Industry practice of configuration auto-tuning for cloud applications and services
Runzhe Wang, Qinglong Wang, Yuxi Hu, Heyuan Shi, Yuheng Shen, Yu Zhan, Ying Fu, Zheng Liu, Xiaohai Shi, Yu Jiang · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · 2022
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.