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Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

Research output: Contribution to journalJournal article

E-pub ahead of print
  • Jie Ren
  • Xiaoming Wang
  • Jianbin Fang
  • Yansong Feng
  • Zheng Wang
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<mark>Journal publication date</mark>24/09/2018
<mark>Journal</mark>IEEE Computer Architecture Letters
Number of pages4
Publication statusE-pub ahead of print
Early online date24/09/18
Original languageEnglish

Abstract

Web browsing is an important application domain, but it imposes a significant power burden on mobile devices. While the heterogeneous multi-core design offers the potential for energy-efficient computing, existing web browsers fail to exploit the hardware to optimize mobile web browsing. Our work aims to offer a better way to optimize web browsing on heterogeneous mobile devices. We achieve this by developing a machine learning based approach to predict the optimal processor setting for rendering the web content. The prediction is based on the web content, the network status and the optimization goal. We evaluate our approach by applying it to the Chromium browser and testing it on a representative big.LITTLE mobile platform. We apply our approach to the top 1,000 hottest websites across seven typical networking environments. Our approach achieves over 80% of the performance delivered by a perfect predictor. Our approach achieves over 30%, 50%, and 60% improvement respectively for load time, energy consumption and the energy delay product when compared to two state-of-the arts approaches.

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©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.