一句话：提出轻量自动化调优工具 KeenTune，用代理模型筛选参数，显著提升云应用吞吐性能。
云应用性能测试与优化困难，手动调参云计算栈繁琐，且面向云服务的自动化调优工具少见。KeenTune 是可与待调优应用一同部署的轻量灵活工具，对被调应用性能影响可忽略。它采用可用机器学习模型实现的代理模型，过滤掉不相关参数，从而高效调优。实证评估表明，KeenTune 显著提升 Nginx Web 服务器吞吐性能，部分场景性能提升达 90.43% 和 117.23%。研究说明 KeenTune 可实现高效有效的云应用性能测试，并提供视频与源码。
The performance testing and optimization of cloud applications is challenging, because manual tuning of cloud computing stacks is tedious and automated tuning tools are rare used for cloud services. To address this issue, we introduce KeenTune, an automated tuning tool designed to optimize application performance and facilitate performance testing. KeenTune is a lightweight and flexible tool that can be deployed with to-be-tuned applications with negligible impact on their performance. Specifically, KeenTune uses a surrogate model that can be implemented with machine learning models to filter out less relevant parameters for efficient tuning. Our empirical evaluation shows that KeenTune significantly enhances the throughput performance of Nginx web servers, resulting in performance improvements of up to 90.43% and 117.23% in certain cases. This study highlights the benefits of using KeenTune for achieving efficient and effective performance testing of cloud applications. The video and source code for KeenTune are provided as supplementary materials.
@inproceedings{WangWHSLMSS23,
title = {KeenTune: Automated Tuning Tool for Cloud Application Performance Testing and Optimization},
author = {Qinglong Wang and Runzhe Wang and Yuxi Hu and Xiaohai Shi and Zheng Liu and Tao Ma and Houbing Song and Heyuan Shi},
booktitle = {ISSTA},
year = {2023},
doi = {10.1145/3597926.3604920}
}