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

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. VoW Setup Video server cdt.luth.se campus.luth.se sm.luth.se luth.se others 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/(p 1-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

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

20. 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

21. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Trace Stack Previous Stack Position Counter 123123 000000 Position Counter (increment previous location of currently referenced document)

22. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie1 123123 000000 Trace Stack Position Counter Previous Stack Position Counter (increment previous location of currently referenced document)

23. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie2 Movie1 123123 000000 Trace Stack Position Counter Previous Stack Position Counter (increment previous location of currently referenced document)

24. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie2 Movie1 123123 100100 Trace Stack Position Counter Previous Stack Position Counter (increment previous location of currently referenced document)

25. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie2 Movie1 123123 200200 Trace Stack Position Counter Previous Stack Position Counter (increment previous location of currently referenced document)

26. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie3 Movie2 Movie1 123123 200200 Trace Stack Position Counter Previous Stack Position Counter (increment previous location of currently referenced document)

27. Temporal Locality LRU stack analysis GET Movie1 GET Movie2 GET Movie3 GET Movie1 : Movie1 Movie3 Movie2 123123 201201 Trace Stack Position Counter Plot this after running through the entire trace Previous Stack Position Counter

28. Temporal Locality: Result

29. 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

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