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1、RESEARCHONOPTIMIZATIONOFCLOUDJOBSCHEDULINGSTRATEGYBASEDONFUZZYCLUSTERINGABSTRACTAsamajortechnicalsolutionofbigdataanddistributedcomputing,cloudcomputingprovidestheclusterresourcestotheendusersintheformofon-demandservices.Howtoallocateresourcesandschedulejobseff
2、ectivelyandreasonablyhavebecomethekeypointsanddifficulties.Itnotonlyhasaninfluenceontheefficiencyoncloudcomputingapplications,butalsohasadirectimpactontheoverallperformanceofcloudplatformandusersatisfaction.Inthecloudcomputingenvironment,clusternodesareheteroge
3、neous,diverse,uncertainandfuzzy,soitisdifficulttoaccuratelydescriberesourcesandjobs,anditleadstothecomplicationofon-demandresourceallocation.Resourceallocationandjobschedulingbasedonfuzzyclusteringhavebecomeahotresearchtopic.Astheclusterscaleisgettingenlarged,t
4、henumberofnodeshasalreadyreachedhundredsofthousands.Whendealingwithhigh-dimensionalandsuperhigh-dimensionalmatrixoperations,thetraditionalserialfuzzyclusteringalgorithmhastheproblemofhigh-amountcomputiation,loweffiency,computationspaceshortageandsoon,soit’sunab
5、letomeetthetimelinessrequirementsofresourceclusteringinthecloudcomputingenvironment.Therefore,optimizationandimprovementhavetobemadebasedontraditionalserialfuzzyclusteringalgorithm,inordertosuitcloudcomputingbetter.Firstly,studyonthetraditionalserialfuzzycluste
6、ringalgorithmintheIIIcloudenvironment,compareandanalyzethreetraditionalclusteringalgorithmsandsummarizetheirfeaturesandshortcomings.Anoptimizationandconcurrencystrategybasedonfuzzyequivalencerelationandfuzzysimilarityprocessing,itcanreducetheamountofcomputation
7、,improvecomputingefficiency,andshortenthetime-consuming.Experimentalresultsshow,comparedtotheexistingmethods,thestrategycaneffectivelysolvetheproblemofhighoverheadinthesmallandmedium-scalecloudresourceclusteringdivisionprocess.Secondly,inordertodealwiththeprobl
8、emofdifficultytodivideresourceclusteringcausedbyhigh-amountcomputationandmemoryshortageinthetraditionalfuzzyclusteringalgorithm,aconcurrencyfuzzyclusteringalgorithmbasedonMa
