TY - GEN

T1 - Optimise web browsing on heterogeneous mobile platforms

T2 - a machine learning based approach

AU - Ren, Jie

AU - Gao, Ling

AU - Wang, Hai

AU - Wang, Zheng

N1 - ©2017 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.

PY - 2017/10/5

Y1 - 2017/10/5

N2 - Web browsing is an activity that billions of mobile users perform on a daily basis. Battery life is a primary concern to many mobile users who often find their phone has died at most inconvenient times. The heterogeneous mobile architecture is a solution for energy-efficient mobile web browsing. However, the current mobile web browsers rely on the operating system to exploit the underlying architecture, which has no knowledge of the individual web workload and often leads to poor energy efficiency. This paper describes an automatic approach to render mobile web workloads for performance and energy efficiency. It achieves this by developing a machine learning based approach to predict which processor to use to run the web browser rendering engine and at what frequencies the processor cores of the system should operate. Our predictor learns offline from a set of training web workloads. The built predictor is then integrated into the browser to predict the optimal processor configuration at runtime, taking into account the web workloadcharacteristics and the optimisation goal: whether it is load time, energy consumption or a trade-off between them. We evaluate our approach on a representative ARM big.LITTLE mobilearchitecture using the hottest 500 webpages. Our approach achieves 80% of the performance delivered by an ideal predictor. We obtain, on average, 45%, 63.5% and 81% improvementrespectively for load time, energy consumption and the energy delay product, when compared to the Linux governor

AB - Web browsing is an activity that billions of mobile users perform on a daily basis. Battery life is a primary concern to many mobile users who often find their phone has died at most inconvenient times. The heterogeneous mobile architecture is a solution for energy-efficient mobile web browsing. However, the current mobile web browsers rely on the operating system to exploit the underlying architecture, which has no knowledge of the individual web workload and often leads to poor energy efficiency. This paper describes an automatic approach to render mobile web workloads for performance and energy efficiency. It achieves this by developing a machine learning based approach to predict which processor to use to run the web browser rendering engine and at what frequencies the processor cores of the system should operate. Our predictor learns offline from a set of training web workloads. The built predictor is then integrated into the browser to predict the optimal processor configuration at runtime, taking into account the web workloadcharacteristics and the optimisation goal: whether it is load time, energy consumption or a trade-off between them. We evaluate our approach on a representative ARM big.LITTLE mobilearchitecture using the hottest 500 webpages. Our approach achieves 80% of the performance delivered by an ideal predictor. We obtain, on average, 45%, 63.5% and 81% improvementrespectively for load time, energy consumption and the energy delay product, when compared to the Linux governor

KW - Mobile Web Browsing

KW - Energy Optimisation

KW - big.LITTLE

KW - Mobile Workloads

U2 - 10.1109/INFOCOM.2017.8057087

DO - 10.1109/INFOCOM.2017.8057087

M3 - Conference contribution/Paper

SN - 9781509053377

BT - IEEE International Conference on Computer Communications (INFOCOM), 2017

PB - IEEE

ER -