Video Sessions KPIs clustering framework in CDNs

Abstract

Users’ viewing experience in the video delivery process is of paramount importance for Content Delivery Networks (CDNs). Throughout their operations, CDN providers target the satisfaction of users’ expectations in terms of Quality of Experience (QoE). In this context, CDN providers need to acquire knowledge on users’ QoE and correlate observations through different video sessions in order to identify QoE degradations and investigate their potential root cause. In the absence of users’ feedback on their QoE, CDN providers can monitor and analyze Key Performance Indicators (KPIs) throughout video sessions. This allows to assess the Quality of Service (QoS) offered to users, influencing their QoE. However, due to the large number of sessions handled by CDN operators, it is not possible to conduct such an analysis manually. In this work, we introduce a framework that allows to automatically group a large set of video sessions into a small number of representative clusters, with each cluster containing video sessions with similar patterns of KPIs. The framework builds upon a set of features representing the evolution of KPIs over a session. It relies on an unsupervised machine learning algorithm to form the clusters. We evaluate the framework over a real-world dataset with traffic logs relating to thousands of sessions. The obtained results underline the capabilities of the proposed framework.