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Web technology underpins many interactive mobile applications. However,energy-efficient mobile web interactions is an outstanding challenge. Given theincreasing diversity and complexity of mobile hardware, any practicaloptimization scheme must work for a wide range of users, mobile platforms andweb workloads. This paper presents CAMEL , a novel energy optimization systemfor mobile web interactions. CAMEL leverages machine learning techniques todevelop a smart, adaptive scheme to judiciously trade performance for reducedpower consumption. Unlike prior work, C AMEL directly models how a given webcontent affects the user expectation and uses this to guide energyoptimization. It goes further by employing transfer learning and conformalpredictions to tune a previously learned model in the end-user environment andimprove it over time. We apply CAMEL to Chromium and evaluate it on fourdistinct mobile systems involving 1,000 testing webpages and 30 users. Comparedto four state-of-the-art web-event optimizers, CAMEL delivers 22% more energysavings, but with 49% fewer violations on the quality of user experience, andexhibits orders of magnitudes less overhead when targeting a new computingenvironment.

Smart, Adaptive Energy Optimization for Mobile Web Interactions