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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

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Outlines

Introduction Experiment Setup Measurement Analysis

General Observation Traffic Pattern Video Download Policy Peers Neighborhood Video Peers Lifetime

Conclusion

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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.

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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

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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/

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Measurement Analysis

Packet traces summary

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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.

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Measurement AnalysisTVants

Fluctuating largely

Quiet constant

Total download and upload throughput for TVAnts.

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Measurement AnalysisPPLive

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Measurement AnalysisPPStream

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Measurement AnalysisSOPcast

Received no traffic, but PPStream was working well.

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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.

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Traffic Pattern

Signaling sessionsSignaling sessions

Video sessions

Video sessions

Signaling sessions

They are not clearly formed.

Signaling sessions

Video sessions

A balanced use of TCP and UDP

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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.

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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

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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

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Peers Neighborhood

Low and constant

High and constant

High and fluctuates largely

High and fluctuates

Using an important part of UDP traffic

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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.

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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

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Video Peers Lifetime

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

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Video Peers Lifetime

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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

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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.