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Where in the World is Carmen BitDiego? And who is she, anyways…

Where in the World is Carmen BitDiego? And who is she, anyways…. Alexandru IOSUP A.Iosup@ewi.t u delft.nl. The 12th annual ASCI Computing Workshop. Introduction (1 of 3). Peer-2-Peer File sharing Everybody has the same rights. P2P average everybody ? Who? Where? When? How? Why?

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Where in the World is Carmen BitDiego? And who is she, anyways…

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  1. Where in the World is Carmen BitDiego? And who is she, anyways… Alexandru IOSUP A.Iosup@ewi.tudelft.nl The 12th annual ASCI Computing Workshop

  2. Introduction (1 of 3) • Peer-2-Peer • File sharing • Everybody has the same rights. • P2P average everybody? • Who? • Where? • When? • How? • Why? • Tons of studies over the past 5 years • Saroiu’02, Yazti’02, Yzal’04, Pouwelse’04 • We go for something else! (tbs)

  3. Introduction (2 of 3) • BitTorrent • Most used P2P network today (53% traffic) • Attributes • 2nd gen. P2P network – no centralized servers; optimizes transfer speed; favors high-bandwidth users; files are split in chunks • Peers – Trackers – Web sites • Tit-for-tat sharing mechanism – everybody gives some; except when they don’t… • no search at peer level • Owners are called seeds, we are called leeches • So much to know:I want my BitTorrent today!

  4. Enters Carmen… • Carmen BitDiego • Famous P2P network • Location: unknown • Likes: who knows? • Clues to where she is: some history, lightweight hints • Caught (?) • NO Multi-files studies • NO Country-per-file • NO Organizations • NO, NO, NO… • Carmen SanDiego • Famous spy • Location: unknown • Likes: to hide • Clues to where she is: history, complicated hints • Never caught Who is this Carmen, anyways…

  5. Introduction (3 of 3) • We track Carmen BitDiego • Tracked data attributes • Users got 204,454,719,497,935B (ok, 204,5TB) • 40,000,000 contacts • 200,000 unique users (*) • 120 files • 9 specific media types • The firstaliased media view • 7 unique views • We got her now! Or is it…

  6. Mission statement • We want to know about Carmen BitDiego • Where she goes • Continent, country, city, organization • When she goes • Time-patterns per country • Time-patterns in seeds/leeches ratio • How many file chunks at any time? • With whom she hangs out • Special users? Super-peers, collector peers • Is she a good companion? • How many users get what they want? We’re getting to this info in no time…

  7. Outline of the presentation • Intro • Enters Carmen… • Mission statement • Our data looks like this… • Methods, or how to catch her • Results, or how we caught her • Conclusions (done) (done) (done) (we are here) (coming up next)

  8. Our data looks like this… • We track 120 files • 120 trace files • Time stamp, IP, port, # of chunks = record = 1observation • 12 big traces (+500,000 observations/trace) • December 2003 – January 2004 • 108 small traces • March 2004 • 3 global categories • All, Big, Small • 9 special categories • Movies, Games, Music, Applications • Alias media • Same contents, different names • Same language • Different language

  9. Outline of the presentation • Intro • Enters Carmen… • Mission statement • Our data looks like this… • Methods, or how to catch her • Results, or how we caught her • Conclusions (done) (done) (done) (done) (we are here) (coming up next)

  10. Methods, or how to catch her • We want to know about Carmen BitDiego • Where she goes • Un-DNS(*): continent (1), country (2), city (3), organization (4) • When she goes (5) • Parse and correlate Time-patterns per country • Parse and correlate Time-patterns in seeds/leeches ratio • Parse and correlate How many file chunks at any time? • With whom she hangs out (6) • Super-peers = nodes that own more than one complete file • Collector peers = nodes that try to get more than one file • Is she a good companion? (7) • How many users get what they want? * Thanks MaxMind (GeoIP lib, database) and WebLog Expert (databases)

  11. Outline of the presentation • Intro • Enters Carmen… • Mission statement • Our data looks like this… • Methods, or how to catch her • Results, or how we caught her • Conclusions (done) (done) (done) (done) (done) (we are here) (coming up next) WARNING! We show only a selection of our results!

