Fuzzy Logic Routing in OPNET 9 - static.aminer.org · Project Description • Two States needed to properly route packets • Challenge: Implementation of Fuzzy Logic within the hub

ENSC 833ENSC 833--3: NETWORK PROTOCOLS AND PERFORMANCE 3: NETWORK PROTOCOLS AND PERFORMANCE CMPT 885CMPT 885--3: SPECIAL TOPICS: HIGH3: SPECIAL TOPICS: HIGH--PERFORMANCE NETWORKSPERFORMANCE NETWORKS

FINAL PROJECT PRESENTATIONSFINAL PROJECT PRESENTATIONS

Fuzzy Logic Routing in OPNET 9.0Fuzzy Logic Routing in OPNET 9.0

Edward K. ChenEdward K. Chen9730097300--23282328

[email protected]@cs.sfu.ca

ROAD MAPROAD MAP

uu Simulation Results and ComparisonSimulation Results and Comparison

uu References and QuestionsReferences and Questions

uu IntroductionIntroduction

uu Project OverviewProject Overview•• Fuzzy LogicFuzzy Logic

uu What is it?What is it?uu Where is it used?Where is it used?

•• Project Scenario and SetupProject Scenario and Setup

INTRODUCTIONINTRODUCTION

uu Explosive growth and use of Internet in past decadeExplosive growth and use of Internet in past decade

uu Saturation of network resources Saturation of network resources

uu Need to effectively route packets to minimize endNeed to effectively route packets to minimize end--toto--end end delaydelay

uu Utilization of resources Utilization of resources àà minimize buffer size with same minimize buffer size with same arrival/service ratesarrival/service rates

uu Fuzzy Logic based router in simple applicationFuzzy Logic based router in simple application

Project OverviewProject Overview

uu Node ModelNode Model

uu Objective: Objective: •• Minimize endMinimize end--toto--end delay of packetsend delay of packets•• Minimize buffer sizeMinimize buffer size

uu Decision made at hub to send packets to one of two serversDecision made at hub to send packets to one of two servers

uu Ability to make “smart” decision to benefit overall system, Ability to make “smart” decision to benefit overall system, not individual packets.not individual packets.

uu Example:Example:•• Packet “A” joins Queue_1 with less packetsPacket “A” joins Queue_1 with less packets•• If If ??11 >> >> ??00 : longer overall system delay: longer overall system delay•• When no additional packets When no additional packets àà individual = system individual = system

performanceperformance



uu Application of Fuzzy Logic to achieve objectivesApplication of Fuzzy Logic to achieve objectives

uu Other Routing Algorithms consideredOther Routing Algorithms considered•• Shortest QueueShortest Queue

uu Packets sent to queues with least number of customerPackets sent to queues with least number of customer

•• Round RobinRound Robinuu Packets sent to alternate queuesPackets sent to alternate queues

uu Introduced by Prof Introduced by Prof LotfiLotfi ZadehZadeh of UC Berkeley in the 1960sof UC Berkeley in the 1960s

uu Closely resembles human reasoning, uncertainty and vaguenessClosely resembles human reasoning, uncertainty and vagueness

uu Used in control application: refrigerators, washing machines, Used in control application: refrigerators, washing machines, welding machines, cameras and robots welding machines, cameras and robots

uu Classic Logic has two value: true or falseClassic Logic has two value: true or false

uu Real life systems are nonReal life systems are non--linear: classic logic not adequatelinear: classic logic not adequate

uu Example:Example:•• How are you feeling today?How are you feeling today?•• How was the movie?How was the movie?

