Modelling IPTV Services
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Modelling IPTV Services Fernando Ramos* [email protected] *Together with Jon Crowcroft, Ian White, Richard Gibbens, Fei Song, P. Rodriguez
Transcript of Modelling IPTV Services
Modelling IPTV Services Fernando Ramos*
*Together with Jon Crowcroft, Ian White, Richard Gibbens, Fei Song, P. Rodriguez
Outline
Introduction: what IPTV is not, and why do we care
Motivation to model IPTV services
The IPTV traffic model, in some detail (W.I.P.)
Conclusions
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
What IPTV is not
live TV
web TV
p2p TV
on-demand video service cable-like TV service
IPTV is a cable-like TV service offered on top of an IP network
X X X
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Why do we care with IPTV?
One of the fastest growing television services in the world [1] 2005: 2 million users 2007: 14 million users ...and growing
High bandwidth and strict QoS requirements Big impact in the IP network
[1]ParksAssociates.TvservicesinEurope:UpdateandOutlook,2008
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Overview of an IPTV network
STB
PC
TV
DSLAM
Customer Premises
Internet IP Network
TV Head End
.
.
.
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Motivation – Why do we need a realistic IPTV Traffic Model?
Brand new service on top of an IP network User behaviour very different from other IP-based
applications
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Motivation – Why do we need a realistic IPTV Traffic Model?
To evaluate different delivery systems for IPTV
To evaluate different network architectures for IPTV
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
The dataset We have analysed real IPTV data from one of the largest
IPTV service providers ~ 6 months worth of data ~ 250,000 customers ~ 620 DSLAMs ~ 150 TV channels
NB: We consider a user is zapping if he switches between 2 TV channels in less than 1 minute.
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
IPTV Traffic Model Workload characteristics
Zapping blocks containing a random number of switching events (zapping period)
Separated by watching/away periods of random length
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
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06:00 07:12 08:24 09:36 10:48 12:00 13:12 14:24 15:36
Swit
chin
g E
vent
Num
ber
Time of the day
Zap blocks
Inter-zap block periods
IPTV Traffic Model
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
WATCHING MODE
ZAPPING MODE
IPTV Traffic Model - Detailed
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Findings: Empirical data fits with 2 gamma and 1 exponential (consistent across regions) To do: Check consistency for different channels Check consistency for period of the day
0.1
1
1 10 100 1000
Pro
port
ion
(CD
F)
Inter zap block interval (minutes) [100 DSLAMs, 40k users]
Empirical data
Gamma CDF
Exponential CDF
Gamma 2 CDF
IPTV Traffic Model - Detailed
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Findings: Empirical data fits with gamma distribution (consistent across regions) To do: Check consistency for period of the day
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1 10
Pro
port
ion
(CD
F)
Zap block size (log) [100 DSLAMs, 40k users]
Empirical data Gamma CDF Geommetric CDF Poisson CDF
IPTV Traffic Model - Detailed
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
Findings: Popularity is a) Zipf-like for top channels, b) decays abruptly for non-popular ones. To do: Add dependency of previous channel.
1.E-06
1.E-05
1.E-04
1.E-03
1.E-02
1.E-01
1.E+00
1 10 100
Nor
mal
ised
cha
nnel
acc
ess
(log
)
Channel index sorted by popularity (log) [49 DSLAMs, 16k users]
Normalised watching counter
Zipf-like distribution
Conclusions Preliminary results of an IPTV Workload model were
presented Some of the main findings:
Workload characteristics: Burst (zapping) periods separated by watch/way periods
Popularity: a) Zipf-like for top channels, b) decays fast for non-popular ones
Watching period empirical data fits with 2 gamma and 1 exponential distributions
Number of channels in a zap period fits with gamma distribution
See you at the SIGCOMM Poster Session!
9 July 2009 Fernando Ramos, Cosener's Multi-Service Networks
