论文标题

用于移动网络互动的智能,自适应能量优化

Smart, Adaptive Energy Optimization for Mobile Web Interactions

论文作者

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

论文摘要

Web技术支持了许多交互式移动应用程序。但是,节能移动网络交互是一个重大挑战。鉴于移动硬件的多样性和复杂性的增加,任何实用的优化方案都必须适用于广泛的用户,移动平台和Web工作负载。本文介绍了骆驼,这是一种用于移动Web交互的新型能源优化系统。骆驼利用机器学习技术来开发一种智能的自适应计划,以明智地贸易绩效,以减少功耗。与先前的工作不同,C Amel直接建模给定的Web内容如何影响用户的期望并使用它来指导能量优化。它通过采用转移学习和保形预测来进一步调整最终用户环境中先前学习的模型并随着时间的推移而改善它。我们将骆驼应用于铬并在四个不同的移动系统上进行评估,涉及1,000个测试网页和30个用户。与四个最先进的Web事件优化器相比,Camel节省了22%的能源,但对用户体验质量的违规行为减少了49%,并且在针对新的计算环境时,较小的高度命令较小。

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.

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