Queuing For Dummies - Performance Evaluationperfeval.epfl.ch/printMe/queuing.pdf · product form 49...

Queuing TheoryFor Dummies

Jean‐YvesLeBoudec1

All You Need to Know About Queuing Theory

Queuingisessentialtounderstandthebehaviour ofcomplexcomputerandcommunicationsystemsIndepthanalysisofqueuingsystemsishardFortunately,themostimportantresultsareeasy

Wewillstudyonlysimpleconcepts

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1. Deterministic QueuingEasybutpowerfulAppliestodeterministicandtransientanalysisExample:playbackbuffersizing

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Use of Cumulative Functions

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Solution of Playback Delay Pb

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A(t) A’(t) D(t)

time

bits

d(0)d(0) - d(0) +

d(t)

A.

2. Operational Laws

Intuition:SayeverycustomerpaysoneFrperminutepresentPayoffpercustomer=RRateatwhichwereceivemoney=NInaverageλcustomersperminute,N=λR

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Little Again

ConsiderasimulationwhereyoumeasureRandN.YouusetwocountersresponseTimeCtr andqueueLengthCtr. Atendofsimulation,estimate

R= responseTimeCtr /NbCustN=queueLengthCtr /T

whereNbCust =numberofcustomersservedandT=simulationduration

BothresponseTimeCtr=0 andqueueLengthCtr=0 initiallyQ:Whenanarrivalordepartureeventoccurs,howarebothcountersupdated?A: queueLengthCtr +=(tnew ‐ told). q(told)whereq(told)isthe

numberofcustomersinqueuejustbeforetheevent.responseTimeCtr +=(tnew ‐ told). q(told)

thusresponseTimeCtr == queueLengthCtr andthus

N=RxNbCust/T;nowNbCust/Tisourestimatorof

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Other Operational Laws

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The Interactive User Model

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Network Laws

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Bottleneck AnalysisApplythefollowingtwobounds1.2.

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Example

(1)

(2)

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Throughput Bounds

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Bottlenecks

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A

DASSA

Intuition:withinonebusyperiod:toeverydeparturewecanassociateonearrivalwithsamenumberofcustomersleftbehind

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3. Single Server Queue

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i.e. which are event averages (vs time averages ?)

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2 4 6 8 10Requests per Second

0.5

1

1.5

2

2.5

Mean Response Time in seconds

Non Linearity of Response Time

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Impact of Variability

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0.2 0.4 0.6 0.8Utilization

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4

6

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10

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Mean Response Time

Optimal SharingComparethetwointermsof

ResponsetimeCapacity

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The Processor Sharing Queue

Models:processors,networklinks

Insensitivity:whatevertheservicerequirements:

Egalitarian

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PS versus FIFO

PS FIFO

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4. A Case Study

Impactofcapacityincrease?OptimalCapacity?

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Methodology

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4.1. Deterministic Analysis

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Deterministic Analysis

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4.2 Single Queue Analysis

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Assumenofeedbackloop:

4.3 Operational AnalysisArefinedmodel,withcirculatingusers

ApplyBottleneckAnalysis(=OperationalAnalysis)

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Z/(N-1)

-Z

1/c

waiting time

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5. Networks of QueuesStability

QueuingnetworksarefrequentlyusedmodelsThestabilityissuemay,ingeneral,beahardone

Necessaryconditionforstability(NaturalCondition)

serverutilization<1

ateveryqueue

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Instability Examples

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Poissonarrivals ;jobsgothrough stations1,2,1,2,1then leaveAjobarrivesastype1,thenbecomes 2,then 3etcExponential,independentservicetimeswith meanmi

Priority schedulingStation1:5>3>1Station2:2>4

Q:What is thenaturalstability condition?A: λ (m1 +m3 + m5 )<1

λ (m2 + m4) < 1

λ =1m1 =m3 =m4 = 0.1m2 =m5 = 0.6Utilizationfactors

Station1:0.8Station2:0.7

Networkisunstable!

Ifλ (m1 +… +m5 )<1networkisstable;why?

