Informational network traffic model based on fractional calculus and constructive analysis
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Informational Network Traffic Model Based On Fractional Calculus and Constructive Analysis. Vladimir Zaborovsky, Technical University, Robotics Institute, Saint-Petersburg, Russia e-mail [email protected] Ruslan Meylanov, Academic Research Center, Makhachkala, Russia e-mail [email protected]

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Informational Network Traffic Model Based On Fractional Calculus and Constructive Analysis

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Informational network traffic model based on fractional calculus and constructive analysis

Informational Network Traffic Model Based On Fractional Calculus and Constructive Analysis

Vladimir Zaborovsky, Technical University, Robotics Institute,Saint-Petersburg, Russiae-mail [email protected]

Ruslan Meylanov, Academic Research Center, Makhachkala, Russiae-mail [email protected]


Content

Content

1.Introduction

2.Informational Network and Open Dynamic System Concept

3.Spatial-Temporal features of packet traffic    3.1 statistical model    3.2 dynamic process

4.Fractional Calculus models    4.1 fractional calculus formalism    4.2 fractal equations     4.3 fractal oscillator

5.Experimental results and constructive analysis

6.Conclusion

  • Keywords:

    • packet traffic, long-range dependence, self-similarity, fractional calculus, fractional differential equations.


Informational network traffic model based on fractional calculus and constructive analysis

Introduction

  • Packet traffic in Information network has the correlation function decays like (fractal features):

  • R(k)~Ak–b,

  • where k = 0, 1, 2, . . ., is a discrete time variable; b - scale parameter

  • QoS engineering for Internet Information services requires adequate models of each spatial-temporal virtual connection; the most probable number of packets n(x; t) at site х at the moment t given by the expression

  • where n0(x) is the number of packets at site х before the packet's arrival from site х-1.

  • The possible packets loss can be count up by distribution function f(t) in the following condition

  • So, the corresponding expression for the f(t) can be written as

1.1


Informational network traffic model based on fractional calculus and constructive analysis

Computer network as an Open System

  • Features:

  •         Dissipation

  •         Selforganization

  •         Selfsimularity

  •         Multiplicative perturbations

  •         Bifurcation

Telecommunication networkInformation network

Dynamic Feature

1 2 N 

xi

y

y= xi

Topological Feature

Point-to-point

logical structure

Multi connected

logical structure


Informational network traffic model based on fractional calculus and constructive analysis

Process Features In Informational Network

[Sec] astronomical time

[ms] effective bandwidth

[ms] nominal bandwidth

( FLAT CHANNEL)

  • Integral character of data flow

  • parameters – bandwidth, number of users ...

  • Differential character of connection

  • parameters – number of packets, delay, buffer

  • Scale invariantness of statistical characteristics

  • Fractalness of dynamics process

  • State space of network process

C(kT) = g(k) C(T)

(t) ~ t


Informational network traffic model based on fractional calculus and constructive analysis

Goals of the Model

  • state forecast

  • throghtput estimation

  • loss minimizing

  • QoS control

Model needs to provide:

Uniting micro and macro descriptions of control object

t0

  • – min packet discovering time

    t0 – relaxation time


Spatial temporal features of traffic

Spatial-Temporal Features of Traffic

Fig. 3.1. 2 RTT signal – blue and its wavelet filtering image.

Fig. 3.2. Curve of Embedding Dimension: n=6

Fig. 3.3. Curve of Embedding Dimension: n >> 1


Network traffic fine structure and general features

Signal: RTT process

Generalized Fractal Dimension Dq

Multifractal Spectrum f()

Network Traffic: Fine Structure and General Features

.


Statistical description

Statistical Description

Characteristics - Distribution Function

Parameter - Period of Test Signal (ping procedure)

Fig. 3.5. RTT Distribution Function: Ping Signals with intervals T=1 ms green, T=2 ms red, T=5 ms blue

Main Feature: Long-Range Dependence


Correlation structure of packet flow

Correlation Structure of Packet Flow

Input signal: ICMP packets

Analysing Structure: Autocorrelation function of number of packets

Fig. 3.6. Autocorrelation functions: upper RTT Ping SignalsAbscissa – numbers of the packets

Main Feature: Power Low of Statistical Moments


Correlation structure of time series

Correlation Structure of Time Series

Input: ICMP packets

Analysing Structure: Autocorrelation function of time interval

between packets

Fig 3.7. Autocorrelation function for ping signal T=5 ms, T=10 ms, T=50 ms Abscissa –time between packets


Traffic as a spatial temporal dynamic process in ip network

Traffic as a Spatial-Temporal Dynamic Process in IP network

Fig 3.8. Packet delay/drop processes in flat channel.

a)

End-to-End model

b)

Node-to-Node model

c)

Jump model

Fig 3.9. Fine Structure Packet transfer.