  12. Results, or how we caught her • Where she goes • continent Europe is now the biggest BitTorrent consumer (not NA) Tit-for-tat discourages low-bandwidth users!

  13. Results, or how we caught her • Where she goes • continent Not the same distribution for different sets of files! Europe is now the biggest BitTorrent consumer (not NA) Coarse media locality property Asia > North America (themed game) Tit-for-tat discourages low-bandwidth users!

  14. Results, or how we caught her • Where she goes • country US still the biggest overall BitTorrent consumer – continent view can be misleading! NL is only 6th!

  15. Results, or how we caught her Hong Kong, Chile: soccer management sim Israel: action movie Japan: animes • Where she goes • country Fine media locality!Countries have habits! Localized versions of the files attract local users! The Nederlands 6thRomania ~50th Themed files attract very specific audiences! What about a marketing study based on BitTorrent file ranks? Not the same distribution for different sets of files! US still the biggest overall BitTorrent consumer – continent view can be misleading!

  16. Results, or how we caught her • Where she goes • city Dispersed locations Oldenburg, Eschborn, Herndon …Internet nodes placed outside major cities – cannot use this to track real users! 30% unknown – not reliable!

  17. Results, or how we caught her • Where she goes • organization We’d like to thank:The Walt Disney Company,Sony Corporation, SANYO Electric Software Co. Ltd.,and Merrill Lynchfor actively supporting BitTorrent! Academic institutions < 10% users! Not the same distribution for different sets of files! 1 ISP covers +60% users 10 ISPs cover <50% users ISP caching policy different for different files and communities!

  18. Results, or how we caught her • When she goes • Time-patterns per country Europe guides the time-patterns! 8:30AM, 1PM, 6-9PM, 12-1AMmostly at work, during slow hours?

  19. Results, or how we caught her • When she goes • How many file chunks at any time? Causes:- trackers down- users interest down- others The network is not robust all the time – attacks at these precise moments could be fatal!

  20. Results, or how we caught her • When she goes • Time-patterns per no. of chunks/seeders/leeches ratio users:seeds ~ 10:1leeches:seeds ~ 9:1chunks:seeds ~ 1000:1

  21. Results, or how we caught her • With whom she hangs out • Super-peers = nodes that own more than one complete file • Collector peers = nodes that try to get more than one file Group Small:Collectors (n files) ~ 2x Superpeers (n files) # users / # files decreases exponentially!

  22. Results, or how we caught her 113 81 81 81 YES! 1 Point = 1% of any file • Is she a good companion? Aliased Media results in exponential drop!people drop after getting 1/many Group Small users download whole files! Group Small Avg. (any) ~ 81 points Avg. (1 file) ~ 113 pointsAliased Media Avg. (any) ~ 52 points Avg. (1 file) ~ 109 points Users download 1 file then disconnect!

  23. Outline of the presentation • Intro • Enters Carmen… • Mission statement • Our data looks like this… • Methods, or how to catch her • Results, or how we caught her • Conclusions (done) (done) (done) (done) (done) (done) (we are here)

  24. Conclusions • Carmen BitDiego • Famous P2P network • Location: known • Likes: established(study per specific file groups) • Clues to where she is: complete hints • Multi-files study • Continents, Country, Cities, Organizations, global and per-file • Time-patterns in the users/seeds/leeches behavior (also country) • Super-nodes / collector nodes analysis • Carmen BitDiego almost caught! • Trivial and Non-trivial locality properties • Alias media hints • Need a full study w/ these methods to catch her!

  25. Thank you… Questions? Remarks? Observations? All welcome! Alexandru IOSUP TU Delft A.Iosup@ewi.tudelft.nl http://www.pds.ewi.tudelft.nl/~iosup/index.html I would like to thank Johan Pouwelse and Pawel Garbacki for all their help in creating this study. Thank you, Johan! Thank you, Pawel! Their previous work:http://www.theregister.co.uk/2004/12/18/bittorrent_measurements_analysis/

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