1 Measurements, Analysis, and Modeling of BitTorrent-like Systems Lei Guo 1, Songqing Chen 2, Zhen Xiao 3, Enhua Tan 1, Xiaoning Ding 1, and Xiaodong Zhang 1 1 College of William and Mary 2 George Mason University, 3 AT & T Labs - Research

2 Basic Model of P2P Systems Peers sharing different files self- organize into a P2P network Exchange files they desire Limitations –Free riding –Large file downloading ♫ ♫ Examples: Gnutella, KaZaa, eDonkey/eMule/Overnet

3 BitTorrent: Fast Delivery with Incentive A large file is divided into chunks Peers interested in the same file self-organize into a torrent Peers exchange file chunks with each other Incentive is established by tit for tat Very simple and effective, scale fairly well during flash crowd 45 Torrent of Bits...

BitTorrent Traffic Online users –6.8 million in August 2004, 9.6 million in August 2005 (BigChampagne) Traffic volume –53% of all P2P traffic on the Internet in June 2004 (CacheLogic) P2P traffic: 60-80% Other traffic: 20-30% Source: CacheLogic, 2004

5 Limited Understanding of BitTorrent Existing studies on BitTorrent systems (INFOCOM04, SIGCOMM04) –Unrealistic assumptions in system model: no evolution considered –Single-torrent based: more than 85% BT users join multiple torrents What we are not clear about BitTorrent systems –Service availability –Service stability –Service fairness Our objective of this work –Evolution of single-torrent system, and limitations of BT –Multi-torrent model for inter-torrent relation and collaboration during the entire lifetime

6 Outline BitTorrent mechanism and our methodology Modeling and characterization of single-torrent system Modeling and characterization of multi-torrent system Inter-torrent collaboration Conclusion

7 How BitTorrent Works: Publishing The publisher –Create a meta file –Publish on a Web site –Start the tracker site –Start a BT client as the initial seed Tracker site Web site foo.torrent I am here! peer list foo.torrent announce: tracker URL for bootstrap creation date: epoch time of file creation length: file size name: file name piece length: chunk size pieces: SHA1 hash key of each chunk seed

8 How BitTorrent Works: Downloading The downloader –Download the meta file –Start a BT client, connect to the tracker site –Get peer list from tracker –Get first chunk from other peers (seeds) Tracker site Web site foo.torrent peer list download I am here! peer list seed

9 How BitTorrent Works: Downloading The downloader –Download the meta file –Start a BT client, connect to the tracker site –Get peer list from tracker –Get first chunk from other peers (seeds) –Exchange file chunk with other peers –Download complete: become a new seed Tracker site Web site foo.torrent peer list seed foo.torrent

How BitTorrent Works: Downloading The downloader –Download the meta file –Start a BT client, connect to the tracker site –Get peer list from tracker –Get first chunk from other peers (seeds) –Exchange file chunk with other peers –Download complete: become a new seed –Initial seed leaves Tracker site Web site foo.torrent peer list seed foo.torrent seed Future performance Depends on the arrival and departure of new downloaders and seeds

11 Our Methodology of this Study Measurement –BitTorrent traffic pattern –Meta file downloading and tracker statistics Analysis –BitTorrent user behavior and performance limitations –Curve fitting, parameter estimation and validation of mathematical models Modeling –Torrent evolution and inter-torrent relation –Fluid model, probability model, and graph model

12 Meta File Downloading The first HTTP packets of.torrent file downloading –Cable network: 3,000+ downloads, 1,000+ torrent meta files –Server farm: 50 tracker sites host hundreds of torrents –Gigasope: fast Internet traffic monitoring tool by AT&T What information it contains? –Torrent birth time –Peer arrival time to the torrent (packet capture time of downloading) –About 10 days announce: tracker URL creation date: epoch time of file creation length: file size name: file name piece length: chunk size pieces: SHA1 hash key of each chunk foo.torrent

13 Torrent Statistics on Trackers Professional/dedicated tracker sites –Each may host thousands of torrents at the same time – and collected by University of Massachusetts, Amhersthttp:// –Ex: alluvion -- 1,500 torrents, 550 are fully traced What information it contains? –Torrents: torrent birth time, file size, number of peers/seeds –Peers: request time, downloading/uploading bytes, downloading/uploading bandwidth –Sampled every 0.5 hour for 48 days

