1. 1 Measuring P2P IPTV Systems Thomas Silverston, Olivier Fourmaux Universit ´e Pierre et Marie Curie - Paris 6 ACM NOSSDAV 2007 17th International workshop on Network and Operating Systems Support for Digital Audio & Video

2. 2 Outlines Introduction Experiment Setup Measurement Analysis  General Observation  Traffic Pattern  Video Download Policy  Peers Neighborhood  Video Peers Lifetime Conclusion

3. 3 Introduction P2P : ~70% overall Internet traffic P2P applications  File sharing : BitTorrent, Kazaa, eDonkey, etc.  P2P streaming IPTV(Live streaming) : PPStream, PPLive, CoolStreaming, etc. Video on Demand(VoD) : Youtube, MSN Video, Dailymotion, etc. P2P measurement studies  File sharing: BitTorrent, Kazaa, eDonkey [Bharambe Infocom06], [Legout IMC06], [Liang Comp. Net.06]  VOIP: Skype, Google talk [Baset Infocom06], [Suh Infocom06], [Barbosa Nossdav07], [Bonfiglio Sigcomm07] No comprehensive study about P2P streaming  Lots of academic P2P streaming protocols not really deployed on the Internet Anysee[Infocom06], Chunkspread[ICNP06], Prime[Infocom07], etc.  Commercial P2P streaming really deployed on the Internet PPLive, PPStream, SOPCast and TVAnts Proprietary softwate No design/implementation information, patented.

4. 4 Introduction(Cont.) Need for P2P streaming measurements  P2P IPTV: massively used in the future  How do P2P streaming applications really works? Traffic analysis is the only feasible to identify the mechanisms Link between academic and commercial Input for model(simulation) P2P video live streaming applications  P2P IPTV  PPLive, PPStream, SOPCast and TVAnts  Features Data are divided into chunks Each peer exchanges with other peers information about the chunks Making comparisons between 4 different applications  Highlighting design similarities and differences  Point out global behavior Packet Traces  Two soccer games in 2006 FIFA World Cup on June 30, 2006 Large-scale event Live interest for users Real conditions

5. 5 Experiment Setup Two soccer games were scheduled on June 30, 2006  They are well representative of all of them With different applications at the same time Four packet traces  The first game(Germany vs. Argentine, in the afternoon) : PPStream, SOPCast  The second game(Italy vs. Ukraine, in the evening) : PPLive, TVAnts Measurement experiment platform  Common PCs with 1.8GHz CPU  100Mbps Ethernet access (campus network environment)  tcpdump for Unix, ethereal for Windows XP http://www.ethereal.com/http://www.ethereal.com/

6. 6 Measurement Analysis Packet traces summary

7. 7 Measurement Analysis(Cont.) Packet traces summary PPStream relies only on TCP. Major part of PPLive traffic relies on TCP. SOPCast traffic relies mostly on UDP. TVAnts is more balanced between TCP and UDP.

8. 8 Measurement Analysis TVants Fluctuating largely Quiet constant Total download and upload throughput for TVAnts.

9. 9 Measurement Analysis PPLive

10. 10 Measurement Analysis PPStream

11. 11 Measurement Analysis SOPcast Received no traffic, but PPStream was working well.

12. 12 Traffic Pattern Application features  Exchanging information about data chunks and neighbor peers (Swarming mechanism)  Discovering other peers iteratively  Establishing new signaling or video sessions Session duration  Video sessions are likely to have long duration  Signaling sessions are likely to be shorter in time Packet size  Video streaming packet size is expected to be large  Signaling session packet size is suppose to be common Average packet size according to peers session duration.

13. 13 Traffic Pattern Signaling sessions Video sessions Signaling sessions They are not clearly formed. Signaling sessions Video sessions A balanced use of TCP and UDP

14. 14 Traffic Pattern Observations summary for traffic patterns Signaling overhead  Separating video and signaling traffic with an heuristic [6] If a session had at least 10 large packets, then it was labeled as a video session Same IP addresses and ports >= 1000 Bytes [6] X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross, “Insights into pplive: A measurement study of a large-scale p2p iptv system,” in Proc. of IPTV Workshop, 2006.

