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Comparing the Impact of Bandwidth and Congestion on Selection of

Heterogeneous Lines Applying Fuzzy-Genetic

Ebrahim saboktakin Rizi 1, MohammadR.Reshadinezhad2, *, Naser Nematbakhsh3

1Student of Master Degree, Department of Computer Engineering, Islamic Azad University, Najaf Abad Branch, Najaf Abad, Iran.

2Assistant professor, Department of Computer Engineering, University of Isfahan, Isfahan

8174673440, Iran. 3Assistant professor, Department of Computer Engineering, University of Isfahan, Isfahan

8174673440, Iran.

[email protected]

Abstract

A major challenge to determine the suitable

amount of submitted data for each line in

heterogeneous lines is considering the important

features of each line, including bandwidth and

congestion. According to the indeterminate

character of each line in sending data, selection of

the best sending lines in SCTP_CMT, where the

possibility of failure in less could be considered as

a significant step in improving the service quality

and speed of data transmission. The objective of this article is to review the bandwidth and

congestion among heterogeneous lines in

SCTP_CMT to evaluate the effects of these features

on the amount of the selected data per line. In here,

a fuzzy-genetic analysis method is designated by

applying the designed data set. The effects of

congestion and bandwidth characteristics in

SCTP_CMT are evaluated. The obtained results

are applied to provide intelligent models in

selecting SCTP_CMT lines and speed up data

transfer.

1. Introduction Communications and communication networks

are considered as one of the major concerns in the

digital systems, performance. Communication

protocols, and their performance may have a

substantial impact on the communication systems,

performance. In the recent years, a variety of

protocols such as TCP / IP [1] and UDP [2] has

been introduced, each with its advantages and disadvantages. Among the newly protocols for

communication systems, one can refer to SCTP

protocol [3] which proposed the idea of selecting

data from several alternate routes, [4] with

additional properties such as the multi-house

characteristic and CMT [5] which can send data to

multiple paths simultaneously. In SCTP_CMT,

when there are heterogeneous data lines with

different characteristics, including the bandwidth

and congestion, challenges such as how to select

the best lines in a data transfer comes to mind [6].

So far, there are formal methods in selecting the

best lines in SCTP_CMT heterogeneous lines, and

most of the studies in this field try to use random

choice and the rotational lines methods for data transmission at the same time on multi paths. For

better understanding of data transmission from

multiple heterogeneous SCTP_CMT paths, Figure

1 presents an example on data transferring trough

the mentioned protocol in [7].

As seen in figure1, line types include Wireless

and Mobile with different properties. In this article

understanding the effects of the important features

and reviewing the most important features that can

select a line to transmit data simultaneously from

multiple directions are studied. To review and

assess the characteristics of the lines in

SCTP_CMT with heterogeneous lines, the attempt

is made by the authors to design a genetic fuzzy

analysis method. In this regard; here adapting the

analytical designed method and using the derived

random data sets to evaluate the characteristics of broadband and congestion properties of lines, the

analysis and calculation of the effects of each of the

listed properties are made. The results obtained can

provide a method for non-homologous SCTP_CMT

lines for enhancing the speed and quality of

SCTP_CMT protocol services.

2. Related Works

Several studies have investigated the challenges

of SCTP_ CMT, including [9-8]. Few studies have

examined the selected lines problem.

Classifications of the studies conducted on lines

selection challenges in SCTP_CMT protocol with heterogeneous lines are presented in table 1.

Mohammad R Reshadinezhad et al, Int.J.Computer Technology & Applications,Vol 5 (3),1161-1167

IJCTA | May-June 2014 Available [email protected]

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Fuzzy Logic was first introduced by Prof.

Zadeh in 1968 and has already undergone

enormous changes [12].The most important idea in

using fuzzy logic, is designing a fuzzy system,

which was introduced in 1978 by Mamdany and

Sugeno for the first time [13]. In order to design the

parameters of the fuzzy system in the computer

engineering field, various methods have been

proposed, in Table (2).

Figure 1. Simultaneous data transferring at

SCTP_CMT

3. Description of the analytical method of

fuzzy-genetic

In this section, the analysis the characteristics

that influence the choice of lines to transmit data

from multiple SCTP_CMT paths with

heterogeneous lines, is presented based on the

analysis of fuzzy logic and genetic algorithms.

