caia t seminar overviewcaia.swin.edu.au/talks/caia-talk-100225a.pdf · t2 h u rsd2 a y, 2 5 f e b...

Broadband Internet Traffic Simulation & Synthesis (BITSS)

Carl JavierResearch Engineer

Funded by Foundation (USA)

1Thursday, 25 February 2010

CAIA Seminar http://caia.swin.edu.au [email protected] February 25, 2010 Page

Funded by

Overview

• Motivation of project

• Previous Research

• Game Traffic Synthesising Technique

• Tools & Libraries Developed

• Results and Demonstration

• Conclude & many thanks

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Funded by

Motivation for BITSS?

• Consumer Broadband Networks incorporate:

• Video/TV

• Telephony

• Gaming

• File/Data downloads

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Future Network Complexity



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Consumer Broadband Internet

4

Home Network

ISP

Internet

Content Download

Telephony

Video

Gateway



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Consumer Broadband Internet

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Home Network

ISP

Internet

Content Download

Telephony

Video

Gateway

Key Link



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Motivation

• ISPs and researchers need to know current trends of current broadband traffic to build and cater for future growth and future networks.

• Create tools and models to be used for experimentation and simulations.

• Start off with game traffic modeling because of previous research experience.

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Previous Game Modeling Work

• Mathematical model predictions by Tony Cricenti and Phillip Branch specifically on First Person Shooter Games (FPS) [1] using Auto Regression Moving Average (ARMA).

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[1] A.Cricenti, P.Branch and G.Armitage, “An ARMA(1,1) Prediction Model of First Person Shooter Game Traffic”, in 10th IEEE Workshop on Multimedia Signal Processing, Cairns, Australia, 8 - 10 October 2008, pp 736 - 741.

• Use ARMA to predict server to client network game packet size time series for any N player FPS game.



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ARMA Specific Game characteristics

• ARMA (1,1) formulae

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Roughly the same regardless of N player traffic game.

Residual

Packet sizeTime Series



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ARMA(1,1) Parameters

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N Players

2 0.97 0.77

3 0.98 0.82

4 0.97 0.81

5 0.98 0.82

6 0.98 0.84

7 0.98 0.82

8 0.98 0.83

9 0.98 0.84

Quake 3



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ARMA game traffic prediction

• Take 2 and 3 player network zero mean traffic time series [2].

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[2] Simulating Online Network Games (SONG) Database http://caia.swin.edu.au/sitcrc/song

• Acquire the actual game play statistics (remove beginning connections and end connections periods)

Time

Pack

et S

ize

StartGame

EndGame

Game Play



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Obtain residual time series & PDF

• Using ARMA, we obtain residual time series are obtained from the empirical time series and create residual PDFs for 2 and 3 player.

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−200 −100 0 100 200

0.00

0.01

0.02

0.03

0.04

0.05

0.06

2 player residuals

X values

Y va

lues

−200 −100 0 100 200

0.00

0.01

0.02

0.03

0.04

3 player residuals

X values

Y va

lues



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Convolution of Residuals

• Convolution of 2 player and 3 player residual PDFs to obtain 5 player.

• Eg

• 2 player pdf * 2 player pdf = 4 player pdf



• ... and so on until N Player

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Correct Minor shifts

• Due to convolution, residuals maximums are shifted and must be rotated via x axis.

• Shape is similar to empirical but maximum not at zero

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* =eg

=Rotatealong x axis



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Obtain N residual time series & PDF

• Using a normal random generator and using the CDF of the new N player residual PDF, time series is of a N player is produced.

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−200 −100 0 100 200

0.00

00.

005

0.01

00.

015

0.02

00.

