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What should you Cache? A Global Analysis on YouTube Related Video Caching

What should you Cache? A Global Analysis on YouTube Related Video Caching. Dilip Kumar Krishnappa , Michael Zink and Carsten Griwodz NOSSDAV 2013. Outline . Introduction Motivation Objective Experiment Setup Results Impact Conclusion. Introduction.

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What should you Cache? A Global Analysis on YouTube Related Video Caching

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  1. What should you Cache? A Global Analysis on YouTube Related Video Caching Dilip Kumar Krishnappa, Michael Zink and Carsten Griwodz NOSSDAV 2013

  2. Outline • Introduction • Motivation • Objective • Experiment Setup • Results • Impact • Conclusion

  3. Introduction • Most popular user-generated video service. • 800 million unique users, billions of hours of videos. • Globally distributed levels of caches. • Difficult to predict videos watched compared to other video services (E.g., Netflix or Hulu).

  4. Motivation

  5. Motivation (Contd..) • Most people select their videos from related list. • Out of the 20 related list offered, most people tend to select videos from the Top 10. • Caching and Prefetching of related videos are shown to be effective. • Streaming quality and network load reduction can be achieved.

  6. Related List Reordering Cache hit rate increases by 2 to 5 times by reordering.

  7. Objective • To find out if the related videos list offered change based on region and time. • How much of the related video list changes? • What is the impact of these related video changes on caching or prefetching?

  8. Experiment Setup • PlanetLab Measurement for global analysis. • 4 different regions (US, EU, AS, SA). • US – 197 nodes, EU- 243 nodes, AS – 62 nodes and SA – 17 nodes. • 519 total nodes and 100 random videos.

  9. Metrics Content Change CC = 2, OC = 4 Order Change CC = 0, OC = 2

  10. Metrics (Contd..)

  11. Analysis Results (Content Change) EU Region US Region

  12. Order Change Results EU Region US Region

  13. Daily Change (Content Change) EU Region US Region

  14. Daily Changes (Order Change) EU Region US Region

  15. Impact on Caching?

  16. Impact on Regional Differences • 35% CC related list difference of at least 2 for Top 5 related videos. • 60% requests for Top 5 videos. • Leads to 21% additional caching of content. • 65% related list difference of at least 2 for the bottom half. • But only 20% requests for bottom half of related videos. • For OC related list difference, 60% of at least 3 in order for Top 5. • Affects the related list reordering technique. • OC increases to 90% for bottom half of list.

  17. Impact on Client Differences • 42% hit rate of client caching/prefetching for Top 10 related videos. • Related list differences of at least 3 for Top 10 is about 20% and 40% for bottom half. • Leads to 8% additional caching/prefetching of content at client. • 6% improvement in cache hit rate for bottom half but 40% increase in list difference.

  18. Conclusion • We perform a global study on related list behavior. • We find that the list changes from region to region and also on the same client on daily basis. • This list difference reduces the efficiency of caching on the edge or at client. • By the analysis, we find that caching Top half of related list offers better trade-off between cache hit rate and list changes.

  19. Future Work • How related list offered differs based on different factors? • Such as popularity of videos, view count, region etc., • What parts of related list transmitted to clients are already stored in YouTube Cache? • We can use the cache reordering approach to know if it is delivered by cache.

  20. Thank You Any Questions??

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