Real-time Traffic Control in Atm Networks
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Transcript of 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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