Fuzzy LogicFuzzy Logic

Fuzzy Logic Component DescriptionFuzzy Logic Component Description

uu Four main components:Four main components:

•• Fuzzification:Fuzzification:uu Input converted to set of certainties according to Input converted to set of certainties according to

membership functionsmembership functions

•• Fuzzy RuleFuzzy Rule--Base:Base:uu Set of IFSet of IF--THEN rules used to decide outcomeTHEN rules used to decide outcomeuu Designer dependent Designer dependent àà different conclusions can be drawn different conclusions can be drawn

from same datafrom same data

Fuzzy Logic Component DescriptionFuzzy Logic Component Description

•• Inference Mechanism:Inference Mechanism:uu Two Step ProcessTwo Step Process

•• Determine which rules from the IFDetermine which rules from the IF--THEN rules applyTHEN rules apply•• Determine which conclusion reached based on rulesDetermine which conclusion reached based on rules

•• DeDe--FuzzificationFuzzification::uu Converts output of Inference Mechanism into a quantitative Converts output of Inference Mechanism into a quantitative

numbernumber

Fuzzy Logic Component DiagramFuzzy Logic Component Diagram

Fuzzification Inference Mechanism

Rule_Base

De-Fuzzification

Fuzzy Fuzzy InputsInputs

Fuzzy Fuzzy OutputOutput

Project DescriptionProject Description

uu Hub Process ModelHub Process Model


•• Two States needed to properly route packetsTwo States needed to properly route packets

•• Challenge: Implementation of Fuzzy Logic Challenge: Implementation of Fuzzy Logic within the hubwithin the hub

•• IDLEIDLEuu Initialization of variables.Initialization of variables.uu No init state used coz all variables needs to be reset No init state used coz all variables needs to be reset

each timeeach time

uu Route PKTRoute PKT•• Uses fuzzy logic to route packetsUses fuzzy logic to route packets•• Dependent on parameters: serve rice rates (2), queue Dependent on parameters: serve rice rates (2), queue

lengths(2) and arrival rates (3)lengths(2) and arrival rates (3)


uu Fuzzification:Fuzzification:

•• Two Inputs: Two Inputs: #1 Expected#1 Expected--delaydelay--differencedifference

( ) ( )

+−

+=

11

00

11

11

µµsss

where where is expected delay of Queue_0is expected delay of Queue_0( )

+

00

11

µs

#2 Arrival Rates of three input streams:#2 Arrival Rates of three input streams:•• ??00, , ??11, , ??22


•• Expected_Delay_DifferenceExpected_Delay_Difference

•• Arrival RatesArrival Rates

uu Membership FunctionsMembership Functions


•• Rules “Active” when Rules “Active” when muimui > 0> 0uu If If Expected_Delay_DiffExpected_Delay_Diff = 0.5, = 0.5, ?? rates all equal 3.5rates all equal 3.5

uu NS, ZO, PS “active” for NS, ZO, PS “active” for Expected_Delay_DiffExpected_Delay_Diffuu PS, PB “active” for arrival ratesPS, PB “active” for arrival rates

uu 3 x 2 x 2 x 2 = 24 rules active3 x 2 x 2 x 2 = 24 rules active


•• Total: 5 x 3 x 3 x 3 = 135 rulesTotal: 5 x 3 x 3 x 3 = 135 rules

•• Rules are designerRules are designer--dependentdependent

•• Different rule yield different resultDifferent rule yield different result

•• Combination of results sent to Combination of results sent to DefuzzificationDefuzzification to decodeto decodeuu 24 results in Example24 results in Example

uu Inference Mechanism:Inference Mechanism:

•• Determine which conclusion reached based on Determine which conclusion reached based on rule_baserule_base..


uu DeFuzzificationDeFuzzification::

•• Converts multiple results into single quantitative figureConverts multiple results into single quantitative figure

•• Uses Center of Gravity (COG) MethodUses Center of Gravity (COG) Method

∑ ∫∑ ∫=

i

i i

i

iboutput

)(

)(

µ

µ

where bwhere bii = max. value of membership function= max. value of membership function= area of the membership function with = area of the membership function with

specified certaintyspecified certainty∫ )(iµ

Results and SimulationResults and Simulation

uu Parameters:Parameters:??00=0.5 packet/sec =0.5 packet/sec µµ 00=2 packet/sec=2 packet/sec??11=0.3 packet/sec=0.3 packet/sec µµ 11=1=1 packet/secpacket/sec??22=0.6 packet/sec=0.6 packet/sec



