New Search

Export article

Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

Nikolas Wehner, Michael Seufert, Joshua Schuler, Sarah Wassermann, Pedro Casas, Tobias Hossfeld

Abstract: This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.
Keywords: machine learning / web qoe / qoe monitoring / rnn

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

Share this article

Click here to see the statistics on "ACM SIGMETRICS Performance Evaluation Review" .
References (5)
    Back to Top Top