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1、2013ProceedingsIEEEINFOCOMDecentralizingNetworkInferenceProblemswithMultiple-DescriptionFusionEstimation(MDFE)MehdiMalboubi,CuongVu,Chen-NeeChuahPuneetSharmaDept.ofElectrical&ComputerEngineeringHewlet-PackardLaboratories(HPLabs)UniversityofCalifornia,Davis,
2、USA.PaloAlto,California,USA.{mmalboubi,cnvu,chuah}@ucdavis.edupuneet.sharma@hp.comAbstract—Twoformsofnetworkinference(ortomography)usingunderlineddeterministicorstatisticalmodels[3],[4],problemshavebeenstudiedrigorously:(a)trafficmatrixesti-[7],[9].mationorc
3、ompletionbasedonlink-leveltrafficmeasurements,and(b)link-levellossordelayinferencebasedonend-to-Althoughtheuniquenessandaccuracyofthesolutionareendmeasurements.Theseproblemsareoftenposedasunder-important,manynetworkinferenceproblemsneedtobesolveddeterminedli
4、nearinverse(UDLI)problemsandsolvedinainatimelymannerforpracticaldeployment.Nevertheless,centralizedmanner,whereallthemeasurementsarecollectedatamostexistingstudiesattempttosolvethenetworkinferencecentralnode,whichthenappliesavarietyofinferencetechniquesprob
5、leminaone-shot,centralizedmanner,whereallmea-toestimatetheattributesofinterest.surementsarecollectedatacentralnode,whichthenappliesThispaperproposesanovelframeworkfordecentralizingdomain-specificinferencetechniquestoestimatetheattributestheselarge-scaleUDLIn
6、etworkinferenceproblemsbyintelli-ofinterest.gentlypartitioningitintosmallersub-problemsandsolvingthemindependentlyandinparallel.Theresultingestimates,referredtoThispapertacklesthesenetworkinferenceproblemsfromaasmultipledescriptions,canthenbefusedtogetherto
7、computethenewangleandasksthequestion:canwedesignanefficientandglobalestimate.WeapplythisMultipleDescriptionandFusionrobustframeworktosolvetheselarge-scaleUDLIproblemsEstimation(MDFE)frameworktothreeclassicalproblems:trafficinadecentralizedmanner?Ourgoalistosp
8、eedupthematrixestimation,trafficmatrixcompletion,andlossinference.computationprocesstoproducetimelyestimates(especiallyinUsingrealtopologiesandtraces,wedemonstratehowMDFEcanadynamicnetworkenvironment),w