025

5 player Synthetic residuals

X values

Y va

lues

EmpiricalSynthentic



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Results of Synthesis Traffic

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0 200 400 600 800 1000 1200

0.000

0.005

0.010

0.015

5 player Quake3 Server toClient UDP payloads

Bytes

Density

EmpiricalSynthetic



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7 and 9 Player

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0 200 400 600 800 1000

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014


Bytes

Density

EmpiricalSynthetic

0 200 400 600 800 1000

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014


Bytes

Density

EmpiricalSynthetic



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Create Network Traffic

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0 200 400 600 800 1000 1200 1400

0.00

00.

001

0.00

20.

003

18 and 30 player Synthetic Packet Lengths

Packet length (Bytes)

N=18N=30



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Game Packet Generator

• ISPs and Researchers can use game packet generator for network testing and experiments.

• Generates synthetic FPS game stream of server to client.

• Generate 2 < N < 32 player game traffic.

• Choose from a selection of FPS games or use your own data.

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Game Packet Generator

• Runs on FreeBSD and Linux

• Dependencies:

• Python 2.6

• Python-numpy

• Python-scipy

• dpkt

• libdnet

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Games Synthesised

• Enemy Territory 2 Pro (ET2PRO)

• Half Life 2 Counter Strike (HL2CS)

• Half Life 2 Death Match (HL2DM)

• Half Life Counter Strike (HLCS)

• Half Life Death Match (HLDM)

• Quake 3 (Q3)

• Quake 4 (Q4)

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Pre Defined Phi & Theta Values

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GameETPRO 0.98 0.68HL2CS 0.79 0.12HL2DM 0.97 0.71HLCS 0.96 0.84HLDM 0.97 0.84

Q3 0.97 0.77Q4 0.95 0.60



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Game Packet Generator Features

• Create live traffic.

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./pktgen.py -game q3 -N 5 - IP 192.168.1.4

• Create network friend PCAP files.

./pktgen.py -game q3 -N 5 - IP 192.168.1.4 -write pcap.dmp



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• Use predefine or customise and values.

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./pktgen.py -game hl2cs -N 5 - IP 192.168.1.4 -phi 0.90 -theta 0.38

Game

ETPRO 0.98 0.68

HL2CS 0.79 0.12

HL2DM 0.97 0.71

HLCS 0.96 0.84

HLDM 0.97 0.84

Q3 0.97 0.77

Q4 0.95 0.60



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• Use own 2 and 3 player PCAP files to generate traffic.

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./pktgen.py -game q3 -N 5 - IP 192.168.1.4 -cap2 2player.dmp -cap3 3player.dmp

• Change the inter-arrival time of packets (ms)

./pktgen.py -game q3 -N 5 - IP 192.168.1.4 -iat 70



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• Specify the number of packets or duration to be generated.

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./pktgen.py -game q3 -N 5 - IP 192.168.1.4-hrs 1 -mins 50 -secs 32

./pktgen.py -game q3 -N 5 - IP 192.168.1.4-pkts 20000



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Where/When to use it?

• Testing/Stressing links

• VoIP Codec Performance testing

• TCP congestion algorithm testing

• Queuing/Router testing

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Omnet/NS C Library

• C library that generates an ARMA packet length time series.

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Future Perspective

• Add more game genres such as Mass Multi Online Role Playing Game (MMORPG)

• Create VoIP capability generator

• Skype and different codecs

• Create empirical models for test scenarios

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Conclude

• Consumer Broadband networks are becoming complex.

• ISPs and Researchers can use tools for experiments and network testing to cater for future networks.

• Game Packet Generator is available.

• C library for Omnet and NS is out.

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Funded by

Thank you

• Thanks to Agilent Technologies Funding (USA)

• The corner of wisdom/coolness (Warren & Lawrence & Amiel)

• Phillip and Tony as technical and theoretical aids.

• Rest of the Telecomms research and academic staff members at Swinburne.

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Special Thanks

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Special Thanks to Grenville Armitage for his wisdom, support and encouragement.



Funded by

Questions?

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caia t seminar overviewcaia.swin.edu.au/talks/caia-talk-100225a.pdf · t2 h u rsd2 a y, 2 5 f e b...

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