Storage device performance prediction with CART models(19)
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
12000
Number of Requests
9000
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Time (aggregated in 10 seconds)
Disk Blocks (aggregated in 1000 blocks)
20
Disk block number
Entropy value
15 10 5 0
Entropy on timeEntropy on LBNJoint entropyCorrelation
60000
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20000
Arrival time
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10Scale
15 20
Entropyplotonone-dimensionaldatasets.Theone-dimensionalentropyplotcapturescharacteristicsofindividualattributes,suchasthetemporalandspatialburstiness.Thesetwotypesofburstinessmeasurestheburstinessinthearrivalprocessandtheskewinaccessfrequenciesofdiskblocks.Weusetheentropyplotforarrivaltimeasanexampletoshowhowtheentropyplotworks.
Givenaworkload,wecanderiveits“margin”onthearrivaltimebycountingthenumberofrequeststhatarriveintothesystemateachtimetick.ThetopgraphofFigure9(a)showsthesampletrace’smarginonarrivaltime.
2n.WecalculatetheAssumethatthetraceis2ntimetickslong,andthemarginisCii12
entropyvalueatscalekbyapplyingtheentropyfunctionontheaggregatedmarginatscalek.TheaggregatedmarginisCkjj122kwhere
Intuitively,theentirelengthofthemarginisdividedinto2kequi-lengthedintervalsatscalek.Thus,applyingtheentropyfunctiononCkgives
where
Pkj
Number of Requests
(a)Sampledisktrace(b)Entropyplot
Figure9:Asampledisktraceanditsentropyplot.
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∑C2n
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∑Pkjlog2Pkj
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