Camel: Smart, Adaptive Energy Optimization for Mobile Web Interactions

Jie Ren, Lu Yuan, Petteri Nurmi, Xiaoming Wang, Miao Ma, Ling Gao, Zhanyong Tang, Jie Zheng, Zheng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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, CAMEL
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.
Original languageEnglish
Title of host publicationIEEE Conference on Computer Communications (INFOCOM 2020)
Publication date2020
ISBN (Electronic)978-1-7281-6412-0
Publication statusPublished - 2020
MoE publication typeA4 Article in conference proceedings
EventIEEE Conference on Computer Communications - Toronto, Canada
Duration: 6 Jul 20209 Jul 2020
Conference number: 39

Fields of Science

  • 113 Computer and information sciences

Cite this