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Bramson’s Example 1:  A Simple FIFO Network

Poissonarrivals;jobsgothroughstationsA,B,B…,B,AthenleaveExponential,independentservicetimes

Steps2andlast:meanisLOthersteps:meanisS

Q:Whatisthenaturalstabilitycondition?A: λ (L +S )<1

λ ((J‐1)S +L )<1Bramsonshowed:maybeunstablewhereasnaturalstabilityconditionholds

Bramson’s Example 2A FIFO Network with Arbitrarily Small Utilization Factor

m queues2typesofcustomersλ =0.5eachtyperoutingasshown,…=7visitsFIFOExponentialservicetimes,withmeanasshown

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L LS L LS S S S S S S

Utilization factorat every station≤4λ SNetworkis unstable forS ≤0.01L ≤S8m =floor(‐2(logL )/L)

Take Home Message

Thenatural stability conditionis necessary butmay notbe sufficient

Thereis aclassofnetworkswhere this never happens.ProductFormQueuingNetworks

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Product Form Networks

Customershaveaclass attributeCustomersvisitstationsaccordingtoMarkovRouting

Externalarrivals,ifany,arePoisson

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2StationsClass=step,J+3classes

Canyou reduce thenumberofclasses?

Chains

Customerscanswitchclass,butremaininthesamechain

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ν

Chains may be open or closed

Openchain=withPoissonarrivals.CustomersmusteventuallyleaveClosedchain:noarrival,nodeparture;numberofcustomersisconstant

ClosednetworkhasonlyclosedchainsOpennetworkhasonlyopenchainsMixednetworkmayhaveboth

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3Stations4classes1openchain1closed chain

ν

Bramson’s Example 2A FIFO Network with Arbitrarily Small Utilization Factor

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L LS L LS S S S S S S

2StationsMany classes2openchainsNetworkis open

Visit Rates

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2Stations5classes1chainNetworkis open

Visit ratesθ11 =θ13= θ15 =θ22 =θ24 = λθsc =0otherwise

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ν

Constraints on Stations

Stationsmustbelong toarestricted catalog ofstationsSee Section8.4forfulldescriptionWe will give commonly used examplesExample 1:GlobalProcessorSharing

OneserverRateofserveris shared equally among allcustomers presentServicerequirements forcustomers ofclassc aredrawn iid from adistributionwhich depends ontheclass(andthestation)

Example 2:DelayInfinite number ofserversServicerequirements forcustomers ofclassc aredrawn iid from adistributionwhich depends ontheclass(andthestation)Noqueuing,servicetime=servicerequirement =residence time

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Example 3:FIFOwith BserversB serversFIFOqueueingServicerequirements forcustomers ofclassc aredrawn iid from anexponentialdistribution,independent oftheclass (butmay depend onthestation)

Example ofCategory 2(MSCCCstation):MSCCCwith BserversB serversFIFOqueueing with constraintsAt most onecustomer ofeach classis allowed inserviceServicerequirements forcustomers ofclassc aredrawn iid from anexponentialdistribution,independent oftheclass (butmay depend onthestation)

Examples 1and2areinsensitive (servicetimecan be anything)Examples 3and4arenot(servicetimemustbe exponential,same forall)

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Saywhichnetworksatisfiesthehypothesesforproductform

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A

B (FIFO, Exp)C (Prio, Exp)

The Product Form Theorem

Ifanetworksatisfies the« ProductForm »conditionsgiven earlierThestationary distrib ofnumbers ofcustomers can be written explicitlyItis aproduct ofterms,where each term depends only onthestationEfficientalgorithms exist tocompute performancemetrics foreven very largenetworks

ForPSandDelaystations,servicetimedistributiondoes notmatter other thanthrough its mean (insensitivity)

Thenatural stability conditionholds

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Conclusions

Queuingisessentialincommunicationandinformationsystems

M/M/1,M/GI/1,M/G/1/PSandvariantshaveclosedforms

BottleneckanalysisandworstcaseanalysisareusuallyverysimpleandoftengivegoodinsightsQueuingnetworksmaybeverycomplextoanalyzeexceptifproductform–beabletorecognizethem!

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