Measuring P2P IPTV Systems

1. Measuring P2P IPTV Systems Thomas Silverston, Olivier Fourmaux Universit ´e Pierre et Marie Curie - Paris 6 ACM NOSSDAV 200717th International workshop on Network and Operating Systems Support for Digital Audio & Video

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

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. 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. 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/

6. Measurement Analysis • Packet traces summary

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

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

9. Measurement AnalysisPPLive

10. Measurement AnalysisPPStream

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

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

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 >= 1000 Bytes Same IP addresses and ports [6] X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross,“Insights into pplive: Ameasurement study of alarge-scale p2p iptv system,” in Proc. of IPTVWorkshop, 2006.

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

16. 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 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

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

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. 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). TVants. Average lifetime = 2778s 5000s

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. Video Peers Lifetime

22. Conclusion • We explored the behavior of 4 popular P2PIPTV systems by measuring and analyzing their networktraffic • Our analyses show that themeasured applications generate different traffic patterns anduse different mechanisms to get the video • This knowledge will be used in our other works to model and simulate these systems

23. Reference • [6] X. Hei, C. Liang, J. Liang, Y. Liu, and K. W. Ross,“Insights into pplive: A measurement study of alarge-scale p2p iptv system,” in Proc. of IPTVWorkshop, 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, andH. Zhang, “The feasibility of supporting large-scalelive streaming applications with dynamic applicationend-points,” in Proc. of SIGCOM, 2004. • [8] X. Zhang, J. Liu, and B. Li, “On large-scalepeer-to-peer live video distribution: Coolstreamingand its preliminary experimental results,” in Proc.MMSP, 2005. • [10] T. Silverston and O. Fourmaux, “P2p iptvmeasurement: 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 NetworkConditions: a Measurement Study,” IEEE INFOCOM 2009.