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10
COMPARISONOFTWOOPTIONS.Anoperatingsystemvendorclaimsthatthenewversionof
thedatabasemanagementcodesignificantlyimprovestheperformance.Wemeasuredtheexecution
timesofaseriesofcommonlyusedprogramswithbothoptions.ThedataaredisplayedintehFigure.
The raw displays and histograms s show that t both options have the same range, but it seems
(graphically)thatthenewsystemmoreoftenprovidesasmallerexecutiontime.Theboxplotsare
moresuggestive;theyshowthattheaverageandtherangeareabouthalfforthenewsystem.
Upperfigure:Measuredexecutiontimes,inms,for100transactionswiththeoldandnewcode,with
histograms.
Lowerfigure:DataofExample2.1.Empiricaldistributionfunctionsfortheoldcode(rightcurve)
andthenewone(leftcurve).Thenewoutperformstheold,theimprovementissignificantatthetail
ofthedistribution.
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Notas:
11
Givenanumberoffactorsthataffectthesystemperformance,itisusefultoseparateouttheeffects
ofindividual factors.In Part IV on experimental design,techniques s to organize e experiments to
obtainmaximuminformationwithaminimumnumberofexperimentsarepresented.
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TCP THROUGHPUT. Th figure, left,plots the e throughput achieved by a mobile e during g a file
transferasafunctionofitsvelocity(speed).Itsuggeststhatthroughputincreaseswithmobility.The
rightplotshowsthesamedata,butnowthemobilesareseparatedintwogroups:onegroup(‘s’)is
usingasmallsocketbuffer(4KBytes),whereasthesecond(‘L’)usesalargersocketbuffer(16K
Bytes).The conclusion is s now inverted: : throughput decreases with mobility. The hidden factor
influencesthefinalresult:allexperiments withlowspeedareforsmall socketbuffersizes.The
socketbuffersizeisahiddenfactor.
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13
In designing a a simulation n model,one e has to o select a language for simulation, select seeds and
algorithms for r random-number generation,decide the length of simulation run,and analyze the
simulationresults.
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14
Queueingmodelsarecommonlyusedforanalyticalmodelingofcomputersystems.Differenttypes
ofqueuesand
networks ofqueues are discussed d and and theiruse to answercommonlyaskedquestions s about
systemperformanceisdescribed.
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15
JOE’S KIOSK. Joe’s s e-kiosk k sells s online e videos to o customers equipped with smartphones. The
system is s made e of one servers and d one e 802.11 base station. Before deployment, , performance
evaluation tests s are performed, , as shown on Figure e (a). . We e see e that the e throughput t reaches a
maximumataround8transactionspersecond.
JoeconcludesthatthebottleneckisthewirelessLANanddecidestobuyandinstall2morebase
stations.Afterinstallation,theresultsareonFigure(b).Surprisingly,thereisnoimprovement.
Theconclusionthatthewireless LANwasthebottleneckwas wrong.Joescratches hisheadand
decidestogomorecarefullyaboutconclusions.MeasurementsaretakenonthewirelessLAN;the
number of collisions s is less than n 0.1%,and the utilization is s below5%. . This s confirms that the
wirelessLANisnotabottleneck.Joemakesthehypothesisthatthebottleneckmaybeontheserver
side.Afterdoublingtheamountofrealmemoryallocatedtotheserverprocess,theresults areas
shownonFigure(c).Thisconfirmsthatrealmemorywasthelimitingfactor.
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No Goals: Goals s are an important t part t of f all endeavors. Any endeavor without goals is bound to
fail.Performance evaluationprojects are no exception. The need fora goal may sound obvious, but many
performanceeffortsarestartedwithoutanycleargoals.Aperformanceanalyst,forexample,isroutinelyhired
alongwiththedesignteam.Theanalystmaythenstartmodelingorsimulatingthedesign.Whenaskedaboutthe
goals,theanalyst’sanswertypicallyisthatthemodelwillhelpanswera
designquestionsthatmayarise.Settinggoalsisnotatrivialexercise.Sincemostperformanceproblems are
vaguewhenfirstpresented,understandingtheproblemsufficientlytowriteasetofgoalsisdifficult.Oncethe
problemisclearandthegoalshavebeenwrittendown,findingthesolutionisofteneasier.
BiasedGoals:Anothercommonmistakeisimplicitorexplicitbiasinstatingthegoals.If,forexample,thegoal
is “toshowthatOURsystemis betterthanTHEIRS,”theproblembecomes thatoffindingthemetricsand
workloadssuchthatOURsystemturnsoutbetterratherthanthatoffindingtherightmetricsandworkloadsfor
comparingthetwosystems.Oneruleofprofessionaletiquetteforperformanceanalystsistobeunbiased.The
performanceanalyst’sroleislikethatofajury.Donot
haveanypreconceivedbiasesandbaseallconclusionsontheresultsoftheanalysisratherthanonpurebeliefs.
