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Characteristics of Streaming Media Stored on the Web

Characteristics of Streaming Media Stored on the Web. Mingzhe Li, Mark Claypool, Robert Kinicki and James Nichols. ACM Transactions on Internet Technology (TOIT) (Accepted for Publication, probably 2005). Introduction (1 of 2).

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Characteristics of Streaming Media Stored on the Web

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  1. Characteristics of Streaming Media Stored on the Web • Mingzhe Li, Mark Claypool, Robert Kinicki and James Nichols • ACM Transactions on Internet Technology (TOIT) • (Accepted for Publication, probably 2005)

  2. Introduction (1 of 2) • Improvements to Internet enable users to stream from Web browsers • Across national and cultural boundaries • Web users expect “point and click” to stream • 2001, RealNetworks says 350,000 hours [1] • 2002, CAIDA says streaming is significant fraction of traffic • Going to increase with cellular networks • Concern drives new protocols, routers, etc. to deal with traffic better

  3. Introduction (2 of 2) • Much work that characterizes streaming applications to better understand • Unfortunately, little shows what current streams stored on Web look like • Previous study in 1997 [19] • Looked at every video on the Web • Found Internet could not support streaming • RealPlayer and Media Player not created • In 1985, papers by Ousterhout et al [21] studied characteristics of files • Fundamental in designing new file system  Need study of streaming media stored on the Web to help research today

  4. Investigation (1 of 2) • What are the most popular streaming media products? • Previous studies [12] show very different • Earlier, prevalence of MPEG, AVI, QuickTime made it difficult for new comers • What is the ratio of streaming audio versus streaming video? • Audio has lower bitrate cap (voice, music) than video • Can give current bitrate expectations • Are media durations long-tailed? • Long-tailed can contribute to self-similarity • Self-similar traffic difficult to manage

  5. Investigation (2 of 2) • What are typical streaming media target bitrates? • Direct impact on network traffic • Provides insight into frame resolution, frame rates, color depth • What fraction of streaming codecs being used? • Codecs determine compression efficiency • Knowledge of codec prevalence suggests how fast improvements incorporated

  6. Focus • Focus on commercial • Big 3: Media Player, RealPlayer, QuickTime • Other studies looked at server side or one client • This study broader • Have been p2p studies, but p2p not streamed (mostly) • Instead downloaded, as is file transfer • Build specialized crawler, crawl over 17 million URLs from different starting points, and analyze about 30 thousand clips

  7. Teasers • Volume and relative amount increased since 1997 • Proprietary most prevalent • RealPlayer 1st, Media Player 2nd • Most clips short, with long-tailed duration • Encoded at low-resolution, less than current monitors can handle • Work useful for: • Selecting clip workloads • Generating streaming models

  8. Outline • Introduction (done) • Methodology • Analysis • Sampling Issues • Conclusions

  9. Methodology(Mini-Outline) • Media Crawler • Starting Pages • Measurement

  10. Media Crawler • Modify Larbin Web crawler • Recursively traverses URLs • Avoid loops by caching previous • Identify streaming media based on protocol type • Ex: mms://, rtsp:// • Also examine HTTP extensions

  11. Wanted international and popular International – chose 10 most wired countries Allow for cross cultural analysis If Nielsen gave no additional info, chose domestic newspaper as starting point USA – chose 7 popular themes Allow for cross-content analysis Feb 13, 2003, crawl 1 million from each Took 4 to 24 hours, based on RTT Starting Pages

  12. Measurement of Content Characteristics • Use specialized tools to access each Media URL • Collect: encoding, bitrate, duration, size, … • Tools built from SDK, use player core • RealNetworks: • RealAnalyzer, TestPlay (could not do levels) • Microsoft Media: • Media Analyzer, Wmprop (could do levels) • MPlayer • Open source (could not do bitrate)

  13. Outline • Introduction (done) • Methodology (done) • Analysis • Aggregate analysis • Commercial products • Video • Audio • Codec • Sampling Issues • Conclusions

  14. Aggregate Analysis (1 of 3) • Remove unique, giving about 11 million URLs • About 54,000 were streaming • In 1997, about 25 million URLs • About 22,000 were streaming • Extrapolating  Today, about 15 million total  Increase from 0.09% to 0.47%

