1 Measurements, Analysis, and Modeling of BitTorrent-like Systems Lei Guo, Ph.D. Candidate Park Graduate Research Award Presentation

2 Internet: the Galaxy of Information Systems Huge amount of information produced everyday Numerous computer and communication systems on the Internet –Web servers and proxies –Streaming media systems –Peer-to-peer systems –Content delivery networks, … How the Internet traffic and systems interact and evolve? What are the Kepler's laws for the Internet?

3 My Research: Two “Solar Systems” in the Internet Galaxy Internet multimedia systems –QoS: jitter, startup latency, traffic reduction, … –Infrastructure: proxies, CDNs/MDNs, … –New delivery technologies: MBR encoding, rate adaptation, fast streaming … Peer-to-peer systems –Infrastructure and protocols: structured/unstructured –Content search and file sharing –P2P based VoIP –BitTorrent systems

4 BitTorrent Peer-to-Peer Systems Measurement Analysis Modeling

5 Basic Model of P2P File Sharing 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

6 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

8 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

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

10 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

11 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

12 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

14 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

15 Meta File Downloading The first HTTP packets of.torrent file downloading –Cable network: 3,000+ users download 1,000+ different torrent meta files –Server farm: 50 tracker sites host hundreds of torrents 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

16 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

17 Outline BitTorrent mechanism and trace collection 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

18 Peer Arrival over Time server farm workloadcable network workload raw data linear fit # of.torrent file downloading (CCDF) decrease with time exponentially raw data linear fit time after torrent birth (day): t = peer arrival time – torrent birth time

raw data linear fit Torrent Popularity: Peer Arrival Rate relative deviation (%) tracker statistics workload # of peer requests (CCDF) time after torrent birth (day) torrents ranked by population (non ascending order) 6% in average fitting deviation of individual torrents derivative of CCDF peer arrival rate

20 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

21 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

22 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)

23 Downloading Failure Ratio Average downloading failure ratio: about 10% Different evolution patterns –Small population  large R fail Altruistic peers make torrents long live download failure population downloading failure ratio torrent population torrents ranked in non-ascending order of downloading failure ratio

24 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

25 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

26 Performance Variation: Evolution model trace time (hour) avg download speed (byte/sec)  10 4 Only stable when population is large Fluctuate significantly after peak time Cannot be smoothed in scale of typical downloading time  time (hour) avg download speed (byte/sec) instant speed avg speed (over typical download time)

27 Performance Variation: Distribution Larger torrents have higher and more table performance time (day)  10 3 downloader seed download speed Avg performance of all torrents over time avg download speed (byte/sec) torrents 10 1  downloader seed download speed Snapshot of torrents at time t # of peers Average downloading speed over all torrents is much more stable

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

29 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

30 Outline BitTorrent mechanism and trace collection 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

31 Modeling Multi-torrent System Considering peers and torrents on the Internet as an open system –Torrent birth and death –Peer birth and death –Participation of multiple torrents The lifecycle of a BitTorrent peer –Downloading, seeding, sleeping, … A huge number of torrents on the Internet –independent in current BT model, but –inherently related by peers joining multiple torrents Our model assumption –Assume each peer joins one torrent at a time, and at most once Current BT encourage a peer to exchange chunks with peers in the same torrent

Dynamics in Multi-torrent Environment Torrent birthRequest arrivalPeer birth Torrent birth time, request arrival time, and peer birth time (hour) CDF of torrents CDF of requests CDF of peers Torrent birth rate t : per hour Torrent request rate q : per hour (for all peers over all torrents) Peer birth rate p : per hour Implication: raw data linear fit raw data linear fit raw data asymptotic fit avg # of requested torrents by a peer torrent request rate peer birth rate = = constant

33 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: Poisson-like, with constant avg request rate Request new a torrent with a probability p: participation probability –x– # torrents +  r  r (day) # of torrents peers

34 Peer Request Pattern: Participation Probability peer rank ( log i ) number of torrents ( m ) trace collection duration born beforerequest after ? peers request at least m torrents peer born

35 trace collection duration born beforerequest after Peer Request Pattern: Participation Probability ––– raw data ––– linear fit number of torrents ( m ) peer rank ( log i ) p = peers request at least m torrents peer born

36 Peer Request Pattern: Participation Probability Another estimation of p trace collection duration born beforerequest after peer born ––– raw data ––– linear fit number of torrents ( m ) peer rank ( log i ) p = peers request at least m torrents Probability model confirmed

37 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

38 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

39 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

40 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

41 Outline BitTorrent mechanism and trace collection 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

42 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 A C B D Neighbor-in Neighbor-out BCBC D torrents that can serve me torrents that I can serve (peer list)

43 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

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

45 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

46 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

47 Acknowledgements Dr. Xiaodong Zhang, my advisor Mr. Joe Anselmo, College of William and Mary Dr. William Bynum, College of William and Mary Dr. Songqing Chen, George Mason University Mr. Xiaoning Ding, College of William and Mary Dr. Song Jiang, Los Alamos National Lab Dr. Teresa Long, College of William and Mary Mr. Shansi Ren, College of William and Mary Mr. Enhua Tan, College of William and Mary Dr. Li Xiao, Michigan State University Dr. Zhen Xiao, AT & T Labs – Research

48 Selected Publications Measurements, Analysis, and Modeling of BitTorrent-like Systems, IMC 2005 Analysis of Multimedia Workloads with Implications for Internet Streaming, WWW 2005 Fast and Low Cost Search Schemes by Exploiting Localities in P2P Networks, JPDC 2005 DISC: Dynamic Interleaved Segment Caching for Interactive Streaming, ICDCS 2005 Exploiting Content Localities for Efficient Search in P2P Systems, DISC 2004 PROP: a Scalable and Reliable P2P Assisted Proxy Streaming System, ICDCS 2004

49 Thank you!