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简介:Accuratepredictionofserverloadisimportanttocloudsystemsforimprovingtheresourceutilization,reducingtheenergyconsumptionandguaranteeingthequalityofservice(QoS).Thispaperanalyzesthefeaturesofcloudserverloadandtheadvantagesanddisadvantagesoftypicalserverloadpredictionalgorithms,integratesthecloudmodel(CM)andtheMarkovchain(MC)togethertorealizeanewCM-MCalgorithm,andthenproposesanewserverloadpredictionalgorithmbasedonCM-MCforcloudsystems.Thealgorithmutilizesthehistoricaldatasampletrainingmethodofthecloudmodel,andutilizestheMarkovpredictiontheorytoobtainthemembershipdegreevector,basedonwhichtheweightedsumofthepredictedvaluesisusedforthecloudmodel.Theexperimentsshowthattheproposedpredictionalgorithmhashigherpredictionaccuracythanothertypicalserverloadpredictionalgorithms,especiallyifthedatahassignificantvolatility.TheproposedserverloadpredictionalgorithmbasedonCM-MCissuitableforcloudsystems,andcanhelptoreducetheenergyconsumptionofclouddatacenters.