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Measurement and Modeling of a Large-scale Overlay for Multimedia Streaming

Long Vu, Indranil Gupta, Jin Liang, Klara Nahrstedt

University of Illinois at Urbana-ChampaignQShine 2007, Vancouver, Canada

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Motivation

• IPTV applications have flourished (SopCast,

PPLive, PeerCast, CoolStreaming, TVUPlayer, etc.)

• IPTV growth: (MRG Inc. April 2007)

– Subscriptions: 14.3 million in 2007, 63.6 million in 2011

– Revenue: $3.6 billion in 2007, $20.3 billion in 2011

• Understanding current IPTV (P2P multimedia

streaming) applications is crucial for future designs

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Motivation• PPLive is one of the largest deployed P2P

multimedia streaming applications

• Existing studies about PPLive:

– Network-centric: video traffic, flow rate, bandwidth utilization [X. Hei 06, A. Ali 06, Silverston 07]

– User-centric: geographic distribution, user-perceived quality [X. Hei 06]

• However, there is no study about PPLive overlay characteristics: node degree, overlay randomness, node availability, etc.

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Study Objectives

• Evaluate overlay characteristics of PPLive

• Compare overlay characteristics PPLive with P2P file sharing overlay characteristics

• Draw conclusions for future designs and developments

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Next…• PPLive overview

• Challenges & study methodology

• Results:– Channel size variation– Node degree– Overlay randomness– Node availability– Session length

• Compare PPLive and P2P file sharing

• Draw conclusions

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PPLive Overview

• One of the largest deployed P2P multimedia streaming systems

• Developed in China

• Hundred thousands of simultaneous viewers

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PPLive Episode Channels

• Most popular (number of viewers)

A Program Segment (PS)

An episode channel

Movie 1 Movie 2 Movie 3 Movie 4

PS PS PS PS PS

Day 2Day 1

Time

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PPLive Membership Protocol

Client

An overlay

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Challenges

• PPLive is a closed-source system

• How to develop the measurement method to measure PPLive overlay

• How to select the right metrics to measure PPLive overlay characteristics

• Many PPLive peers are unresponsive

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Our Approaches

• Capture traffic, analyze messages

• Develop a crawler-based study methodology

• Studied Metrics:

– Channel population size

– Node degree

– Overlay randomness

– Node availability

– Node session length

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Crawler• 10 PlanetLab geographically distributed nodes to

crawl peers (Previous works use only one machine)

• Aggregate, de-duplicate crawled peers to get the peer list

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Operations

Snapshot collects peers in one channel

PartnerDiscovery collects partners of responsive peers

Studied

channels

Time1st Snapshot 2nd Snapshot 3rd Snapshot 4th Snapshot

10 min 10 min 10 min 10 min

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Next…• PPLive overview

• Challenges & study methodology

• Results:– Channel size variation– Node degree– Overlay randomness– Node availability– Session length

• Compare PPLive and P2P file sharing

• Draw recommendations

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Channel Size Varies over a day

• Peaks at noon and night

• A varies 10 times, B and C varies 2 times

• Different from P2P file sharing [Bhagwan 03]

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Channel Size Varies over Consecutive Days

• The same channel, same program: Peaks drift• Peaks depend on time and channel content

First day Second day

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Node Degree is Independent of Channel Size

Similar to P2P file sharing [Ripeanu 02]

Average node

degree scale-free

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Overlay Randomness• Clustering Coefficient (CC) [Watts 98]

– for a random node x with two neighbors y and z, the CC is the probability that either y is a neighbor of z or vice versa

• Probability that two random nodes are neighbors (D)– Average degree of node / channel size

• Graph is more clustered if CC is far from D [well-known results from theory of networks and graphs]

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Overlay Randomness May Depend on Channel Size

• Small overlay, more random

• Large overlay, more clustered

Different from P2P file sharing (small-world behavior) [Ripeanu 02, Saroiu 03]

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Time

X

Y Y

X X

Y

10m 10m 10m

1st snapshot 2nd snapshot 3rd snapshot 4th snapshot

Node Availability (1) [Bhagwan 03]

• Number of peer pairs (X,Y):• Compute P(Y=1|X=1). E.g. P(Y=1|X=1) = 2/3• 12 hours: 72 snapshots, 24 hours: 144 snapshots

Pick 185 random peers from one snapshot

…

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Nodes in one Snapshot Have Correlated Availability

Different from P2P file sharing [Bhagwan 03]

Correlated Availability

Nodes appearing together is likely appear together again

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Node Availability (2)

• Compute the difference of P(Y=1|X=1) and P(Y=1)• For example: P(Y=1|X=1) = 1/3 ; P(Y=1) = 1/2

Pick 500 random peers from all peers of the whole day (144 snapshots)

Time

X

YY

X X

10m 10m 10m

1st snapshot 2nd snapshot 3rd snapshot 4th snapshot

…

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Random Node Pairs Have Independent Availabilities

Similar to P2P file sharing [Bhagwan 03]

IndependentAvailabilities

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PPLive Peers are Impatient

50% sessions are less than 10 minutes

Different from P2P file sharing [Saroiu 03]

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Geometric Series Session Length

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Comparison

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Conclusions• Overlay characteristics of PPLive and P2P file

sharing are very different

• PPLive channel population size depends on time and channel content

• Nodes in one snapshot have correlated availability:

– route media streams (shorten startup delay)

– create sub-overlay to share content

• Simulation of P2P multimedia streaming needs to take the node availability into account

• Session Lengths are short and follow Geometric series

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Thank you!

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Backup slides

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Responded Peers

• More than 50% of peers do not respond to PING messages

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Membership Update• Experiment 1: A UIUC PPLive client attends a channel

– Turn off the UIUC client– After 10 seconds, use 10 PlanetLab crawlers to crawl that

channel, there is no IP of the UIUC client

• Experiment 2:– A UIUC PPLive client joins a PPLive channel– After 10 seconds, use 10 PlanetLab crawlers to crawl that

channel, there exists IP of the UIUC client

• Conclusion: PPLive membership update is very fast

• Unresponsive peers are not dead peers

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Active PeersGiven an overlay (or a channel) G:

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Channel Size Varies over a day

Coefficients match

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Channel Size Varies over a day