Watching User Generated Videos with Prefetching
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1 Watching User Generated Videos with Prefetching Samamon Khemmarat, Renjie Zhou, Lixin Gao, Michael Zink Electrical and Computer Engineering Department UMass Amherst MMSys 2011, Feb. 24 th
2 User Generated Videos Professional Produced Videos Netflix Hulu User Generated Videos YouTube, Youku, Tudou Hundreds of millions of short video clips Wide ranges of topics Growing user generated videos Readily available device Production cycle is short
3 Motivation User experience in watching videos is not satisfactory Slow startup time Many pauses during playback
4 Measuring User Experiences Watching YouTube Video download traces from various environments
5 Likelihood of Experiencing Pauses 10 out of 12 environments contain playbacks with pauses 41 out of 117 playbacks (35%) contain pauses
6 Number of Pauses 31 out of 117 playouts (22.6%) contain more than 10 pauses
7 How to improve user experiences?
8 Video Prefetching Scheme Prefetching Agent (PA) Select videos to be prefetched and retrieve their prefixes Store prefixes of prefetched videos At clients (PF-Client) or proxy (PF-Proxy) Predict videos that are most likely to be watched PA determines videos to prefetch from incoming requests
9 How to select videos to prefetch? PA predicts a set of videos to be requested Two main sources of video requests Search Result lists Related Video lists Use top N videos from these lists Advantages Simple Require no additional data Effectiveness?
10 Datasets for Evaluation Traces of data traffic between a campus network and YouTube servers Retrieve Search Result lists and Related video lists via YouTube data API
11 How Often Users Click on Related Videos and Search Results? Determine the referrers of each video request in the traces From URL patterns, e.g., feature=related, feature=channel From inference: look at a browse session to infer requests from Search Result list Related Video lists and Search Results lists are the most frequently used referrers
12 Evaluation Methodology Issue the requests based on real user request traces Keep track of the videos in PA s storage Evaluation metric Hit ratio: How many requests we can serve from the PA s storage? Hit ratio = Hit requests All requests
13 Effectiveness of various scheme combinations Videos from a Related Video list of a user are watched by other users Best combination is using RV-N algorithm with PF-Proxy setting
14 Combining Caching with Prefetching Cache-and-Prefetch can reach up to 81% of hit ratio Improvement is smaller as N increases due to larger overlapping between prefetched videos and cached videos
15 Analyzing Hit Ratios Only half of the hit requests come from RV lists Requests from SR lists is a large portion of the hit requests especially in PF- Proxy setting Recommendation system is a good indicator of topic interest PF- Client PF- Proxy
16 Analyzing the High Hit Ratios RV lists overlap with the video requests generated from other sources (esp. in PF-Proxy) up to 70% PF- Client PF- Proxy
17 Storage Requirement Measured in slots a slot holds one prefix of a video One slot = 2.5 MB (for prefix size of 30% and average video size of 8.4 MB) Require only 5 TB to reach 81% of hit ratio (at N=25)
18 Impact of Storage space Hit ratio decreases with the storage space size Still can achieve hit ratio of around 60% with 125 GB (50k slots) Compared to caching, cache-and-prefetch always performs better
19 Do we need to prefetch the whole video? Prefetching the whole videos is not necessary From analysis of video download traces, each location and each video requires different prefix size
20 Feasibility Traffic Overhead Suppose prefix size = 15%, N = 11 and caching whole videos Caching helps reduce the traffic Pure prefetching yields higher hit ratio while increase traffic by 44% Combining the two results in highest hit ratio and only introduce 2% additional traffic
21 Conclusion Watching videos with prefix prefetching Delay and Pauses are often Prefix prefetching is feasible during browsing Related videos are good interest predictors Prefetching can reach hit ratio over 81% while caching can reach hit ratio of 40%
22 Q&A
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