KylinTune: DQN-based Energy-efficient Model for Browser in Mobile Devices

Hao Xu, Long Peng, Xiaodong Liu, Menglin Zhang, Jun Ma, Jie Yu, Zibo Yi

Abstract: Browser is a key application for mobile devices and its power management is significant given that mobile devices are power-sensitive. Currently, dynamic voltage and frequency scaling (DVFS) and energy-aware scheduling (EAS) techniques have been implemented in mobile devices for energy savings. However, it is still challenging to achieve an energy-efficient mobile browser due to the varied content of webpages that need different resources to fetch, parser, render, etc. An ideal power governor should adjust CPU frequency dynamically according to webpage characteristics, but the current governor is configured statically and webpage-agnostic. To address the above issues, we propose KylinTune, an energy-efficient model for mobile browsers. The KylinTune is based on Deep-Q Network (DQN), a reinforcement learning technique. KylinTune learns from the browser runtime and adjusts CPU frequency to an optimal execution speed for a specific webpage based on EAS. We apply KylinTune to the Chromium browser on Google Pixel2 XL and evaluate it on the top 100 popular websites. Experimental results show that KylinTune achieves 14.51%–24% energy savings in different loading environments, with trivial quality of service (QoS) degradation.