一句话：扩展开源调优工具 KeenTune，针对云应用配置自动调优的五项工业挑战提出解决方案，并在多种企业级服务上验证性能提升。
配置自动调优对参数众多的云应用与服务性能优化很重要，但其系统层次复杂、参数关联紧密。现有工具与算法常忽略工业场景中的实际问题，如工作负载压力控制、分布式部署支持和调优时间成本。本文与企业和工具开发者合作，扩展开源调优工具 KeenTune，从实践中提炼五个关键挑战并给出相应解决方案。改进后的工具被应用于多种典型企业云应用与服务，在 MySQL、OceanBase、nginx、ingress-nginx 上取得 2%-14% 的性能提升，在 ACK 云容器服务上取得 5%-70% 的性能提升，验证了方案的有效性。
Auto-tuning attracts increasing attention in industry practice to optimize the performance of a system with many configurable parameters. It is particularly useful for cloud applications and services since they have complex system hierarchies and intricate knob correlations. However, existing tools and algorithms rarely consider practical problems such as workload pressure control, the support for distributed deployment, and expensive time costs, etc., which are utterly important for enterprise cloud applications and services. In this work, we significantly extend an open source tuning tool – KeenTune to optimize several typical enterprise cloud applications and services. Our practice is in collaboration with enterprise users and tuning tool developers to address the aforementioned problems. Specifically, we highlight five key challenges from our experiences and provide a set of solutions accordingly. Through applying the improved tuning tool to different application scenarios, we achieve 2%-14% improvements for the performance of MySQL, OceanBase, nginx, ingress-nginx, and 5%-70% improvements for the performance of ACK cloud container service.
DOI 原文 ·
@inproceedings{paperbot6972,
title = {Industry practice of configuration auto-tuning for cloud applications and services},
author = {Runzhe Wang and Qinglong Wang and Yuxi Hu and Heyuan Shi and Yuheng Shen and Yu Zhan and Ying Fu and Zheng Liu and Xiaohai Shi and Yu Jiang},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558962}
}