UnsystematicApproach:Oftenanalystsadoptanunsystematicapproachwherebytheyselectsystemparameters,
factors,metrics,andworkloads arbitrarily.This leadstoinaccurateconclusions.Thesystematicapproachto
solvingaperformanceproblemistoidentifyacompletesetofgoals,systemparameters,factors,metrics,and
workloads.
AnalysiswithoutUnderstandingtheProblem:Inexperiencedanalystsfeelthatnothingreallyhasbeenachieved
untilamodelhasbeenconstructedandsomenumericalresultshavebeenobtained.Withexperience,theylearn
thatalargeshareoftheanalysiseffortgoesintodefiningaproblem.Thisshareoftentakesupto40%ofthe
totaleffort.Thissupportstheoldsaying:Aproblemwellstatedishalf
solved.Oftheremaining60%,alargesharegoesintodesigningalternatives,interpretationoftheresults,and
presentationofconclusions.Developmentofthemodelitselfisasmallpartoftheproblem-solvingprocess.
IncorrectPerformanceMetrics:Ametricreferstothecriterionusedtoquantifytheperformanceofthesystem.
Examplesofcommonlyusedperformance metrics arethroughput andresponsetime. Thechoice ofcorrect
performance metrics depends upon n the services provided by the e system m or subsystem being modeled. By
manipulatingthemetrics,itispossibletochangetheconclusionsofaperformancestudy.Acommonmistakein
selectingmetricsisthatanalystsoftenchoosethosethatcanbeeasilycomputedormeasuredratherthanthe
onesthatarerelevant.Metricsthataredifficulttocomputeareignored.
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UnrepresentativeWorkload:Theworkloadusedtocomparetwosystemsshouldberepresentativeoftheactualusageofthe
systems in the field.Forexample,ifthepacketsin networks aregenerally a mixtureoftwosizes—short and long—the
workloadtocomparetwonetworksshouldconsistofshortandlongpacketsizes.Thechoiceoftheworkloadhasasignificant
impactontheresultsofaperformancestudy.Thewrongworkloadwillleadtoinaccurateconclusions.
Wrong Evaluation Technique: There are three evaluation techniques: measurement,simulation,and d analytical l modeling.
Analystsoftenhaveapreferenceforoneevaluationtechniquethattheyuseforeveryperformanceevaluation problem.For
example,thoseproficientinqueueingtheorywilltendtochangeeveryperformanceproblemtoaqueueingproblemevenifthe
system istoo complex andiseasilyavailableformeasurement.Thoseproficientinprogrammingwilltend tosolveevery
problembysimulation.Thismarriagetoasingletechniqueleadstoamodelthattheycanbestsolveratherthantoamodelthat
canbestsolvetheproblem.Theproblemwiththesetransformationsisthattheymayintroducephenomenaintothemodelthat
werenotpresentintheoriginalsystemortheymayleaveoutsomeimportantphenomenathatwereintheoriginalsystem.
Ananalystshouldhaveabasicknowledgeofallthreetechniques.Thereareanumberoffactorsthatshouldbeconsideredin
selectingtherighttechnique.
OverlookingImportantParameters:Itisagoodideatomakeacompletelistofsystemandworkloadcharacteristicsthataffect
theperformanceofthesystem.Thesecharacteristicsarecalledparameters.Workloadparametersmayincludethenumberof
users,requestarrivalpatterns,priority,andsoon.Theanalystcanchooseasetofvaluesforeachoftheseparameters;thefinal
outcomeofthestudydependsheavilyupon thosechoices.Overlookingoneormoreimportantparameters mayrenderthe
resultsuseless.
IgnoringSignificantFactors:Parametersthatarevariedin thestudy arecalledfactors.Forexample,amongtheworkload
parameterslistedaboveonlythenumberofusersmaybechosenasafactor;otherparametersmaybefixedattheirtypical
values.Notallparametershavean equaleffecton theperformance.Itisimportantto identifythoseparameters,which,if
varied,willmakeasignificantimpactontheperformance.Unlessthereisreasontobelieveotherwise,theseparametersshould
be
usedasfactorsintheperformancestudy.Factorsthatareunderthecontroloftheenduser(ordecisionmaker)andcanbe
easilychangedbytheendusershouldbegivenpreferenceoverthosethatcannotbechanged.Donotwastetimecomparing
alternativesthattheendusercannotadopteitherbecausetheyinvolveactionsthatareunacceptabletothedecisionmakersor
becausetheyarebeyondtheirsphereofinfluence.
