Transcript Document

COMMS IRAD: Traffic Modeling
IRAD Traffic Modeling Goals
• Assess current understanding of
network traffic modeling
• Determine best approach for modeling
Internet related traffic using OPNET
• Develop process for determination of
model parameters from empirical data
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Traffic Modeling
Research Plan
• Data Collection and Analysis
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Literature Search
Data Capture
Tool Acquisition
Analysis
• Custom Traffic Generator Implementation
and Test
– Design
– Code
– Test
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Data Collection & Analysis:
Literature Search Results
• Approximately 44 research papers obtained
covering the following subjects
– Probabilistic Modeling (Poisson models, ARIMA,
discrete Markovian processes, etc.)
– Self Similarity Models (Heavy tailed distributions,
Fractal Gaussian Noise)
– Fractal Point Processes
– Multi-fractal Scaling Processes
– Network Traffic / User Service Traffic Characteristics
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Literature Search Results:
Assessment of Industry
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Determined that traffic modeling has progressed over past 7 years as follows:
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Poisson models modified with heavy tails (called M Pareto models) [inadequate and
poor results] to
Discrete-time Batch Markovian Arrival Processes [better but still inadequate] to
Fractal Gaussian Noise [better with self similarity feature but does not exhibit
observed multifractal scaling] to
Fractal Point Processes (such as Fractal Binomial Noise Driven Process) which
exhibit monofractal scaling to
Multifractal based on Fractal Point Processes [better with self affinity scaling
feature] to
Multifractal [conservative cascades and discrete wavelet transform synthesis]
All methods assume stationarity for the rate process even though observed
network traffic follows diurnal cycles (not problematic as when modeling
network stress via background traffic only the peak times need be
considered)
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Data Collection & Analysis:
Reported Observations
• Traffic Characterization
– Possible sources of fractal behavior include: heavy tail distributions of WEB
file size, user idle times, FTP file size, transmit & idle times of LAN host
– WAN Traffic
• Self similar (monofractal) at larger time scales
• Recent observations indicate multifractal scaling at short time scales in data traces
• Aggregate WAN is self similar if user initiated sessions arrive in a Poisson fashion
w/ heavy tailed distribution having infinite variance
• Self similarity is independent of the network
– Network Impact
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When studying traffic over small time scales
Local properties of WAN traffic are consistent with multifractals
Multifractal scaling has little to do with the user session characteristics
Multifractal scaling is the result of protocols and end to end congestion control
methods
• The transition between multifractal and self similar scaling occurs at times scales
of approximately the Round Trip Time (RTT) of packets in the network
• For small time scales the conservative cascade closely matches the way network
mechanisms influence individual TCP connections
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Literature Search Results:
Methodologies Adopted
• Most promising approaches identified are:
– Superposition of Fractal Point Processes (Fractal Shot Noise
Driven Poisson Process, Fractal Binomial Noise Driven Poisson
Process, Fractal Renewal Process, and superposition of FRPs)
• Already incorporated into OPNET rel. 7 Modeling tools
• The FBNDP can model monofractal distributions
• The FSNDP can model multifractal distributions (3 distinct scaling
regions)
– Multifractal Wavelet Method (using conservative cascades)
• Intuitive mechanistic modeling technique, similar to that used to
analyze turbulence
• Can be used to generate general multifractal scaling
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Literature Search Results:
Current Areas of Investigation
• Most pressing needs for improvement are:
– Traffic research needs to mature from just numerical “curve” fitting to
empirical data towards the ability to predict traffic statistics based on
network configuration, protocols, and user session characteristics
– Math models can be generated but understanding of the precise causes
and mechanisms for multifractal scaling is still rather heuristic
– The problem is exacerbated in that long term observations are hampered
due to continual modifications in protocols, routing algorithms,
configurations, etc. in the networks
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Literature Search Results:
Current Areas of Investigation
• Debatable Issues
– Which method more truly models self similarity aspects of network
traffic both in terms of fitting the data and by associating cause with
effects
• Some argue that flow driven FPP is representative of user sessions (flows) and
naturally models the network traffic
• Others argue that probabilistic cascades with pdfs determined by multiresolution approximation via orthogonal wavelet coefficients can more
accurately model TCP traffic as it naturally models the network aspects
mechanisms of traffic aggregation in reverse (here the aggregate data rate is
successively divided by each stage in the cascade so as to result in multifractal
time scaling)
– Another network modeling concern is the appropriate granularity level
• Start with individual user profiles and combine them to model aggregate OR
• Generate expected aggregate traffic based on combination of empirical data
and mathematical models
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Data Collection & Analysis:
Data Capture
• Sources Identified for Modeling Internet Traffic
– Bellcore (Morristown Research & Eng. Facility)
• LAN Traces for Aug. and Oct. 1989 were found and
downloaded
• Each trace consists of first million packets starting at 11:25 am
and 11:00 am for Aug. 29th ‘89 and Oct. 5th ’89 respectively
– National Laboratory for Applied Network Research
• Offers freely available high quality network traces (this has
not been investigated under this IRAD as of yet)
– In-house collected traces
• TASC has network monitoring tools which can be used to
collect traffic statistics
• Work has begun on collecting these traces
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Data Collection & Analysis:
Analysis
• Studies undertaken to complete analysis
– Fractal Mathematics
– Fractal Point Processes
– Wavelet Processing (multiresolution approximation)
• Data Analysis
– Matlab scripts and functions were written to:
• Perform processing of raw trace data into rate process data
• Perform data reduction (statistical analysis at different time
scales) of traffic rate data
– Mean, standard variance, Allan variance, Index of Dispersion
Counts
– MLE (Least squares fitting) of reduced data to Fractal Binomial
Noise Driven Poisson Process Model Parameters (Hurst
Parameter, Fractal Onset Time Scale, mean packet rate, # of
processes, source activity ratio, and cutoff parameter)
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Data Collection & Analysis:
Analysis
• OPNET Investigations
– Use of Raw Packet Generators (RPG) added in release 7 of
OPNET was completed
– Test project files created for analyzing effectiveness of
RPG to modeling empirical data
– Confirmed correct operation by comparison with Bellcore
August ’89 trace data and OPNET generated project file
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Index of Dispersion for Counts for FBNDP
Vs Rate per FPP R, cutoff Parameter A, and 
F(T)
FBNDP IDC
vs R,A,and gamma
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IDC for A = 0.036000 and R = 250.000000
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3
10
Gamma =
Gamma =
Gamma =
Gamma =
Gamma =
Gamma =
Gamma =
Gamma =
Gamma =
1.100000
1.200000
1.300000
1.400000
1.500000
1.600000
1.700000
1.800000
1.900000
2
10
1
10
0
10
-3
10
-2
10
-1
0
10
10
T
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1
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Bellcore Ethernet LAN Trace Data
(Packet arrivals in 16 sec. Intervals)
Packet arrival rate vs time for pAug
9000
# of packets per time T
8000
7000
6000
5000
4000
3000
2000
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0
10
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30
40
Time (min) with T = 16.000000 sec.
50
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Measured IDC for Bellcore Trace vs
Fitted IDC using FBNDP Model
Note: The red trace is fitted model data using T0=0.0040 and =0.6759
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Bellcore pAug Trace Fano Factor
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Model Curve Equation:
 T
F(T)  1.0  
 T0
Note that:





