  The web has become a ubiquitous application development platform for mobile
systems. Yet, web access on mobile devices remains an energy-hungry activity.
Prior work in the field mainly focuses on the initial page loading stage, but
fails to exploit the opportunities for energy-efficiency optimization while the
user is interacting with a loaded page. This paper presents a novel approach
for performing energy optimization for interactive mobile web browsing. At the
heart of our approach is a set of machine learning models, which estimate
\emph{at runtime} the frames per second for a given user interaction input by
running the computation-intensive web render engine on a specific processor
core under a given clock speed. We use the learned predictive models as a
utility function to quickly search for the optimal processor setting to
carefully trade responsive time for reduced energy consumption. We integrate
our techniques to the open-source Chromium browser and apply it to two
representative mobile user events: scrolling and pinching (i.e., zoom in and
out). We evaluate the developed system on the landing pages of the top-100
hottest websites and two big.LITTLE heterogeneous mobile platforms. Our
extensive experiments show that the proposed approach reduces the system-wide
energy consumption by over 36\% on average and up to 70\%. This translates to
an over 17\% improvement on energy-efficiency over a state-of-the-art
event-based web browser scheduler, but with significantly fewer violations on
the quality of service.
