Storage device performance prediction with CART models(8)
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
Figure3:TwotypesofCART-baseddevicemodels.
2.Ricanbecalculatedfromrj,ji.Thisconstraintsimpli estherequestdescription.Inmostcases,theresponsetimeofacurrentrequestdependsonlyonpreviousrequestsandtherequestitself.
OurrequestdescriptionRiforrequestricontainsthefollowingvariables:
Ri
TimeDiffi1
TimeDiffik
LBNi
LBNDiffi1
LBNDiffil
Sizei
RWi
Seqi
whereTimeDiffikArrivalTimeiArrivalTimei2k1andLBNDiffilLBNiLBNil.The rstthreegroupsoffeaturescapturethreecomponentsoftheresponsetime,andSeqiindicateswhethertherequestisasequentialaccess.The rstk1featuresmeasurethetemporalburstinessoftheworkloadwhenriarrives,andsupportpredictionofthequeuingtime.WeallowtheTimeDifffeaturestoexponentiallygrowthedistancefromthecurrentrequesttohistoryrequesttoaccommodatelargebursts.Thenextl1featuresmeasurethespatiallocality,supportingpredictionoftheseektimeoftherequest.SizeiandRWisupportpredictionofthedatatransfertime.
Thetwoparameters,kandl,determinehowfarwelookbackforrequestburstsandlocality.Smallvaluesdonotadequatelycapturethesecharacteristics,rgevalues,ontheotherhand,leadstoahigherdimensionality,meaningtheneedforalargertrainingsetandalongertrainingtime.Theoptimalvaluesfortheseparametersarehighlydevicespeci c,andSection5.1showshowweselecttheparametervaluesinourexperiments.
4.3Workload-LevelDeviceModels
Theworkload-levelmodelrepresentstheentireworkloadasasingleworkloaddescriptionandpredictsaggregatedeviceperformancedirectly.TheworkloaddescriptionWcontainsthefollowingfeatures.
W
Averagearrivalrate
Readratio
Averagerequestsize
Percentageofsequentialrequests
Temporalandspatialburstiness
Correlationsbetweenpairsofattributes
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