Storage device performance prediction with CART models(10)
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
Tracenamecello99acello99bcello99c
Length4weeks4weeks4weeks
Tracedescription
AverageSize
7.8Million
7.1KB118.0KB8.5KB
1.1Million
singledisk
35.4%
115.71ms113.61ms5.04ms
99.9%
7.40ms59.28ms
Table2:Tracesummary.WemodelanAtlas10K9GBandaRAID5diskarrayconsistingof8Atlas10Kdisks.TheresponsetimeiscollectedbyreplayingthetracesonDiskSim3.0[5].
Traces.Weusethreesetsofreal-worldtracesinthisstudy.Table2liststhesummarystatisticsoftheeditedtraces.The rsttwo,cello92andcello99capturetypicalcomputersystemresearchI/Oworkloads,collectedatHPLabsin1992and1999respectively[27,14].Wepreprocesscello92toconcatenatetheLBNsofthethreemostactivedevicesfromthetraceto llthemodeleddevice.Forcello99,wepickthethreemostactivedevices,amongthe23devices,andlabelthemcello99a,cello99b,andcello99c.Thecello99traces tina9GBdiskperfectly,sonotraceeditingisnecessary.Asthesetracesarelong(twomonthsforcello92andoneyearforcello99),wereportdataforafour-weeksnapshot(5/1/92to5/28/92and2/1/99to2/28/99).
TheSAPtracewascollectedfromanOracledatabaseserverrunningSAPISUCCS2.5Binapowerutilitycompany.Theserverhasmorethan3,000usersanddiskaccessesre ecttheretrievalofcustomerinvoicesforupdatingandreviewing.SequentialreadsdominatetheSAPtrace.
Evaluationmethodology.Theevaluationusesthedevicemodelstopredicttheaverageand90thper-centileresponsetimeforone-minuteworkloadfragments.Wereportthepredictionerrorsusingtwometrics:
Y,andrelativeerrorabsoluteerrorde nedasthedifferencebetweenthepredictedandtheactualvalue,Y
de nedasYY
下一篇:中国食物成分表(全)2010版