Storage device performance prediction with CART models(15)

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

200

# of intervals

1501005000%

25%50%75%100%% of sequential requests

(a)Histogramsonsequentiality(b)Averageabsoluteerror(c)Absoluteerrordistribution

Figure8:Effectsofdifferenttrainingworkloads.

6Conclusions

Storagedeviceperformancemodelingisanimportantelementinself-managedstoragesystemsandotherapplicationplanningtasks.Ourtargetmodeltakesaworkloadasinputandpredictsitsaggregateperfor-manceonthemodeleddeviceef cientlyandaccurately.Thispaperpresentsourinitialresultsinexploringmachinelearningtoolstobuilddevicemodels.Ablackboxpredictivetool,CART,makesdevicemodelsindependentofthestoragedevicesbeingmodeled,andthus,generalenoughtohandleanytypeofdevices.Themodelconstruction,alsoknownastraining,consistsoftwophases:replayingtracesonthedevicesandbuildingaCARTmodelbasedontheobservedresponsetimes.Modelinganewdeviceinvolvesonlytrainingonthetargetdevice.

CART-basedmodelstakeinputintheformofvectors,soworkloadsmustbetransformedintovectorsinordertouseCARTasthebasisfordevicemodels.Thispaperpresentstwowaystoaccomplishsuchatransformation,yieldingtwotypesofdevicemodels.Therequest-leveldevicemodelsrepresenteachrequestasavectorandpredictitsresponsetime.Asaresult,themodelsareabletopredicttheentireresponsetimedistribution.Theexperimentsshowthatthepredictedresponsetimehasademerit gureof33%foramodernUNIX leservertrace,leadingtoamedianrelativeerroraslowas16%foraggregateperformancepredictions.Theworkload-leveldevicemodels,ontheotherhand,transformaworkloadfragmentintoavectorandpredictitsaggregateperformancedirectly.Thevectortakesadvantageoftheef ciententropyplotmetrictocapturethetemporalandspatialburstinessaswellasthecorrelationswithinI/Oworkloads.Themedianrelativeerrorcanbeaslowas29%fortheworkload-leveldevicemodels.

Theerroranalysissuggeststhatthequalityofthetrainingworkloadsplaysacriticalroleinthemodelaccuracy.Themodelsareunabletopredictworkloadsthataredifferentfromthetrainingworkloads.Toaccuratelypredictarbitraryworkloads,itisimportantforthetrainingworkloadstobeasdiverseaspossibletocoverawiderangeofworkloads.Ourfutureworkwillexploretheeffectivenessofexistingsyntheticworkloadgeneratorsinproducinghigh-qualitytrainingworkloads.

Continuingresearchcanimprovethemodelpredictionaccuracy.First,ourexperimentsshowtherele-vanceoftrainingtraces.Generatingrulestoassistintrainingsuchmodelsbroadlyenoughwillbeimportant.Second,theworkloadcharacterizationproblempersists,affectingtheworkload-levelmodels.Webelieve,however,thatthecontextofferedbythemodelscanhelpproduceinsightintothislong-standingproblem.Third,thetwotypesofdevicemodelsshowdesirablepropertiesintrainingandpredicting,respectively.Itshouldbevaluabletohaveamodelthatcombinesthebestofbothapproaches.

…… 此处隐藏:913字,全部文档内容请下载后查看。喜欢就下载吧 ……
Storage device performance prediction with CART models(15).doc 将本文的Word文档下载到电脑

精彩图片

热门精选

大家正在看

× 游客快捷下载通道(下载后可以自由复制和排版)

限时特价:7 元/份 原价:20元

支付方式:

开通VIP包月会员 特价:29元/月

注:下载文档有可能“只有目录或者内容不全”等情况,请下载之前注意辨别,如果您已付费且无法下载或内容有问题,请联系我们协助你处理。
微信:fanwen365 QQ:370150219