14 Outline BitTorrent mechanism and our methodology Modeling and characterization of single-torrent system –The evolution of torrent over time –Limitations of current BitTorrent systems Modeling and characterization of multi-torrent system Inter-torrent collaboration Conclusion

15 Torrent Popularity Peer arrivals: decrease with time exponentially relative deviation (%) individual torrents torrents ranked by population (non ascending order) derivative of CCDF Peer arrival rate meta file workload CCDF of peer arrival raw data linear fit tracker site workload raw data linear fit time after torrent birth (day) 6% in average

16 t Torrent Death peer arrival rate: seed leaving rate: inter-arrival time: seed service time: Peer n arrives at time t n : downloading rate: downloading time: When t n  , what will happen? tntn t n+1 peer npeer n+1 torrent dead inter-arrival time > seed service time

17 trace model torrent lifespan (hour) torrents Torrent Population and Lifespan trace model torrent population rank of torrents Most torrents are small (avg 102) Most torrents are short live (avg 8 days)

18 Define: Avg downloading failure ratio –about 10% Different evolution patterns Small population  large R fail –Reminder: most torrents have small population! Altruistic peers make torrents long live Downloading Failure Ratio download failure population downloading failure ratio torrent population torrents ranked in non-ascending order of downloading failure ratio

19 Torrent Evolution: Fluid Model Existing model (SIGCOMM 04) –Constant arrival rate = const –Torrent reaches equilibrium The correct model –Exponentially decreasing arrival rate –Torrent dead finally –Verified by our measurements Two completely different pictures

20 Torrent Evolution: Modeling Results Flash crowd –Downloader #: exponentially  –Seed #: exponentially  Peek time –A very short duration –Constant arrival model: flat peak Attenuation – a long tail –Downloader #: exponentially  –Seed #: exponentially  –Constant arrival model is far from the reality: no attenuation Torrent death trace model trace model time (hour) # of seeds # of downloaders constant arrival model

21 Performance Stability model trace time (hour) avg download speed (byte/sec)  torrents 10 1  downloader seed download speed Snapshot of torrents at time t # of peers Evolution over time Only stable when torrent is large Fluctuate significantly after peak time Larger torrents have higher and more table performance avg download speed (byte/sec)

peer contribution ratio # of torrents + contribution ratio –x– # of torrents ranked peers Service Unfairness Unfairness:  download speed,  uploading contribution Seeds serve high speed downloaders first –Peers not willing to serve after downloading –Not due to new file downloading: selfish ranked peers download speed (byteps) peer contribution ratio + contribution ratio –x– download speed Contribution ratio: uploaded bytes downloaded bytes

23 Single-torrent Model: Summary Torrent evolution over time –Exponentially decreasing arrival rate –Flash crowd – short peak – long tailed attenuation BitTorrent Limitations –Content availability: torrent death –Performance stability –Service fairness

24 Outline BitTorrent mechanism and our methodology Modeling and characterization of single-torrent system Modeling and characterization of multi-torrent system –Traffic pattern and user behavior –Graph based model of inter-torrent relation Inter-torrent collaboration Conclusion

Multi-torrent Environment Dynamics Torrent birthRequest arrivalPeer birth Torrent birth time, request arrival time, and peer birth time (hour) CDF of torrents CDF of requests CDF of peers Considering peers and torrents on the Internet as an open system –Torrent birth rate, torrent request rate, and peer birth rate are constant Implication: The lifecycle of a BT peer: downloading, seeding, sleeping, …, dead raw data linear fit raw data linear fit raw data asymptotic fit avg # of torrents a peer requests torrent request rate peer birth rate = = constant

26 Peer Request Pattern: Request Rate Peer request rate: requests by a peer to different torrents per unit time  r  77 years ! Assume Peer request process: seems Poisson-like Request a new torrent with a probability p: participation probability Dead with probability 1-p –x– # torrents +  r  r (day) # of torrents peers

