Storage device performance prediction with CART models(13)
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
averageresponsetime
(b)Predictionerrorfor90thpercentileresponsetime
Figure6:Comparisonofpredictorsforasingle9GBAtlas10Kdisk.
Forexample,constantlyover-predictsforcello99cbecausethemodelwasnevertrainedwththesmallsequentialaccessesthatareparticulartocello99c.Section5.4givesaninformalerroranalysisandidenti esinadequatetrainingbeingthemostsigni canterrorsource.
Fourth,highquantileresponsetimesaremoredif culttopredict.Weobservelargerpredictionerrorsfromallthepredictorsfor90thpercentileresponsetimepredictionsthanforaverageresponsetimepredic-tions.TheaccuracyadvantageofthetwoCART-basedmodelsishigherfor90thpercentilepredictions.
Insummary,thetwoCART-basedmodelsgiveaccuratepredictionswhenthetrainingandtestingwork-loadssharethesamecharacteristicsandinterpolatewellotherwise.Thegoodaccuracysuggeststheeffec-tivenessoftherequestandworkloaddescriptionsincapturingimportantworkloadcharacteristics.
5.3ModelingADiskArray
Figure7comparestheaccuracyofthefourpredictorsinmodelingthediskarray.ThepredictorisnotpresentedbecausetheSAPtracedoesnotprovideenoughinformationonarrivaltimeforustoknowtheoffsetwithinaweek.Theoverallresultsaresimilartothoseforthesingledisk.ThetwoCART-basedmodelsarethemostaccuratepredictors.Theabsoluteerrorsbecomesmallerduetothedecreasedresponsetimefromthesingledisktothediskarray.Therelativeaccuracyamongthepredictors,however,staysthesame.Overall,theCART-baseddevicemodelingapproachworkswellforthediskarray.
5.4ErrorAnalysis
Thissectionpresentsaninformalerroranalysistoidentifythemostsigni canterrorsourcefortheCART-baseddevicemodels.
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