EBrowser: Making human-mobile web interactions energy efficient with event rate learning

Due to the limited screen size of mobile devices, finger movements on touchscreen, such as scrolling and pinching (i.e., zooming in or out), are frequently used on mobile Web browsers and WebView-based apps, consuming considerable energy on mobile devices. While existing works on mobile Web browsers focus on reducing the power consumption or optimizing the performance of webpage loading, the power consumption of mobile Web interactions, especially after webpage loading, has received comparatively little attention. Motivated by an empirical study of the power consumption and user experience survey of human-mobile interactions, we design and implement eBrowser, an energy-efficient mobile Web interaction framework. It leverages a cloud-based machine learning model to enable personalized interaction event rate for individual users according to the interaction speed of their finger movement and the content of rendered webpages. To adapt to user behavior changes, eBrowser continuously monitors the interaction experience on each mobile device and periodically updates the personalized event rate model with incremental learning in the cloud. We implement eBrowser in Chromium and deploy the event rate model in a remote Aliyun cloud instance. Experimental results show that eBrowser reduces the energy consumption of mobile Web interactions by up to 43.8% with negligible runtime overhead, while guaranteeing user satisfaction on both mobile browsers and WebView-based apps.