27 Peer Request Pattern: Participation Probability peers request at least m torrents ––– raw data ––– linear fit number of torrents ( m ) peer rank ( log i ) p = Another estimation of p Probability model Probability model confirmed

28 Inter-torrent Relation Graph: How Torrents Can Help with Each Other? ji ji 1 some peers in torrent i have downloaded j 2 some peers in torrent j have downloaded i

29 Inter-torrent Relation Graph: How Torrents Can Help with Each Other? Edge weight W i,j : number of such peers ji ji 1 some peers in torrent i have downloaded j 2 some peers in torrent j have downloaded i torrents weighted out-degree weighted in-degree torrent size (# of online peers) trace model trace model torr size torrents

30 Single-torrent vs. Multi-torrent Model Single-torrent model –  seed service time,  download failure rate –Limited seed service time , but inter-arrival time  exponentially –Small improvement Multi-torrent model –Old peers come back multiple times –  peer arrival rate,  peer inter-arrival time –Significant improvement

31 Single-torrent vs. Multi-torrent Model Multi-torrent modelSingle-torrent model Inter-torrent collaboration is much more effective than stimulating seeds to serve longer  ≈ 0 seeds stay 10 times longer:  * =  /10 torrent death ' (T' life ) =  0.1

32 Outline BitTorrent mechanism and our methodology Modeling and characterization of single-torrent system Modeling and characterization of multi-torrent system Inter-torrent collaboration –Tracker site overlay –Instant incentive for collaboration Conclusion

33 Tracker Site Overlay Self-organized P2P network (a logical structure) An instance of inter-torrent relation graph A built-in mechanism for content search, cover 99%+ torrents Trackerless BitTorrent: uses DHT to store meta file A C B D Neighbor-in Neighbor-out BCBC D torrents that can serve me torrents that I can serve (peer list)

34 Jack file A file D Incentive for Inter-Torrent Collaboration C B D Instant incentive – similar to “tit-for-tat” principle Neighboring cycle detection Neighboring cycle construction –Bandwidth trading: get one chunk, serve multiple peers A Thanks Jack! Tom

35 Conclusion Extensive analysis and modeling to study the behaviors of BT-like systems –Tracker trace and.torrent downloading trace –Mathematical model BitTorrent system has its limitations due to exponentially decreasing peer arrival rate –Service availability, performance stability, and fairness Graph based multi-torrent model System design for inter-torrent collaboration

36 Thank you!

37 Backup for Questions

38 Torrent Lifespan Extract  t and t from trace Get 0 and  using linear regression Lifespan model verified by measurement trace model torrent lifespan (hour) torrents torrent lifespan

39 Model verified by measurement Observations: –The population of most torrents are small (102 in average) –Downloading failure ratio –Small population  large R fail Torrent Population Total population trace model torrent population rank of torrents (in non-ascending order of modeling results)

40 Torrent Evolution: Fluid Model Parameters Basic equation set Resolution x(t)x(t)number of downloaders y(t)y(t)number of seeds 0 initial peer arrival rate  attenuation parameter of  uploading bandwidth c downloading bandwidth (c >>  )  seed leaving rate  file sharing efficiency 1,21,2 eigen values of the equation set a,b,c1,c2,d1,d2a,b,c1,c2,d1,d2 constants

41 Peer Request Pattern: Summary Multi-torrent environment: an open model –Torrent birth rate: per hour (nearly a constant) –Peer birth rate: per hour (nearly a constant) –Torrent request rate (for all peers over all torrents): per hour (nearly a constant) Actually increase slowly according to BigChampagne Peer request pattern –Lifecycle: downloading, seeding, sleeping, …, next req with prob. p –Peer participation probability: 0.85 –Request rate (for different torrents by a peer): Poission-like

42 Tracker Site Overlay Table size Node degree distribution –Similar to unstructured P2P networks Many content search and msg routing algorithms –Flooding –Random walk –… Trackerless BitTorrent: uses DHT to store meta file

43 Simulation Experiments performance stability content availability service fairness without inter-collaboration with inter-collaboration R fail  0 more balancedmore stable Inter-torrent collaboration can improve BitTorrent performance downloading speeddownloading failure ratiocontribution ratio