Informational network traffic model based on fractional calculus and constructive analysis

The equation of packet migration

  • The equation of packet migration in a spatial-temporal channel can be presented as

    • where the left part of equation with an exponent is the fractional derivative of function

    • n(x; t) – number of packets in node number x at time t

    • For the initial conditions: n0(0) = n0 and n0(k) = 0, k = 1,2, …,. we finally obtain

The dependence n(k,100)/n0 is shown graphically in Fig.3.10.

Fig.3.10.


Informational network traffic model based on fractional calculus and constructive analysis

Spatial-temporal co-variation function

The co-variation function for the obtained solution for the initial conditions n(0;t)=n0(t):

The evolution of c(m,t)/n02 with time t is shown in Fig. 3.11

Fig. 3.11.


Fractional calculus formalism

Virtual channel operator:

4.2

Multiplicative transformation of input signal:

4.3

Analytical description of input signal:

Fractional differential equation

,where

Fractional Calculus formalism

4.1

Fig 4.1. Transmission process f(t) in n-nodes (routers with  fractal parameter).

4.4

define new class of parametric signals

E, - Mittag-Leffler function,  - key parameter or order of fractional equation

4.5


Dynamic operator of network signal

a)

burst

b)

Dynamic Operator of Network Signal

network

signal

f(t)

input process

u(t)

output process

Fig. 4.2.

Input parameters: , A

network parameters: , n

Total transformation of signal in n nodes: model with time and space parameters

4.6

where E, - Mittag-Leffler function,

input process

delay

output process

burst

dissemination

Fig. 4.3.


Simple model fractal oscillator

X(t)

t

0

10

Simple Model: Fractal oscillator

4.7

where, 1<2,  - frequency, t -time.

Common solution

4.8

where AandB – constants

Example=2

X(t)

1

2

Fig. 4.4.

1 where =1.5

2 where =1.95

Fig. 4.5.


Basic solution

X(t)

Fig. 4.6.

Basic solution

The common solution: input ,A,B, output F(t)

4.9

Identification formula: input F(t), output F

4.10

Modeling example

where , 0, +<1, k - whole number then

k=4 , =0, = 0,95 and t(0,6).


Phase plane

X(t)

1

2

t

0

6

Fig. 4.8.

X(t)

Fig. 4.7.

Phase Plane

k=4 , =0, = 0,75 and t(0,6).


Model with biffurcation

X(t)

Fig. 4.9b

X(t)

1

2

t

7

Fig. 4.9c

Model with Biffurcation

If

Then

X(t)

Fig. 4.9а


Parameters identification model detailed chaos

а)

b)

c)

d)

Parameters Identification Model(Detailed chaos)

Identification process formulas

4.11

C(t)/C(0)

(0)(t)

(1)(t)

(2)(t)

Fig. 4.10.


Informational network traffic model based on fractional calculus and constructive analysis

Experimental results and constructive analysis

PPS

delay:

RTT  integral characteristic

traffic:

PPS  differential characteristic

RTT

Input

process

Output

process

Fig. 5.1.


Informational network traffic model based on fractional calculus and constructive analysis

MiniMax Description

  • Basic Idea:

  • Natural Basis of the Signal

  • Constructive Spectr of the Signal

Fig. 5.2.


Informational network traffic model based on fractional calculus and constructive analysis

blocks sequence

Constructive Components of the Source Process

source process

time

Fig. 5.4.


Constructive analysis of rtt process

Constructive Analysis of RTT Process

RTT process

sec

number of “max” in each block

Fig. 5.5.


Dynamic reflection

Dynamic Reflection

Fig. 5.6.


Network quasi turbulence

Network Quasi Turbulence

Fig. 5.7.


Forecasting procedure

Forecasting Procedure

Fig. 5.8.


Multilevel forecasting procedure

Multilevel Forecasting Procedure

Fig. 5.9.


Informational network traffic model based on fractional calculus and constructive analysis

Conclusion

  • 1The features of processes in computer networks correspond to the open dynamic systems process.

  • Fractional equations are the adequate description of micro and macro network process levels.

  • Using of constructive analysis together with identification procedures based on fractional calculus formalism allows correctly described the traffic dynamic in information network or Internet with minimum numbers of parameters.


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