What should you cache?: a global analysis on YouTube related video caching

Authors:
Dilip Kumar Krishnappa;Michael Zink;Carsten Griwodz
Affiliations:
University of Massachusetts Amherst;University of Massachusetts Amherst;Simula Research Laboratory and University of Oslo
Venue:
Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video
Year:
2013

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Abstract

Following advice from the YouTube recommendation system is one of the ways users browse through the videos offered by YouTube. The system presents related videos based on several factors depending on the current video requested. This related videos list can be used by caching infrastructure to reduce network bandwidth consumption. In this paper, we analyze the differences between user-specific recommendation lists. We perform this analysis on 100s of user nodes from all around the world divided into 4 geographical regions using PlanetLab. Based on our analysis, we find that the related videos differ less in the top half (1-10) of the related video list offered by YouTube compared to the bottom half (11-20). Based on our analysis, we suggest that, caching or prefetching of the Top 10 of the related videos is advantageous over a period of time than caching the whole list offered by YouTube.