InappropriateExperimentalDesign:Experimentaldesignrelatestothenumberofmeasurementorsimulationexperimentsto
beconductedandtheparametervaluesusedineachexperiment.Properselectionofthesevaluescanleadtomoreinformation
fromthesamenumberofexperiments.Improperselectioncanresultinawasteoftheanalyst’stimeandresources.Innaive
experimentaldesign,eachfactorischangedonebyone.This“simpledesign”mayleadtowrongconclusionsiftheparameters
interactsuchthattheeffectofoneparameterdependsuponthevaluesofotherparameters.
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18
InappropriateLevelofDetail:Thelevelofdetailusedinmodelingasystemhasasignificantimpact
on the e problem formulation. Avoid formulations that are either too narrow or too broad. For
comparing alternatives s that t are e slight t variations s of a common approach, , a a detailed model that
incorporates the variations maybe more usefulthan n a high-level model.Ontheother hand,for
comparing alternatives s that are very different, simple e high-level models may allow several
alternatives tobeanalyzedrapidly y andinexpensively.Acommonmistake istotakethe detailed
approachwhenahigh-levelmodelwilldoandviceversa.Itisclearthatthegoalsofastudyhavea
significantimpactonwhatismodeledandhowitisanalyzed.
NoAnalysis:Oneofthecommonproblemswithmeasurementprojectsisthattheyareoftenrunby
performanceanalysts whoaregood d inmeasurementtechniques s butlackdataanalysis expertise.
Theycollectenormousamountsofdatabutdonotknowhowtoanalyzeorinterpretit.Theresultisa
setofmagnetictapes(ordisks)fullofdatawithoutanysummary.Atbest,theanalystmayproducea
thickreportfullofrawdataandgraphs withoutanyexplanationofhowonecanuse theresults.
Therefore,itisbettertohaveateamofperformanceanalystswithmeasurementaswellasanalysis
background.
No Sensitivity Analysis: Often analysts s put t too o much emphasis s on the e results of their analysis,
presentingitasfactratherthanevidence.Thefactthattheresultsmaybesensitivetotheworkload
andsystemparametersisoftenoverlooked.Withoutasensitivityanalysis,onecannotbesureifthe
conclusionswouldchangeifthe analysis was doneina slightlydifferentsetting.Also,withouta
sensitivityanalysis,itisdifficulttoaccesstherelativeimportanceofvariousparameters.
IgnoringErrors in n Input: Oftentheparameters ofinterest cannotbe measured.Instead,another
variablethatcanbemeasuredisusedtoestimatetheparameter.Suchsituationsintroduceadditional
uncertainties in n theinputdata.The analystneeds s toadjustthe levelofconfidenceonthemodel
outputobtainedfromsuchdata.
Notas:
19
AssumingNoChangeintheFuture:Itisoftenassumedthatthefuturewillbethesameasthepast.A
modelbasedontheworkloadandperformanceobservedinthepastisusedtopredictperformancein
the future.The futureworkload and systembehaviorisassumedtobe the same as that already
measured.Theanalystandthedecisionmakersshoulddiscussthisassumptionandlimittheamount
oftimeintothefuturethatpredictionsaremade.
Ignoring Variability: It is s common to analyze only the mean performance since determining
variabilityis oftendifficult, , ifnotimpossible.Ifthe e variability is high,themean alone may be
misleadingtothedecisionmakers.Forexample,decisionsbasedonthedailyaveragesofcomputer
demandsmaynotbeusefuliftheloaddemandhaslargehourlypeaks,whichadverselyimpactuser
performance.
TooComplexAnalysis:Giventwoanalysesleadingtothesameconclusion,onethatissimplerand
easiertoexplainisobviouslypreferable.Performanceanalystsshouldconveyfinalconclusionsinas
simpleamanneraspossible.Someanalystsstartwithcomplexmodelsthatcannotbesolvedora
measurementorsimulationprojectwithveryambitiousgoalsthatareneverachieved.Itisbetterto
start with simple models or experiments, , get some e results s or insights, and then introduce the
complications.
ImproperPresentationofResults:Theeventualaimofeveryperformancestudyistohelpindecision
making.Ananalysis thatdoes not produceanyusefulresultsisa failure,as istheanalysis with
resultsthatcannotbeunderstoodbythedecisionmakers.Thedecisionmakerscouldbethedesigners
ofa system,thepurchasers ofa system,orthe sponsors ofa project.Conveying (orselling)the
results ofthe e analysis to decision makers s is the e responsibility of the analyst. . This requires the
prudentuseof
words,pictures,andgraphstoexplaintheresultsandtheanalysis.Therightmetrictomeasurethe
performanceofananalystisnotthenumberofanalysesperformedbutthenumberofanalysesthat
helpedthedecisionmakers.
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