arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00749cs.NIcs.HCcs.PF

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

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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