prof. k.j.blow, dr. marc eberhard and dr. scott fowler
DESCRIPTION
Significance of Joint Density Plots in Markov Internet Traffic Modelling. AHMED D. SHAIKH. Prof. K.J.Blow, Dr. Marc Eberhard and Dr. Scott Fowler Adaptive Communications Networks Research Group Electronic Engineering Aston University. Outline. Outline. Traffic Modelling Approaches. - PowerPoint PPT PresentationTRANSCRIPT
Prof. K.J.Blow, Dr. Marc Eberhard and Dr. Scott Fowler
Adaptive Communications Networks Research Group
Electronic Engineering
Aston University
Significance of Joint Density Plots in Markov Internet Traffic Modelling
AHMED D. SHAIKH
Outline
Outline
Traffic Modelling Approaches
Two types of Approaches:
• Black Box models• Internal structure is unknown. Opaque to user.
• Examples: HMM, MMPP, BMAP
• White Box models• Transparent structure. Has a physical meaning.
• Examples: Classic Markov Models, On-Off models
Markov Models – An Introduction
• Probabilistic models defining a stochastic process with finite number of states observing the Markov Property.
• Transitions occur with a fixed
transition rate Rij.
• States can model activities of traffic
sources on a network.
• Inter-Arrival times are exponentially
distributed.
• Packet level statistics obtained from Monte Carlo simulations are expressed in IPT (Inter-Packet times)
Outline
Simple Two State Markov Traffic Model
The sequence of packets will be ABABABABABABAB…..
Two state Model (Analytical analysis contd..)
• Two state models will have equal number of visits to each state.
So, V1 = V2 = 0.5
• Probability densities of time spent in each state:
• The Probability Density function of IPT for a two state model is:
Two state Markov Model (Numerical vs. Analytical results )
Two state Markov Model (Numerical vs. Analytical results –
Symmetric rates)
Higher order Statistics for Markov Models
• Higher Order Distributions
• Markov Models can also produce higher order statistics.
• Possible to study the sequence of IPTs and a variety of other unique features associated with the network traffic statistics.
• The Joint Density function for the two state Markov Model is given by:
Second Order Statistics – Joint Density (Results for Symmetric 2-
state model)
Higher Order Statistics – Joint Density
(Results for Asymmetric 2-state Model)
Outline
N-state models with Poisson statistics
The general form equation for the IPT PDF of N-state Markov Models where every state is emitting packets is:
PDF (N-state) = V1 P1(t) + V2 P2(t) + V3 P3(t)……... + VN PN(t)
Outline
Two state Model with non-Poisson statistics
The sequence of packets is AAAAAAAAAAAA……
The PDF equation for the IPT is:
PDF for the two state model with only one state emitting packets
Joint Density – 2 state model with one packet emitting state / source
PDF for IPT for N-state Markov Models with only one state
emitting packets
The general form analytical equation of the PDF of IPT for Markov loop Models with only one state emitting packets is:
Use of Gamma Markov Models
Taking it further - A Gaussian Markov Model
• Now in the general equation of the Gamma distribution, we know that as N approaches infinity, the gamma distribution can be approximated by a normal or Gaussian distribution.
• Gives a normal distribution with mean
Variance
• Gaussian Distribution PDF.
Gaussian Markov Models
Outline
Modelling Real World Example – IP Traffic Measurement at UDP Port
15010 - VoIP
Fitting a Gaussian Markov Model
Gaussian Model(PDF) = V1* Gaussian(μ1,σ1) + V2 * Gaussian(μ2,σ2) + …+V6 * Gaussian(μ6,σ6)
Comparing the Joint Densities
Outline
Understanding Packet Sequences from Joint Density Results
The significance of the Joint Density Plots
• Let us consider a 3 + 1 states Model where V1 = V2 = V3 = 1/3. (Markov Model A)
• Packet sequence can be ABBACACABCABBACAACA……….
PDF and Joint Density – Markov Model A
Markov Model ‘B’
• Let us now consider a 3 state Loop Model where V1 = V2 = V3 = 1/3. (Markov Model B)
• Packet sequence must be ABCABCABCABCABCABC…..
PDF and Joint Density – Markov Model B
Observation: Two different models have the same PDFs yet different Joint Densities. The Joint density Plots give more statistical details on Packet Sequences.
Outline
Understanding the curve of periodicity
Modelling Periodic Events with Markov Models
Small ∆ for Markov Models C and D - S∆ model
Large ∆ for Markov Models C and D - L∆ model
Multiple Periodicity
Use of S∆ and L∆ model sets to model measured results
Use of S∆ and L∆ model sets to model measured results
Outline
Summary and Conclusions• Summary:
• Observed first and second order statistics for N-state Markov Models with Poisson and Non-Poisson statistics and confirmed our anlaytical understanding of the models with simulated results.
• Established the significance of the Joint Density Plots and explored the use of simple Markov models to model unique features of Joint Density Traffic Statistics Results.
• Conclusions:
• The Joint Density Plot contains much more statistical information on the activities and nature of the traffic sources than the PDF.
• Modelling PDFs alone will result in reproducing first order statistics. Use of Joint Density Plots is Recommended to model source behaviour. Simple Markov Models can be used to model the unique features of Joint Densities.
Thank you!
Questions or comments?
The man himself:
Andrey Markov
(1856 - 1922)