  15. Aggregate Analysis (2 of 3) Some “heavy hitters”, more so than typical Web servers

  16. Aggregate Analysis (3 of 3) - Real almost ½ of all streaming content - In 1997, MPEG, AVI, QuickTime were all, but now only 10% combined - MP3 is most popular non-proprietary format

  17. Outline • Introduction (done) • Methodology (done) • Analysis • Aggregate analysis • Commercial products • Video • Audio • Codec • Sampling Issues • Conclusions

  18. Commercial Product Analysis • Run custom tools on commercial • Of original 39,000 only about 29,000 valid • 50% “cannot find specified file” • 25% “cannot connect to server” • 10% “authorization failure” • Can be from playlist • But 97% only 1 clip

  19. Live versus Pre-Recorded - Most pre-recorded - 98% is pre-recorded, 2% live

  20. Percentage of Audio and Video - More RealAudio than MP3 Audio - Proportionally less WSM is audio - Almost no QuickTime is audio

  21. Duration - 1997, 90% only 45 seconds or less - Still, today much shorter than T.V. show or movie

  22. Self-Similar Analysis (1 of 2) Definitive test: Is tail flat? Looks flat, but that is not good enough [31]

  23. Self-Similar Analysis (2 of 2) • Measure curve of tail (1/16th of distro, others same) • Curve defined as 3 point estimate, take derivative • Estimate Pareto (long-tailed) slope  • Used aest tool • Generate 1000 samples from Pareto with  • Each sample has same number of points as n • Calculate curvature of sample tail, mean  • Calculate difference (d) between  and original • Count number out of 1000 differ by d • 495 (video) and 498 (audio), about ½ • Cannot reject null-hypothesis  May be long-tailed

  24. Outline • Introduction (done) • Methodology (done) • Analysis • Aggregate analysis • Commercial products • Video • Audio • Codec • Sampling Issues • Conclusions

  25. Video Encoded Bitrate In 1997, 1% stream for modem, 50% for broadband, 20% for T1+ - Said, modem could not support streaming Note, today, broadband still not targeted

  26. Streams Encoded Per Clip Audio is one stream Media Scaling will be difficult! Note, earlier study [15] found real at 65%

  27. Aspect Ratios Very uniform, but a few odd-balls 30% above or below Take product for size (next)

  28. Video Resolution - Most much smaller than typical monitors (1024 x 768 would be 786,432) - Room to grow!

  29. Outline • Introduction (done) • Methodology (done) • Analysis • Aggregate analysis • Commercial products • Video • Audio • Codec • Sampling Issues • Conclusions

  30. Audio Encoded Bitrates - Most for modems, but 10% for broadband - In 1999, 100% found for modems - Will likely increase (MP3 128 kbps), but cap

  31. Video Codecs v8 buffers differently than v9 - Newest versions, v9, still not deployed much - Useful as snapshot in time

  32. Outline • Introduction (done) • Methodology (done) • Analysis (done) • Sampling Issues • Conclusions

  33. Sampling Issues • In 1997, could analyze all on Web • Today, impractical • Would take 16 years to crawl and analyze clips • Is 17 million large “enough” sample? • Is is possible to obtain same results with fewer starting points? • Is it possible to obtain same results with fewer than 1 million URLs per starting point? • How does sampling affect distributions? • How does choice of starting point affect distribution?

  34. Percentage of Media versus URLs Took 200k from each, build set Overall, above 400k from each is stable  ½ million

  35. Duration of Video for Number of URLs Can get away with far fewer and have same distribution of durations

  36. Media Type versus Starting Points 9 Starting points sufficient

  37. Duration for Number of Starting Points

  38. Media Type in USA versus International - International similar - May be because cross-cultural Web

  39. Duration for USA and Non-USA

  40. Summary • Many researchers worry about volume increase of Video • Video characteristics made based on old data • Current data on media stored on Web • Crawled 17 million URLs, analyzed 30k clips

  41. Conclusions • Streaming media increased 600% in past 5 years • Real Media 1st, Microsoft Media 2nd • Audio and video about equal • Vast majority pre-recorded (not live) • Most targets still for modem • Potential to be large since monitor resolutions much larger than video

  42. Future Work?

  43. Future Work • Correlate to actual data streamed • Congestion responsiveness • P2P • Future study (now ~1.5 years old!)

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