Quantifying the Potential of Control Algorithms through Large Language Models
Lianchen Jia, Chaoyang Li, Qian Houde, Tianchi Huang, Jiangchuan Liu, Lifeng Sun
Control algorithms in production environments typically require domain experts to tune them for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of tuning potential. To bridge this gap, we introduce \texttt{Crucible}, a novel framework leveraging large language models to quantitatively evaluate the tuning potential of control algorithms. We demonstrate \texttt{Crucible}'s effectiveness through case studies of adaptive bitrate algorithms from networking and scheduling algorithms from systems domains. Our experimental results reveal that \texttt{Crucible} not only quantifies the tunable space across different algorithms but also enables the optimization of existing algorithms by using potential as a new optimization direction, ultimately enhancing performance outcomes. Our code is available in the supplementary materials.