enabling it to identify the relative performance of different tunable parameter configurations in a noisy environment. Compared to existing solutions, DarwinGame achieves more than 27% reduction in execution time, with less than 0.5% performance variability. DarwinGame is the first performance tuner that will help developers tune their applications in shared, interference-prone, cloud environments.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2509-25090,
title = {DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments},
author = {Rohan Basu Roy and Vijay Gadepally and Devesh Tiwari},
booktitle = {ASPLOS},
year = {2025},
doi = {10.1145/3669940.3707259}
}