15. 15 Video Download Policy(VDP) The problem did not occur for network reasons A major part of the download traffic Almost all the traffic during its session duration Do not contribute to a large part of the download traffic Neither the top peer About half the total download traffic All the top ten peers traffic during its session duration About half the total download traffic (like SOPCast) Not a large amount of the total traffic (like PPStream) Total traffic, top ten peers traffic and top peer traffic

16. 16 Video Download Policy(VDP) PPLive  Getting the video from only a few peers at the same time  Switching periodically from a peer to another one PPStream  Getting the data from many peers at the same time  Its peers have long session duration SOPCast  Download policy looks like PPLive policy  Need more than a peer to get the video compare to PPLive TVAnts  Mix PPStream and SOPCast policies Summary  The presented applications implement different download policies  Do not expect peers to have the same capabilities Session duration Short Long PPLive, SOPcast PPStream Peers capacities Low Huge PPStream, TVAnts PPLive The number of VDP at the same time A few Many PPLive SOPCast TVAnts PPStream

17. 17 Peers Neighborhood Low and constant High and constant High and fluctuates largely High and fluctuates Using an important part of UDP traffic

18. 18 Video Peers Lifetime The video peer lifetime  The duration between the first time and the last time our controlled nodes exchanging video traffic with another peer. End-hosts, similar to the tracker in BT, are responsible to duplicate flows to each other  End-hosts can join and leave the network whenever they want and are prone to suffer failures.  The systems have to deal with the arrivals and departures of peers (churn of peer).  A high churn of peers will involve additional delays or jitter variations for packet delivery, which will decrease overall video quality.

19. 19 Video Peers Lifetime Video peers lifetime for TVAnts  All the applications have the same Weibull-like distribution for peers lifetime  The video peers lifetime CCDF follows a Weibull distribution  Complementary Cumulative Distribution Function (CCDF) For all applications, there are no more than 10 % of peers, which stay in the network during an entire game (5400s). 5000s TVants. Average lifetime = 2778s

20. 20 Video Peers Lifetime For all the applications, no more than 10% of peers stay in the network during the entire game(5400s=1.5hr).

21. 21 Video Peers Lifetime

22. 22 Conclusion We explored the behavior of 4 popular P2P IPTV systems by measuring and analyzing their network traffic Our analyses show that the measured applications generate different traffic patterns and use different mechanisms to get the video This knowledge will be used in our other works to model and simulate these systems

23. 23 Reference [6] X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross, “Insights into pplive: A measurement study of a large-scale p2p iptv system,” in Proc. of IPTV Workshop, 2006.  X. Hei, C. Liang, J. Liang, Y. Liu, K.W. Ross, “A Measurement Study of a Large-Scale P2P IPTV System,” IEEE Transactions on Multimedia,vVol.9, No.8, pp.1672-1687, Dec. 2007.(Journal version) [7] K. Sripanidkulchai, A. Ganjam, B. Maggs, and H. Zhang, “The feasibility of supporting large-scale live streaming applications with dynamic application end-points,” in Proc. of SIGCOM, 2004. [8] X. Zhang, J. Liu, and B. Li, “On large-scale peer-to-peer live video distribution: Coolstreaming and its preliminary experimental results,” in Proc. MMSP, 2005. [10] T. Silverston and O. Fourmaux, “P2p iptv measurement: A comparison study,” http://www.arxiv.org/abs/cs.NI/0610133, 2006. Eugenio Alessandria, Massimo Gallo, Emilio Leonardi, Marco Mellia, Michela Meo, “P2P-TV Systems under Adverse Network Conditions: a Measurement Study,” IEEE INFOCOM 2009.