Future WorkFuture Work

uu Adaptation of Fuzzy Logic Router to more complex system.Adaptation of Fuzzy Logic Router to more complex system.

uu Use Fuzzy Logic Control as a general approach to more practical Use Fuzzy Logic Control as a general approach to more practical situationssituations

uu Networks with finite buffers and nonNetworks with finite buffers and non--MarkovianMarkovian parametersparameters

uu Ability to prioritize different packets (guaranteed Ability to prioritize different packets (guaranteed QoSQoS))

ReferencesReferences

uu [1] [1] J. Walrand and P. Varaiya, High-performance Communication Networks,Second edition, Morgan Kaufmann, 2000

uu [2][2] R.ChengR.Cheng, , C.ChangC.Chang, “Design of a fuzzy traffic controller for ATM networks” IEEE/A, “Design of a fuzzy traffic controller for ATM networks” IEEE/ACM CM Transactions, Volume: 4 Issue: 3 , Jun 1996 Page(s): 460 Transactions, Volume: 4 Issue: 3 , Jun 1996 Page(s): 460 -- 469 469

uu [3] [3] M.SalamahM.Salamah, , H.LababidiH.Lababidi, “FBLLB: a fuzzy, “FBLLB: a fuzzy--based traffic policing mechanism for ATM based traffic policing mechanism for ATM networks”, ACS/IEEE International Conference 2001,Volume: 3, Issnetworks”, ACS/IEEE International Conference 2001,Volume: 3, Issue:2, ue:2, Page(sPage(s): 31 ): 31 --35 35

[4][4] A.KasiolasA.Kasiolas, , D.MakrakisD.Makrakis, “A fuzzy, “A fuzzy--based traffic controller for highbased traffic controller for high--speed ATM networks speed ATM networks using realistic traffic models “ Multimedia Computing and Systemusing realistic traffic models “ Multimedia Computing and Systems,. IEEE Transactions , s,. IEEE Transactions , Volume: 2 , Jul 1999 Page(s): 389 Volume: 2 , Jul 1999 Page(s): 389 --394 394

uu [5] [5] R.ZhangR.Zhang, , Y.PhillisY.Phillis, “Admission control and scheduling in simple series parallel ne, “Admission control and scheduling in simple series parallel networks tworks using fuzzy logic”, Fuzzy Systems, IEEE Transactions, Volume: 9 using fuzzy logic”, Fuzzy Systems, IEEE Transactions, Volume: 9 Issue: 2 , Apr 2001 Issue: 2 , Apr 2001 Page(sPage(s): 307 ): 307 –– 314314

uu [6] [6] L.BarolliL.Barolli, , A.KoyamaA.Koyama, , T.YamadaT.Yamada, , S.YokoyamaS.Yokoyama, , T.SuganumaT.Suganuma, , N.ShiratoriN.Shiratori, “A fuzzy , “A fuzzy admission control scheme for highadmission control scheme for high--speed networks”, Proceedings of 12th International speed networks”, Proceedings of 12th International Workshop on Database and Expert Systems Application, 2001. PagesWorkshop on Database and Expert Systems Application, 2001. Pages 157157--161. 161.

u [7] “What is Fuzzy Logic” Pacific Northwest National Laboratory http://www.emsl.pnl.gov:2080/proj/neuron/fuzzy/what.html (March 26, 2003)

u [8] D.Mitchell, J.Yeung “Implementation of Start-time Fair Queuing Algorithm in OPNET” Ensc835, School of Engineering Science, Simon Fraser University, April 2002

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Fuzzy Logic Routing in OPNET 9 - static.aminer.org · Project Description • Two States needed to properly route packets • Challenge: Implementation of Fuzzy Logic within the hub