Energy-aware architectures provide applications with a mix of low and high frequency cores. Selecting the best core configurations for running programs is very challenging. Here, we leverage compilation, runtime monitoring and machine learning to map program phases to their best matching configurations. As a proof-of-concept, we devise the Astro system to show that our approach can outperform a state-of-the-art Linux scheduler for heterogeneous architectures.