goal measurement overviewprs/15-744-f12/lectures/olympics-yixin.pdf · 11/5/2012 2 measurement...

11/5/2012

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Inside the Bird's Nest: Measurements of Large‐Scale Live

VoD from the 2008 OlympicsAuthors: Hao Yin, Xuening Liu, Feng Qiu, Ning Xia, Chuang Lin, Hui Zhang, Vyas Sekar, Geyong MinPresenter: Yixin LuoFor 15‐744 Computer NetworksInstructor: Peter Steenkiste

Reference: Hao Yin, Xuening Liu, Feng Qiu, Ning Xia, Chuang Lin, Hui Zhang, Vyas Sekar, and Geyong Min. 2009. Inside the bird's nest: measurements of large‐scale live VoD from the 2008 olympics. In Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference (IMC '09). ACM, New York, NY, USA, 442‐455. DOI=10.1145/1644893.1644946 http://doi.acm.org/10.1145/1644893.1644946

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Outline

• Goal• Measurement Overview• Live VoD Characteristic• Understanding User Behavior• Analysis of Flash Crowds• Impact of Presentation Models• Summary and Implications

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Goal

Study live VoD and user behaviors:• How does the access pattern of this live differ from traditional Video‐on‐Demand(VoD) and User‐Generated Content system

• How presentation models affect user behavior• Flash‐crowd phenomenaAnd discuss implications for future live VoDsystems based on observations

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Measurement Overview‐ VoD System Architecture

360 FMSesIn 8 districts

27 FMSes

95 FMSes

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Measurement Overview‐ Content Providers

• Official Olympics video website (Off)– Content length vary from 10s to 2.5h

• Social networking site (Soc)– No embedded advertisements

• Official Olympics vedeo syndication site (Synd)– Linked by about 174 websites– Mainly fragments of matches

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Live VoD Characteristic

• Diverse video length• Anticipated and unanticipated peak days• Rush hours• Skew in video popularity of live VoD• Frequent change of popular content

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Anticipated and Unanticipated Peak Days

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Two peaks



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Skew in Video Popularity

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Top‐10% videos contribute 80% of the total accessTop‐20% videos contribute 90% of the total access

Even more skewed in Soc

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Understanding User Behavior

• Viewing percentage is inversely proportional to the video duration

• Infrequent use of streaming capabilities• Implications of user behaviors

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Viewing Percentage vs. Video Length

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Viewing Time vs. Video Length

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Actual video length



• Infrequent use of streaming capabilities– 80% of the sessions have no user operations– “stop” and “play” events

• Implications of user behaviors

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– Cache the initial segments of many videos– Simpler delivery modes instead of streaming

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Analysis of Flash Crowds

• Correlated accesses• Sooner released content gets bigger flash crowds

• Implication of flash crowd

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Flash Crowd of Men’s 100m Race

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100m race qualifying stage

100m final race

Related video receive similar flash crowd




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Flash Crowd vs. Release Time

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• Implication of (avoiding) flash crowd– Users can be satisfied by providing related videos– VoD system can defer releasing new content during overload situations

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Impact of Presentation Models

• More concentrated accesses in Soc• Video popularity more skewed in Soc• Little effect of pre‐video advertisements• Implications

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Time to Reach 80% Accesses

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SocOff

Videos become hot much faster in Social Networks

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Skew of Popularity in Soc

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Popular videos are more popular in Social networks



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User Behavior During 30s Ads

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– Soc brings flash crowds of much greater speed and magnitude

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Summary and Implications

• Observations– Clear flash crowd effects– Viewing times independent of video duration– Users seldom seek or pause– Presentation model affects access patterns– Sooner released videos get more concentrated access

• Implications

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Summary and Implications

• Observations• Implications

– Simpler delivery systems instead of streaming– Cache first few minutes of many long videos– Leverage related videos and defer releasing new content to avoid flash crowds

– Newer presentation models are bringing more focused access and more abrupt flash crowds

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Thoughts

• Some observations are interesting– Popular video contribute most accesses– Viewing time vs. viewing percentage– Release time vs. flash crowd– Newer presentation models aggregate flash crowd

• But not all implications make sense– Avoid flash crowd vs. Overcome flash crowd

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Thank you

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Live VoD Characteristic‐ Diverse video length

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Video length varies a lot especially in Off


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Distinguishable rush hour exists every day


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Rush hour varies everyday

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Popular content change frequently


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goal measurement overviewprs/15-744-f12/lectures/olympics-yixin.pdf · 11/5/2012 2 measurement...

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