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🔗 Source: opus.bibliothek.uni-augsburg.de
📊 Size: 352 KB
📄 Pages: 2 pages
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Summary

Researchers propose a machine learning approach to infer the Speed Index (SI) of web page loading sessions from encrypted network traffic, focusing on mobile devices. They collect a dataset of over 30,000 web page loading sessions from the top 500 websites, using three types of devices (smartphones, tablets, and desktops) and various network configurations. The study uses supervised machine learning models to map network-traffic features into the SI metric, achieving good performance in inferring the RUM Speed Index (RUMSI) metric. The results show that the proposed approach can accurately infer Web QoE metrics from encrypted traffic, with mean absolute errors (MAE) ranging from 298 to 1082 ms and mean relative errors (MRE) ranging from 11% to 34%.

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Researchers propose a machine learning approach to infer the Speed Index (SI) of web page loading sessions from encrypted network traffic, focusing on mobile...

Technical Information

  • File Format: PDF
  • File Size: 352 KB
  • Pages: 2
  • Language: EN
  • Total Downloads: 66
  • Last Updated: 7 hours ago

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