2
10
F(T)
  2
1
10
0
10
-2
10
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-1
10
0
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Time (sec)
1
10
2
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Comparison for Bellcore pAug Trace
(As Reported by B. Ryu and S. Lowen)
Parameter
Our Computations
Ryu Reported Value
Hurst Parameter, H
0.84
0.87

318.2
318.2
T0
0.004
0.006
SAR
87.5
87.5

1.32
1.27
A
0.048
0.036
M
3
3
R
212.12
212.13
Note: These results compare favorably with our results; although it is not clear
why the least square fitted results are not identical.
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OPNET Generated FBNDP
(FMPP PowON-PowOFF) Model
Comments:
1. Station 1 (Stn_1) transmits via the hub to station 2 (stn_2)
2. Self similar traffic generated by raw packet generator above MAC sub-layer
3. Parameters selected correspond to Bellcore pAug trace using std variance fitting
Process Model for Stations
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Node Models
OPNET Generated Self SimilarTraffic
OPNET Generated FBNDP (PowON-PowOFF) using Standard Variance
Traffic is shown averaged over T=0.01, 0.1, 1.0, 5.0 sec intervals
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Comparison of OPNET Generated FBNDP
Trace & Bellcore Measured Trace
Packet arrival rate vs time for pAug
# of packets per time T
200
150
100
50
0
0
0.5
1
Time (min) with T = 0.125000 sec.
1.5
Traffic (packets/sec; 0.125 sec
ave.)
OPNET RPG FBNDP MODEL
800
700
600
500
400
300
200
100
0
10
30
50
Time (sec)
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70
90
Work Planned for Next Quarter
• Custom Traffic Generator Implementation
– Design: Identify and Delineate Multifractal
Wavelet Method (MWM) Processing Steps
– Code: Implement Conservative Cascade
Multifractal Traffic Generator in C/C++ and
Matlab using Wavelab
– Test: Assess MWM modeling performance
– Code: Develop OPNET Based MWM Traffic
Generator
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