Android's inability to sense web page content during resources allocation to the browser often results in over-allocation of resources and unnecessary loss of power. At the same time, due to the growth of CPU adjustable frequency density, optimizing energy consumption through dynamic voltage and frequency scaling (DVFS) technology becomes increasingly challenging. Furthermore, the role of the graphics processing unit (GPU) in browser operation is ignored under the system's default regulation policy. Aiming at the above problems, we propose a method to optimize power consumption by co-regulating CPU and GPU. First, web pages are classified by logistic regression based on the processor operating characteristics when loading web pages. We assign weights to webpage characteristics to quantify the complexity, and then use DVFS to limit the CPU frequency while adjusting the GPU frequency based on webpage category and complexity. The proposed method is applied to the Chromium browser on Google Pixel2 XL, and tested on the top 500 Chinese websites, resulting in a 12% reduction in power consumption and an average 5% decrease in webpage loading time.
ZHANG Jin, HUANG Jiangjie, PENG Long, LIU Xiaodong, YU Jie, HUANG Haowei, WANG Wenzhu
. Browser Power Optimization Based on CPU-GPU Co-regulation and Web Page Feature Perception[J]. Journal of Applied Sciences, 2024
, 42(1)
: 134
-144
.
DOI: 10.3969/j.issn.0255-8297.2024.01.011
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