Multi-intention-aware configuration selection for performance tuning
Haochen He, Zhouyang Jia, Shanshan Li, Yue Yu, Cheng-Long Zhou, Qing Min Liao, Ji Wang, Xiangke Liao · Proceedings of the 44th International Conference on Software Engineering · 2022
Automatic configuration tuning helps users who intend to improve software performance. However, the auto-tuners are limited by the huge configuration search space. More importantly, they focus only on performance improvement while being unaware of other important user intentions (e.g., reliability, security). To reduce the search space, researchers mainly focus on pre-selecting performance-related parameters which requires a heavy stage of dynamically running under different configurations to build performance models. Given that other important user intentions are not paid attention to, we focus on guiding users in pre-selecting performance-related parameters in general while warning about side-effects on non-performance intentions. We find that the configuration document often, if it does not always, contains rich information about the parameters' relationship with diverse user intentions, but documents might also be long and domain-specific.