Storage device performance prediction with CART models(7)
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
Figure2:Modelconstructionthroughtraining.RTiistheresponsetimeofrequestri.
4PredictingPerformancewithCART
ThissectionpresentstwowaysofconstructingdevicemodelsbasedonCARTmodels.
4.1Overview
OurgoalistobuildamodelforagivenstoragedevicewhichpredictsdeviceperformanceasafunctionofI/Oworkload.Thedevicemodelreceivesaworkloadasinputandpredictsitsaggregateperformance.Wede neaworkloadasasequenceofdiskrequests,witheachrequest,ri,uniquelydescribedbyfourattributes:arrivaltime(ArrivalTimei),logicalblocknumber(LBNi),requestsizeinnumberofdiskblocks(Sizei),andread/writetype(RWi).Thestoragedevicecouldbeasingledisk,adiskarray,orsomeotherlike-interfacedcomponent.Theaggregateperformancecanbeeithertheaverageorthe90-thpercentileresponsetime.
OurapproachusesCARTtoapproximatethefunction.Weassumethatthemodelconstructionalgorithmcanfeedanyworkloadintothedevicetoobserveitsbehaviorforacertainperiodoftime,alsoknownas“training.”Thealgorithmthenbuildsthedevicemodelbasedontheobservedresponsetimes,asillustratedinFigure2.Modelconstructiondoesnotrequireanyinformationabouttheinternalsofthemodeleddevice.Therefore,itisgeneralenoughtomodelanydevice.
Regressiontoolsareanaturalchoicetomodeldevicebehavior.Suchtoolsaredesignedtomodelfunc-tionsonmulti-dimensionalspacegivenasetofsampleswithknownoutput.Thedif cultyistotransformworkloadsintodatapointsinamulti-dimensionalfeaturespace.Weexploretwowaystoachievethetrans-formationasillustratedinFigure3.Arequest-levelmodelrepresentsarequestriasavectorRi,alsoknownasthe“requestdescription,”andusesCARTmodelstopredictper-requestresponsetimes.Theaggregateperformanceisthencalculatedbyaggregatingtheresponsetimes.Aworkload-levelmodel,ontheotherhand,representstheentireworkloadasasinglevectorW,orthe“workloaddescription,”andpredictstheaggregateperformancedirectlyfromW.Inbothapproaches,thequalityoftheinputvectorsiscriticaltothemodelaccuracy.Thenexttwosectionspresenttherequestandworkloaddescriptionsindetail.
4.2Request-LevelDeviceModels
ThissectiondescribestheCART-basedrequest-leveldevicemodel.ThismodelusesaCARTmodeltopredicttheresponsetimesofindividualrequestsbasedonrequestdescriptions.Themodel,therefore,isabletogeneratetheentireresponsetimedistributionandoutputanyaggregateperformancemeasures.
Weadoptthefollowingtwoconstraintsindesigningtherequestdescription.1.Ridoesnotincludeanyactualresponsetimes.Onecouldrelaxthisconstraintbyallowingthein-clusionoftheresponsetimeinformationforalltherequeststhathavealreadybeenservedwhenthecurrentrequestarrives.Thisrelaxation,however,isfeasibleonlyforonlineresponsetimepredictions;itwouldnotbeappropriateforapplicationplanningtasksbecausetheplannerdoesnotrunworkloadsondevices.
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