In serverless computing, cold start results in long response latency. Existing approaches strive to alleviate the issue by reducing the number of cold starts. However, our measurement based on real-world production traces shows that the minimum number of cold starts does not equate to the minimum response latency, and solely focusing on optimizing the number of cold starts will lead to sub-optimal performance. The root cause is that functions have different priorities in terms of latency benefits by transferring a cold start to a warm start. In this paper, we propose Incendio, a serverless computing framework exploiting priority-based scheduling to minimize the overall response latency from the perspective of cloud providers. We reveal the priority of a function is correlated to multiple factors and design a priority model based on Spearman’s rank correlation coefficient. We integrate a hybrid Prophet-LightGBM prediction model to dynamically manage runtime pools, which enables the system to prewarm containers in advance and terminate containers at the appropriate time. Furthermore, to satisfy the low-cost and high-accuracy requirements in serverless computing, we propose a Clustered Reinforcement Learning-based function scheduling strategy. The evaluations show that Incendio speeds up the native system by 1.4×, and achieves 23% and 14.8% latency reductions compared to two state-of-the-art approaches.