[Proceeding] YouLighter: An Unsupervised Methodology to Unveil YouTube CDN Changes

YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users’ Quality of Experience (QoE) while having no control on the CDN decisions. This paper presents YouLighter, an unsupervised technique to identify changes in the YouTube CDN. YouLighter leverages only passive measurements to cluster co-located identical caches into edge-nodes. This automatically unveils the structure of YouTube’s CDN. Further, we propose a new metric, called Pattern Dissimilarity, that compares the clustering obtained from two different time snapshots, to pinpoint sudden changes. While several approaches allows us to compare the clustering results from the same dataset, no technique measures the similarity of clusters from different datasets. Hence, we develop a novel methodology, based on the Pattern Dissimilarity, to solve this problem. By running YouLighter over 10-month long traces obtained from ISPs, we pinpoint both sudden changes in edge-node allocation, and modifications to the cache allocation policy which actually impair the QoE that the end-users perceive.