Section 3.1 reviews the concepts of fuzzy logic and

fuzzy systems. in Section 3.2, a summary of the

application of genetic algorithms is expressed. The

final section outlines the research methodology

proposed for analysis of fuzzy systems using fuzzy

logic and genetic algorithms.

Table 1. The most important investigations in line selection issue

Researcher

The issue

Qiao[10]

Effect of broadband lines factors on

heterogeneous wireless networks to

transmit in SCTP_CMT

Dreibhoz[11] CMT Evaluation of simultaneously data

transferring from a heterogeneous multi-

path

Adhari[7] Evaluation of Challenges of CMT in

heterogeneous lines

Table 2. The most important proposed methods for fuzzy systems

Provider

Design Method

Pham [14] Optimization by Genetic

Algorithm

Reddy [15] Extraction of fuzzy

parameters by PSO

Jang [16] Fuzzy parameters

optimization using fuzzy-

neural

Bontoux [17] Fuzzy parameters

optimization using ant colony

algorithm

3.1. Fuzzy Logic One of the new technologies which is

widespread in all fields, of science and engineering,

and the humanities, is fuzzy logic. The most

important application of fuzzy logic is in cases

where there is uncertainty for decision making and

optimization become clearer. In the recent years,

the adoptions of fuzzy logic grow in engineering

sciences, with a wide usage. The fuzzy systems are

among the most important applications of fuzzy

logic, developed by Mamdany and Sugeno (2008). They used flexibility in decision making and

management control systems, which made an

important step in the quality and efficiency in the

engineering fields. Fuzzy system is composed of

several components, the most important of which is

the inference engine using the fuzzy rules that

make decisions for control systems. The most

important flow chart of the components in a fuzzy

decision-making control systems are presented in

Figure2 [18].



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Figure 2. The main components of the

fuzzy system designed by SUGENO

For designing the optimal rules in fuzzy systems,

several models are proposed. in the related work

section.

3.2. Genetic Algorithm Over the past few years, researchers have

investigated the nature of the decision-making

mechanisms in order to understand and optimize

living creature's behaviour. This investigation was

conducted to provide heuristic and meta-heuristic

methods such as the genetics algorithm [19], ant

colony algorithm and colonial competition. Genetic

algorithm and its application in digital systems was

first proposed by John Holland in 1992 and since

then it has undergone extensive changes [20].

The genetic algorithm uses the idea of reproduction

and creates a better generation in living creatures,

acting on optimization and decision making

problems and challenges in science and

engineering, especially the digital systems. In order

to use the genetic algorithms in the optimization, several steps, including the initial generation, the

design of cost function and mutations should be

considered. One of the important points in efficient

genetic algorithm design is the selection of cost

function with genetic algorithm to solve the

problem. To solve the optimization problems a

flowchart is presented the below to designing the

genetic [21].

Figure 3. Flowchart of the genetic algorithm

3.3. Fuzzy- genetic analysis To study the impact of network congestion and

bandwidth characteristics of the selected lines in

SCTP_CMT with heterogeneous lines, in this article, using fuzzy-genetic analysis, the

specifications of [22] are examined. In order to

design fuzzy systems for analysis, the optimal rules

for fuzzy systems are required. Here the genetic

algorithm is used to optimize the rules. A better

understanding on the process of streamlining the

rules used in fuzzy systems is presented in figure 4

[23].

Figure 4. Optimization process rules

Using the evaluation data set presented in section

4.1 for genetic algorithms, the attempt is made to

derive the cost function and other parameters of the

genetic algorithm, including mutations and

enhancement functions through trial-and-error

method for extracting the most optimized rules

The inference

engine

Input

variable

s

Output

variable

s

F

u

z

z

y

f

a

i

r

D

e

f

u

z

z

y

f

a

i

r

Database

Database

Rules

Database



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required in this proposed fuzzy system and to

analyze the bandwidth and delay characteristics of

SCTP_CMT. The main parameters in the genetic

algorithm used in this study are presented in Table

3.

Table 3. The genetic algorithm settings used in this study

value parameter

Gaussian(0.1,0.1) Mutation

16 Population size

stochastic uniform Selection

5 Crossover fraction

2 Elite count

25 Generations

FIS Production Fitness

In order to become familiar with the way to convert

fuzzy system rules with chromosomes and genes

required in the form of a genetic algorithm Figure 5

of [23] is presented.

