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An Intelligent Sampling Framework for Controlled Experimentation and QoE Modeling

Abstract : For internet applications, measuring, modeling and predicting the quality experienced by end users as a function of network conditions is challenging. A common approach for building application specific Quality of Experience (QoE) models is to rely on controlled experimentation. For accurate QoE modeling, this approach can result in a large number of experiments to carry out because of the multiplicity of the network features, their large span (e.g., bandwidth, delay) and the time needed to setup the experiments themselves. However, most often, the space of network features in which experimentations are carried out shows a high degree of similarity in the training labels of QoE. This similarity, difficult to predict beforehand, amplifies the training cost with little or no improvement in QoE modeling accuracy. So, in this paper, we aim to exploit this similarity, and propose a methodology based on active learning, to sample the experimental space intelligently, so that the training cost of experimentation is reduced. We validate our approach for the case of YouTube video streaming QoE modeling from out-of-band network performance measurements, and perform a rigorous analysis of our approach to quantify the gain of active sampling over uniform sampling.
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Contributor : Chadi Barakat <>
Submitted on : Friday, October 26, 2018 - 2:38:21 PM
Last modification on : Monday, December 14, 2020 - 5:38:27 PM
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Muhammad Jawad Khokhar, Nawfal Saber, Thierry Spetebroot, Chadi Barakat. An Intelligent Sampling Framework for Controlled Experimentation and QoE Modeling. Computer Networks, Elsevier, 2018, 147, pp.246-261. ⟨10.1016/j.comnet.2018.10.011⟩. ⟨hal-01906145⟩



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