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1、MR-Scope:AReal-TimeTracingToolforMapReduceDachuanHuang,XuanhuaShi,ShadiIbrahim,LuLu,HongzhangLiu,SongWu,HaiJinClusterandGridComputingLabServicesComputingTechniqueandSystemLabHuazhongUniversityofScienceandTechnology,Wuhan,430074,Chinahjin@hust.edu.cnABSTRACTperformanceofMapReduce-basedapplication,im
2、plementingMapReduceprogrammingmodelisemergingasanefficienttoolMapReduceindifferentenvironments[11,14]andmakingfordata-intensiveapplications.Hadoop,anopen-sourceMapReducesuitableforperformingwiderrangeofapplicationimplementationofMapReduce,hasbeenwidelyadoptedandsuchasscientificapplications[15]orcom
3、putingintensiveexperiencedbybothacademiaandenterprise.Recently,lotsofapplication.effortshavebeendoneonimprovingtheperformanceofDuetothesimplicityoftheMapReduceparadigm,itiseasytoMapReducesystemandonanalyzingtheMapReduceprocesswriteMapReduceprogram,butitisstillhardandcumbersometobasedonthelogfilesge
4、neratedduringtheHadoopexecution.debugitandgetaclearunderstandingbeneaththeveil.ForVisualizinglogfilesseemstobeaveryusefultooltounderstandexample,launchingatypicalMapReducejobwillinvolveahugethebehavioroftheHadoopprocess.Inthispaper,wepresentMR-numberofdistributedprocessesandthreadswhosenumberequals
5、Scope,areal-timeMapReducetracingtool.MR-Scopeprovidesatoapproximatelytwotimesoftheparticipatedmachinespluseachreal-timeinsightoftheMapReduceprocess,includingtheon-sub-processbornoutofmaporreducetaskinslavenodes,whichgoingprogressofeverytaskhostedinTaskTracker.Inaddition,makesithardtomonitorandanaly
6、ze.Recently,manyuniversitiesitdisplaysthehealthoftheHadoopclusterdatanodes,theandenterpriseshaveestablishededucationalcoursesandtutorialsdistributionofthefilesystem'sblocksandtheirreplicasandthetohelppeopleunderstandtheMapReducealgorithms.Mochicontentofthedifferentblocksplitsofthefilesystem.Wesyste
7、misintroducedtovisualizethelogfiles[4]andauniversalimplementMR-ScopeinnativeHadoop0.1.ExperimentalresultstracingframeworkX-TraceisusedtotraceHadoopworkflow[5].demonstratthatMR-Scope’soverheadislessthan4%whe
