real-time traffic control in atm networks

8/3/2019 Real-time Traffic Control in Atm Networks

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Real Time Traffic Control

in ATM Networks


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Overview

This paper presents a fuzzy logic-based

system to deal with the real-time traffic

control problem in ATM networks. TheFuzzy Leaky Bucket ( FLB ) modifies

the token rate. In this the modified

Leaky Bucket ( LB ) technique iscombined with the moving window

mechanism to identify the traffic

parameters.


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WHAT IS FUZZY LOGIC?????

It is the logic applied tohandle the uncertainity

due to vagueness by

representing the

human response in

proper mathematical

algorithms.


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Asynchronous transfer Mode is preferred

over synchronous mode.

receivertransmitter


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Asynchronous Transfer Mode

ATM is the suitable transfer mode for

transmission in new high-speed

integrated service networks.

To guarantee a certain quality of service

in terms of delay and cell-loss

probability, suitable traffic control isrequired.

The most popular traffic control method

is the Leaky Bucket ( LB ) mechanism .


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Leaky Bucket Technique

Reference:- Behrouz A Forouzan,Data Communications And Networking, Tata McGraw-

Hill Publishing Company Limited,4th Edition,pp.761-780,2006.


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Leaky Bucket Technique

This mechanism turns an uneven

flow of packets from the user

processes inside the host into

an even flow of packets onto

the network, smoothing out

bursts .


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Modified Leaky Bucket Mechanism

But for many applications, it is better to

speed up the output when large bursts

arrive, so a more flexible algorithm isneeded.

In this paper, a fuzzy logic-basedsystem is presented to deal with the

real-time traffic control problem in

ATM networks.


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Requirements of Real TimeTraffic Control Mechanism

We try to build a dynamic control mechanism

which will provide quality of services to all

connections sharing the network resources.

On the other hand, it should be smooth thetraffic and improve utilisation efficiency of the

bandwidth.


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Fuzzy Logic Control Mechanism

We have combined the modified LB technique

with the moving window mechanism to

identify the traffic parameters.

It needs to be point out that the token rate R

and queue buffer length M are crucial to cell

loss rate and mean time delay.

The Fuzzy Leaky Bucket ( FLB ) modifies the

token rate according to the peak rate and the

burst time which characterize the source

behavior.


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FUZZY LOGIC CONTROLLER

Reference:-Ray-Guang Cheng, Cheng-Ju Chang, Design of a FuzzyTraffic Controller for A

Networks, IEEE/ACM Transactions on Networking, Vol 4, No 3, pp 460-469, June 1996.


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T. D. Ndousse, Fuzzy neural control in ATM networks, IEEE J.Select. Areas Commun., vol. 12, pp. 14881494, Dec. 1994.

Reference-


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Working Principle

FLB chooses the burst time ( x ) and the peak

cell rate ( PCR ) as the crisp input parameters.

After fuzzification, decision making and

defuzzification, get the crisp output value K , K

( 0,l ), then

Token rate R = K*h.

The membership functions chosen for thefuzzy sets are shown below. The input and

output variables are divided into five fuzzy

subsets: Very Low ( VL ), Low ( L), Medium (

M ), High ( H ) , Very High ( VH ).


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Steps of Fuzzy Logic

Define the Input and Output.

Choose the Universe where Fuzzy is

defined.

Fuzzification.

Define the set of RuleBase.


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Membership Function for the input X

Membership Function for the

input PCR.


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Membership Function for the output K


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X

PCR VL L M H VH

VL VH VH H M L

L VH VH H M L

M VH H M L VL

H H M L VL VL

VH M L VL VL VL

Table: Fuzzy Rule Base

The above table gives the fuzzy conditional rules. If the

PCR is low or medium, that is, the source continues non-

violating behavior, its credit is increased and K will increase.

If the PCR is high, a sign of possible beginning of violation

on the part of source, the K will be Medium.


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Simulation Results

a) Cell loss prob vs peak rate variation b) Cell loss prob vs mean burst

time variation

Performance compare of FLB and LB algorithm

Reference-T. D. Ndousse, Fuzzy neural control in ATM networks, IEEE J. Select.Areas Commun., vol. 12, pp. 14881494, Dec. 1994.


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Simulation Results (contd)

The performance of FLB is compared with LB

system, focusing on the cell loss probabilitycurves as a function of the peak rate and burst

time violations.

The curve 2 is the response curve of modified

FLB system, while the curve 1 is the response

curve of the LB mechanism. Simulation results

show that FLB is better than the LB in terms

of cell loss probability and mean delay time.


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Conclusion

We have modified the Leaky Bucket

technique( LB ) to identify the traffic

parameters. The Fuzzy Leaky Bucket ( FLB )modifies the token rate according to the peak

rate and the burst time which characterize the

source behavior.

Due to this token rate we get maximumutilisation of bandwidth with less conjestion in

the network.


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References S.Rajasekaran and G.A.Vijayalakshmi,Neural

Networks,Fuzzy Logic and Genetic Algorithms Synthesis andApplications,Prentice,Hall of India Private Limited,4th

Edition,pp.157-221,2006.

Behrouz A Forouzan,Data Communications And

Networking,Tata Mc Graw-Hill Publishing Company

Limited,4th Edition,pp.761-780,2006.

Vojislav Kecman,Learning and Soft Computing ,Pearson

Education,pp.365-391,2006.

R. Jain, Congestion control and traffic management in ATM

networks: recent advances and a survey, Comput. Networks

ISDN Syst., vol. 28, no. 13, pp. 17231738, Oct. 1996.


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References -Guang Cheng, Cheng-Ju Chang, Design of a FuzzyTraffic

Controller for ATM Networks, IEEE Transactions onNetworking, Vol 4, No 3, pp 460-469, June 1996.

RayV. Frost and B. Melamed, Traffic modeling for ATM

Networks, IEEE Commun. Mag., vol. 32, Mar. 1994.

J.S.R.JANG AND C.T,SUN, Neuro Fuzzy and Soft

Computing,Pearson Education,pp.47-70,2004.


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Thank You!!!


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real-time traffic control in atm networks

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