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Detecting Degradation of Web Browsing Quality of Experience

Quality of Experience (QoE) inference, and particularly the detection of its degradation is an important management tool for ISPs. Yet, this task is made difficult due to widespread use of encryption on the data-plane on the one hand so that measuring QoE is hard, and to the ephemeral properties of the web content on the other hand so that changes in QoE indicators may be rooted in changes in properties of the content itself, more than being caused by network-related events. In this paper, we phrase the QoE degradation detection issue as a change point detection problem, that we tackle by leveraging a unique dataset consisting on several hundreds thousands browsing sessions spanning multiple months. Our results, beyond showing feasibility, warn about the exclusive use of QoE indicators that are very close to content, as changes in the content space can lead to false alarms that are not tied to network-related problems.