• DocumentCode
    3861083
  • Title

    YouLighter: A Cognitive Approach to Unveil YouTube CDN and Changes

  • Author

    Danilo Giordano;Stefano Traverso;Luigi Grimaudo;Marco Mellia;Elena Baralis;Alok Tongaonkar;Sabyasachi Saha

  • Author_Institution
    DET Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Turin, Italy
  • Volume
    1
  • Issue
    2
  • fYear
    2015
  • fDate
    6/1/2015 12:00:00 AM
  • Firstpage
    161
  • Lastpage
    174
  • Abstract
    YouTube relies on a massively distributed content delivery network (CDN) to stream the billions of videos in its catalog. 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 almost no visibility and understanding of CDN decisions. This paper presents YouLighter, an unsupervised technique that builds upon cognitive methodologies to identify changes in how the YouTube CDN serves traffic. YouLighter leverages only passive measurements and clustering algorithms to group caches that appear colocated and identical into edge-nodes. This automatically unveils the YouTube edge-nodes used by the ISP customers. Next, we leverage a new metric, called Pattern Dissimilarity, that compares the clustering results obtained from two different time snapshots to pinpoint sudden changes. By running YouLighter over 10-month long traces obtained from two ISPs in different countries, we pinpoint both sudden changes in edge-node allocation, and small alterations to the cache allocation policies, which actually impair the QoE that the end-users perceive.
  • Keywords
    "YouTube","Videos","Monitoring","Google","Measurement","Algorithm design and analysis","Probes"
  • Journal_Title
    IEEE Transactions on Cognitive Communications and Networking
  • Publisher
    ieee
  • Electronic_ISBN
    2332-7731
  • Type

    jour

  • DOI
    10.1109/TCCN.2016.2517004
  • Filename
    7378926