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Characterizing User Access To Videos On The World Wide Web

Characterizing User Access To Videos On The World Wide Web. Soam Acharya Inktomi Corporation Foster City, CA. Peter Parnes Center For Distance Spanning Technology Luleå University of Technology Sweden. Brian Smith Department of Computer Science Cornell University Ithaca, NY. MMCN 2000.

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Characterizing User Access To Videos On The World Wide Web

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  1. Characterizing User Access To Videos On The World Wide Web Soam Acharya Inktomi Corporation Foster City, CA Peter Parnes Center For Distance Spanning Technology Luleå University of Technology Sweden Brian Smith Department of Computer Science Cornell University Ithaca, NY MMCN 2000

  2. Overview • Analysis of traces from an ongoing VoW trial (VoD over the Web) • 2 year period • 13100 requests • 246 titles

  3. Why? • Audio/Video content: • coming online rapidly • constitute a large percentage (17%) of bytes transferred online • Useful to: • Cache Designers • Codec Engineers • Network Engineers • Other Multimedia Researchers: • MM Storage Systems

  4. Questions We Asked • Do accesses to videos exhibit temporal locality? • How frequently are videos accessed? • Do users exhibit specific browsing patterns when viewing videos? • What are the file size trends?

  5. Roadmap • VoW Setup • Analysis Methodology • Results • Conclusion • Future Work

  6. others luth.se Videoserver sm.luth.se campus.luth.se cdt.luth.se VoW Setup • Lulea University, Sweden • Center for Distance Spanning Technology • High speed network (34 Mbps) • mMOD software system

  7. VoW Setup II • Two years (end of Aug ‘97 - mid Oct ‘99) • 246 video titles • encoded using H.261 (CIF - 320x240) • ~ 500 campus machines involved in access, ~1400 outside • title categories • general • movies • educational • courses • tutorials, seminars

  8. Analysis • Video file characteristics • size • duration • bitrate distribution • Trace access analysis • Trace refinement • Actual analysis on refined data

  9. Median Movie Size: 96 MBytes

  10. Median Duration ~ 70 minutes

  11. Quality of video streams deliberately kept low (for external users) • Compression scheme designed to produce lower bitrates

  12. Trace Access Analysis - Log Filtering • Initially eliminate from the trace: • HTML documents • Java applet requests • images • Joining a session already in progress 02:01:33 salt.cdt.luth.se GET Movie1 02:03:23 spock.cdt.luth.se GET TVSerial_970206 03:04:12 aniara.cdt.luth.se GET Movie2 03:10:11 aniara.cdt.luth.se STOP Movie2

  13. Log Filtering II • Eliminate from trace: • requests from demo machines • resolve IP addresses for machine names • reduce user errors • hitting STOP button too many times • hitting GET requests too many times • Removed 1160 requests, 11965 remaining

  14. Trace Analysis Methodology • General: • How do video requests vary by day? • Mathematical distributions? • Do some machines request more than others? • Pattern Detection: • Inter-access times • Do users access videos all the way? • Type of file • Temporal locality

  15. 11965 accesses over twenty five months

  16. Movie Popularity Movie popularity did not follow Zipf’s law -- P ~ 1/(p1-t ) P = freq. of access to a document, p = its rank in popularity

  17. Distribution of Requests By Machine • About 73% of all requests from campus and surrounding community • For requests from within campus: • 2% of all machines (11) => 21% of requests • 10% of machines (53) => 50% of requests • Lab machines

  18. Partial Access • 61% of accesses went to completion • 39% stopped early • Suggests browsing pattern

  19. File Category Variations • Access patterns vary by file category • Lectures have temporal locality of access • Many accesses shortly after going online • Entertainment videos do not

  20. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 0 0 0 (increment previous location of currently referenced document)

  21. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie1 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 0 0 0 (increment previous location of currently referenced document)

  22. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie2 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 0 0 0 Movie1 (increment previous location of currently referenced document)

  23. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie2 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 1 0 0 Movie1 (increment previous location of currently referenced document)

  24. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie2 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 2 0 0 Movie1 (increment previous location of currently referenced document)

  25. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie3 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 2 0 0 Movie2 Movie1 (increment previous location of currently referenced document)

  26. Temporal Locality • LRU stack analysis Previous Stack Position Counter Stack Trace Position Counter Movie1 GET Movie1 GET Movie2 GET Movie2 GET Movie2 GET Movie3 GET Movie1 : : 1 2 3 2 0 1 Movie3 Movie2 Plot this after running through the entire trace

  27. Temporal Locality: Result

  28. Conclusion • Videos are relatively large (to capture entire lectures, movies) • Users browse portions of video • A small number of machines accounted for a large number of accesses • High temporal locality of trace accesses

  29. Future Work • Further analysis on inter-access patterns • Repeat analysis on traces from other VoW type experiments, cache traces ...

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