Storage device performance prediction with CART models(6)

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

FeatureCART

high(505%)

Neuralnetworks

fair(66%)

Poor

Fair

Poor

Poor

fast(seconds)

slow(hours)

fast

(milliseconds)

low(60B)

low(2MB)

Fair

k-nearestneighbors

InterpretabilityAbilitytohandleirrelevantinput

GoodGood

PoorPoor

PredictiontimeEaseofuse

fast

(milliseconds)

Good

slow(minutes)Fair

Table1:Comparisonofregressiontoolsinpredictingper-requestresponsetime.(ThesamedatasetisusedinFigure5.)Thecomparisononrow2,3,4andthelastoneistakenfrom[16].Werankthefeaturesintheorderoftheirimportance.Interpretabilityisthemodel’sabilitytoinfertheimportanceofinputvariables.Robustnessistheabilitytofunctionwellundernoisydataset.Irrelevantinputreferstofeaturesthathavelittlepredictivepowers.

buildtherequest-leveldevicemodelasdescribedinSection4.2.Themodelswereconstructedonthe rstdayofcello99aandtestsrunonthesecondofthesametrace.TheinformaiononthetracesweusedmaybefoundinSection5.

Themodel[29]usesalinearfunctionofXtoapproximatefX.Duetonon-linearstoragedevicebehavior,linearmodelshavepooraccuracy.

Themodel[26]consistsofasetofhighlyinterconnectedprocessingelementsworkinginunisontoapproximatethetargetfunction.Weuseasinglehiddenlayerof20nodes(bestamong20and40)andalearningrateof0.05.Halfofthetrainingsetisusedinbuildingthemodelandtheotherhalfforvalidation.Suchamodeltakesalongtimetoconverge.

The[6]mapstheinputdataintoahighdimensionalspaceandperformsalinearregressionthere.Ourmodelusestheradialbasisfunction

Kxix

expγx

xi

2

asthekernelfunction,andγissettobe2(bestamong1,3,4,6).Weuseanef cientimplementation,SVMlight[18],inourexperiment.Selectingtheparametervaluesrequiresexpertiseandmultipleroundsoftrials.

Themodel[9]ismemory-basedbecausethemodelremembersallthetrain-ingdatapointsandpredictionisdonethroughaveragingtheoutputoftheknearestneighborsofthedatapointbeingpredicted.WeusetheEuclideandistancefunctionandakvalueof5(bestamong5,10,15,and20).Themodelisaccurate,butisinef cientinstorageandcomputation.

Thelastthreetoolsrequirethatallthefeaturesandoutputbenormalizedtotheunitlength.Forfeaturesoflargevaluerange,wetakelogarithmsbeforenormalization.Overall,CARTisthebestatpredictingper-requestresponsetimes,withtheonlydownsidebeingslightlyloweraccuracycomparedtothemuchmorespace-andtime-consumingapproach.

…… 此处隐藏:451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(6).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