Speeding up webpage loads is a crosslayer optimization problem that depends on webpage structure and network conditions. Yet, traditional HTTP resource prioritization forgoes combining resource dependency and network state data, resulting in varying performance. More sophisticated optimization approaches increasingly incorporate crosslayer data, but they usually gather it a-priori, questioning the practical applicability. We present xPrio, a reinforcement learning-based resource prioritization approach that provides a scalable middleground: it avoids costly a-priori knowledge, but still includes crosslayer data from browser and transport layer signals collected at runtime. xPrio turns this readily available information into actionable resource priorities that avoid detriments of traditional strategies and achieves mean SpeedIndex speedups above 15% on pages of the Alexa Top 500. As such, xPrio can widely improve performance with little overhead in use.
Sander et al. (Mon,) studied this question.