  Web technology underpins many interactive mobile applications. However,
energy-efficient mobile web interactions is an outstanding challenge. Given the
increasing diversity and complexity of mobile hardware, any practical
optimization scheme must work for a wide range of users, mobile platforms and
web workloads. This paper presents CAMEL , a novel energy optimization system
for mobile web interactions. CAMEL leverages machine learning techniques to
develop a smart, adaptive scheme to judiciously trade performance for reduced
power consumption. Unlike prior work, C AMEL directly models how a given web
content affects the user expectation and uses this to guide energy
optimization. It goes further by employing transfer learning and conformal
predictions to tune a previously learned model in the end-user environment and
improve it over time. We apply CAMEL to Chromium and evaluate it on four
distinct mobile systems involving 1,000 testing webpages and 30 users. Compared
to four state-of-the-art web-event optimizers, CAMEL delivers 22% more energy
savings, but with 49% fewer violations on the quality of user experience, and
exhibits orders of magnitudes less overhead when targeting a new computing
environment.
