Optimise web browsing on heterogeneous mobile platforms:a machine learning based approach

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 workload characteristics 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 mobile architecture 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% improvement respectively for load time, energy consumption and the energy delay product, when compared to the Linux governo

Adaptive Web Browsing on Mobile Heterogeneous Multi-cores

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