Measuring P2P IPTV Systems
-
Upload
nola-dennis -
Category
Documents
-
view
28 -
download
2
description
Transcript of 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
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
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
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
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
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
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
Peers Neighborhood
Low and constant
High and constant
High and fluctuates largely
High and fluctuates
Using an important part of UDP traffic
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).
5000s
TVants. Average lifetime = 2778s
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 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
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.