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1、200814thIEEEInternationalConferenceonParallelandDistributedSystemsMRBench:ABenchmarkforMap-ReduceFrameworkKiyoungKim,KyunghoJeon,HyuckHan,Shin-gyuKim,HyungsooJung,HeonY.YeomSchoolofComputerScienceandEngineeringSeoulNationalUniversitySeoul151-742,Korea{kykim,khjeon,hhyuck,sgkim,jhs,yeom}@dcsla
2、b.snu.ac.krAbstractdatadistribution,parallelprocessing,resultscollectionwithfaulttolerance.TherearesomesolutionssuchasMapReduceisGoogle’sprogrammingmodelforeasyMPIandGridforparallelcomputing,butitisnoteasydevelopmentofscalableparallelapplicationswhichpro-tosolveproblemswithbothefficiencyandconv
3、enience.cesshugequantityofdataonmanyclusters.DuetoMapReduce[7]isGoogle’sprogrammingmodelitsconveniencyandefficiency,MapReduceisusedinforscalableparalleldataprocessing,andvariousvariousapplications(e.g.,websearchservicesandon-implementations[7,11,1]ofMapReduceareusedtolineanalyticalprocessing.)H
4、owever,thereareonlyfewsolveproblemsinmanyareas(i.e.,websearchser-goodbenchmarkstoevaluateMapReduceimplementa-vice.)MapReduceusestwofunctions-MapandRe-tionsbyrealistictestsets.duce-fromtheparallelizablefunctions.Usersim-Inthispaper,wepresentMRBenchthatisabench-plementonlyMapfunctionandReducefu
5、nctiontomarkforevaluatingMapReducesystems.MRBenchfo-converttheirprogramstoparallelizedversionseasily.cusesonprocessingbusinessorientedqueriesandcon-TherearemanyimplementationsofMapReducesuchcurrentdatamodifications.Tothisend,webuildMR-asMap-Reduce-Merge[6],MapReduceforMulti-coreBenchtodealwith
6、largevolumesofrelationaldataandandMultiprocessorsystems[11],GoogleMapReduce,executehighlycomplexqueries.ByMRBench,usersandApacheHadoop[1].canevaluatetheperformanceofMapReducesystemsEvenifMapReduceimplementationsarealreadybe-whilevaryingenvironmentalparameterssuchasdataingusedwidely,thereareon
7、lyafewwaystomeasuresizeandthenumberof(Map/Reduce)tasks.Ourex-performanceofMapReduceimplementations.TheretensiveexperimentalresultsshowthatMRBenchisaaresomeresearches[7,11,1]whichtriedtobench-usefultooltobenchmarkthecapabilityofansweringmarkMa
