Storage device performance prediction with CART models(4)
时间:2025-07-10
时间:2025-07-10
Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as data assignment. This work explores the application of a machine learning tool, CART models, to storage device modeling. Our appr
usingsimulatorsisalsoresourceintensive.Analyticalmodels[7,22,24,30,31]aremorecomputationallyef cientbecausethesemodelsdescribedevicebehaviorwithasetofformulae.Findingtheformulasetrequiresdeepunderstandingoftheinteractionbetweenstoragedevicesandworkloads.Inaddition,bothdisksimulatorsandanalyticalmodelsaretightlycoupledwiththemodeleddevice.Therefore,newdevicetechnologiesmayinvalidateexistingmodelsandrequireanewroundofmodelbuilding.
OurapproachusesCART,whichtreatsstoragedevicesasblackboxes.Asaresult,themodelconstruc-tionalgorithmisfullyautomatedandshouldbegeneralenoughtohandleanytypeofstoragedevice.Thedegenerateformsof“black-boxmodels”areperformancespeci cations,suchasthemaximumthroughputofthedevices,publishedbydevicemanufacturers.Theactualperformance,however,willbenowherenearthesenumbersundersomeworkloads.Anderson’s“table-based”approach[3]includesworkloadcharacter-isticsinthemodelinput.Thetable-basedmodelsrememberdevicebehaviorforawiderangeofworkloadanddevicepairsandinterploatesamongtablesentriesinpredicting.Anderson’smodelsareusedinanautomatedstorageprovisiontool,Ergastulum[2],whichformulatesautomaticstorageinfrastructureprovi-sioningasanoptimizationproblemandusesdevicemodelstoguidethesearchalgorithminlocatingthesolution.Ourapproachimprovesonthetable-basedmodelsbyemployingmachinelearningtoolstocapturedevicebehavior.Becauseofthegoodscalabilityofthetoolstohighdimensionaldatasets,weareabletousemoresophisticatedworkloadcharacteristicsasthemodelinput.Asaresult,themodelsaremoreef cientinbothcomputationandstorage.
Workloadcharacterizationisanimportantpartofdevicemodelingbecauseitprovidesasuitablerep-resentationofworkloads.Despiteabundantpublishedworkinmodelingwebtraf c[23,25,8],I/Otraf cmodelingreceiveslessattention.Directapplicationofwebtraf canalysismethodstoI/worktraf chasacategoricaladdressspace,andthereisnonotionofsequentialscans.Incontrast,theperformancevariabilitycanbeseveralordersofmagnitudebetweenrandomandsequentialaccessesforI/Oworkloads.Ganger[10]pointedoutthecomplexityofI/Oworkloads,andeventhedetectionofsequentialscansisahardproblem[19].Gomezetal.[14]identi edself-similarityinI/Otraf candadoptedstructuralmodelstogenerateI/Oworkloads.Kurmasetal.[20]employedaniterativeapproachtodetectimportantworkloadcharacteristics.Rome[34]providedageneralframeworkofworkloadspeci cations.Alltheapproaches,inonewayoranother,useempiricaldistribu-tionsderivedfromgivenworkloadsastheparametervalues.Ourpreviouswork[32]takesadvantageoftheself-similarityofI/Oworkloadsandproposesatool,the“entropyplot,”tocharacterizethespatio-temporalcharacteristicsofI/Oworkloadswiththreescalars.SinceourCART-basedmodelsrequireworkloadstobepresentedintheformofvectorsofscalars,theentropyplotisanattractivechoice.
3Background:CARTModels
ThissectiongivesabriefintroductionoftheCARTmodelsandjusti esourchoiceofthetool.AdetaileddiscussionofCARTisavailablein[4].
3.1CARTModels
CARTmodelingisamachinelearningtoolthatcanapproximaterealfunctionsinmulti-dimensionalCarte-fXε,sianspace.(Itcanalsobethoughtofasatypeofnon-linearregression.)GivenafunctionY
dwhereX ,Y ,andεiszero-meannoise,aCARTmodelapproximatesYusingapiece-wiseconstant f X.WerefertothecomponentsofXasfeaturesinthefollowingtext.Theterm,ε,capturesfunction,Y
theintrinsicrandomnessofthedataandthevariabilitycontributedbytheunobservablevariables.Thevari-anceofthenoisecouldbedependentonX.Forexample,thevarianceofresponsetimeoftendependsonthearrivalrate.
…… 此处隐藏:1669字,全部文档内容请下载后查看。喜欢就下载吧 ……下一篇:中国食物成分表(全)2010版