Figure 5. Mapping Fuzzy Control rules to chromosomes and genes in the genetic

algorithm

As shown in Figure 5, each one of the rules are

represented as a number and finally, every rule is

extracted in the form of chromosomes. By using

the genetic algorithms and parameters required in

designing these algorithms, here the process of selecting the best generations (rules) in the research

of genetic algorithm are presented in Figure 6 to

optimize the proposed fuzzy system rules .After

extracting the optimal rules by using a genetic

algorithm, the designing a fuzzy controller and

fuzzy-genetic analysis, take place. This proposed

fuzzy system design is performed in MATLAB

software [24]. Except the required rules where the

optimal values are calculated through a genetic

algorithm, other parameters required for the

proposed fuzzy system design are calculated

through the results of previous studies and trial-

and-error method.

Figure 6. Selection of the best rules for the

generation of a genetic algorithm

To understand this proposed fuzzy system, and the

fuzzy analysis of genetic in this study, the main

parameters used in this proposed fuzzy system are presented in Table (4). The optimal rules derived in

the inference engine of this proposed fuzzy system

by a genetic algorithm with fuzzy terms for every

time variable is discussed in [25] Figure 7.

Table 4. The parameters used in this

proposed fuzzy system

4. Evaluation of results

To review the methods of fuzzy-genetic

analysis, the required data sets in the evaluation are

discussed in section 4.1. In section 4.2, the

influence of the broadband factor on the choice of

lines in SCTP_CMT with heterogeneous lines is

discussed and in Section 4.3, this effect is

investigated with respect to congestion factor.

Finally, in Section 4.4 comparison of specifications

of each investigated factories is made.

Lin

gu

isti

c

va

ria

ble

s

Nu

mb

er

of

me

mb

ers

hip

fu

nc

tio

ns

Nu

mb

er

of

rule

s

Ty

pe

o

f m

em

be

rsh

ip

fun

cti

on

s

Ty

pe

d

es

ign

ed

co

ntr

oll

er

Bandwidth 3 27 GAUSS

MF

SUGENO

[26]

Line

congestion 3 27 GAUSS

MF

SUGENO

[26]



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Figure 7. Inference engine designed using optimized rules

4.1 Sets of evaluation data To analyse the characteristics listed in Section

3, the data sets for the evaluation and analysis of

fuzzy-genetic algorithm are designed and extracted.

The data sets presented here are used to calculate

the cost function in the genetic algorithm, and

examine the impact of congestion and bandwidth

characteristics of the SCTP_CMT with

heterogeneous lines. In order to extract the required

data sets data transferring must be calculated

through equation 4.1.

Z (X\Y)*0.01(D+F) (4.1)

Table 5. Conditions considered to extract

evaluating data sets

Where x is the bandwidth, y is the congestion, d is

the data and f is the data fail. This formula is based

on the amount of features from each line and should be used as the initial data set for designing a

cost function and fuzzy-genetic analysis. The initial

data collection for extracting the evaluation data

sets are presented in Table 5. In this study, the data

sets and fuzzy control of the proposed fuzzy-

genetic analysis are applied for designing the

system rules. The following sections examine the

results of the analysis for fuzzy-genetic

characteristics affecting the choice of transmitted

data on each line in SCTP_CMT with heterogeneous lines.

Then, by using the formula depicted in 4.1 buffer

amounts required per line minimum lost are

extracted and the obtained results are summarized

in Table 6.

4.2 Bandwidth Properties

The most important performance characteristics

of a data transferring in a multi-path system, is line

free bandwidth [6]. Higher bandwidth for each line indicates the ability to send more data through that

line without repetition again. Following the design

and analysis of genetic fuzzy provided in this

study, the amount of data needed to evaluate the

characteristics of bandwidth lines in SCTP_CMT

with heterogeneous lines are discussed. Here after

the simulation of this proposed fuzzy system and

design of optimize required rules of fuzzy systems

using a genetic algorithm in MATLAB software,

the influence of factors on the amount of bandwidth

required for each line are investigated and shown in

Figure 8.

