The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Computer Networks: The International Journal of Computer and Telecommunications Networking
Accelerating YouTube with video correlation
WSM '09 Proceedings of the first SIGMM workshop on Social media
The impact of YouTube recommendation system on video views
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Watching user generated videos with prefetching
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Boosting video popularity through recommendation systems
Databases and Social Networks
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In this paper, we take advantage of the user behavior of requesting videos from the related list provided by YouTube and the user behavior of requesting videos from the top of this related list to improve the performance of YouTube's caches. We recommend a related list reordering approach which modifies the order of the videos shown on the related list based on the content in the cache. The main goal of our reordering approach is to push the contents already in the cache to the top of the related list and push non-cached contents towards the bottom, which increases the likelihood that the already cached content will be chosen by the viewer. We analyze the benefits of our approach by an investigation that is based on two traces collected from an university campus. Our analysis shows that the proposed reordering approach for related list would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. The increase in hit rate would lead to a 5.12% to 18.19% reduction in server load or back-end bandwidth usage. This increase in hit rate and reduction in back-end bandwidth reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.