Table 6. The amount of data required for every line by using equation 4.1

Free

bandwidth

line

congestion

Amount of

required data

0.02 0.5 0.04

0.04 0.07 0.57

0.02 0.3 0.06

0.03 0.9 0.03

0.07 0.25 0.28

0.2 0.03 6.66

0.45 0.09 5

0.3 0.02 15

0.8 0.4 2

0.02 0.02 1

0.9 0.07 12.86

Pe

rce

nt

of

the

a

ctu

al

am

ou

nt

of

req

uir

ed

da

ta

Pe

rce

nta

ge

of

co

ng

es

tio

n l

ine

Pe

rce

nta

ge

of

fre

e b

an

dw

idth

of

lin

e

Ty

pe

of

lin

es

he

tero

ge

ne

ou

s

lin

es

0.4 0.25 0.10 Wireless Line 1

2.33 0.15 0.35 ADSL Line 2

0.75 0.20 0.15 Mobile Line 3

0.17 0.30 0.05 Phone Line 4



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As seen in this figure, with an increase in the

bandwidth, the amount of data to be transmitted

over the data lines in SCTP_CMT are increased.

By using the results obtained in this section, the

average bandwidth impact factor on the amount of

data needed to transmit in each line in SCTP_CMT

is calculated (section 4.4).

Figure 8. The impact of bandwidth characteristics on required amount of data

per line

4.3 Characteristics of line congestion

Another important feature in this calculation is

the amount of data that each line can transmit with

respect to the congestion characteristics of the line.

Line congestion has almost a linear dependence on

bandwidth and with an increase and a decrease in

bandwidth the amount of delay will fluctuate [6].

The simulation is carried out using MATLAB

software to evaluate the characteristics of line

congestion for investigating the effect of this factor

on the amount of data required for each line to be transmitted in SCTP_CMT with heterogeneous

lines. The amount of transferred data in

SCTP_CMT lines in assessment data set is

proposed by considering the characteristics of line

congestion. As seen with an increase in line

congestion, in different time intervals in

simultaneous data transfer from multiple

heterogeneous paths in SCTP_CMT, the amount of

data required for each of the lines will have a

notable decrease in their slopes, and this fact

reflects the strong influence of this factor on the

amount of data required by each line in the

SCTP_CMT (figure 9).

Figure 9. Impact of line congestion characteristics on the amount of required data per line

4.4 Comparison of the features

According to the analytical results obtained in the

previous sections, here the average effects of each

of the features are extracted, and the importance of

features are evaluated. The average effect derived

in this section is based on the measured data sets in (4.1). The average impact factors extracted for

bandwidth and congestion in line on the amount of

data transmission in SCTP_CMT with

heterogeneous lines is presented. In table 7 and

graph 1.

Table 7. Comparing the effects of means bandwidth and congestion

Factor Average of impact

bandwidth 0.52

Network delay 0.38

Figure 10. Comparison of average effect of congestion and bandwidth features

As observed in table 7 and figure 10, after the

fuzzy-genetic analysis is designed in the article, in

order to review the most important features

influencing the data transfer in SCTP_CMT with

heterogeneous lines, bandwidth on average has a

25% greater impact on the amount of data required

for each line with respect to the congestion factor

of the line. The results obtained in here can be

assigned to provide parametric models of the

selection lines used in SCTP_CMT with

heterogeneous lines which would eventually lead to



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an increase in performance and the QOS in this

presented Protocol.

5. Conclusions and recommendations

One of the major facing the researchers in

SCTP_CMT protocol performance is the selection

of a line and the amount of data to be transmitted

for each line in SCTP_CMT. In order to present

models for selection of transferring data on each

heterogeneous line in SCTP_CMT, identifying the

most important factors influencing data transfer is

required for each line. Therefore, in this study by

using a fuzzy system design, the effects of

bandwidth and congestion line on the amount of

required data in SCTP_CMT with heterogeneous

lines are determined. For extracting fuzzy system parameters used in this study, the genetic

algorithms are used along with, the fuzzy-genetic

expression. After extracting the raw data set and

design of Fuzzy Systems, the assessments of

mentioned criteria are discussed. The findings here

indicate a higher impact of bandwidth on the

amount of required data in selecting each line in

SCTP_CMT with heterogeneous lines. Using the

results of this study in selecting parametric models

based on obtained factors mentioned here can

contribute to future work.

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