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1、UnderstandingUserBehaviorinLarge-ScaleVideo-on-DemandSystems∗HongliangYu†,DongdongZheng†,BenY.Zhao§andWeiminZheng††ComputerScienceDepartment,TsinghuaUniversity,BeijingChina§ComputerScienceDepartment,U.C.SantaBarbara,CA,USA†§{hlyu@,zdd03@mails.,zwm-dcs@}tsinghua.edu.cn,ra
2、venben@cs.ucsb.eduABSTRACTvideorentalcompanies(Netflix)andothermediacompanies(TiVo)havebeendevelopingonlinestreamingvideosystemsforanin-Video-on-demandoverIP(VOD)isoneofthebest-knownexam-creasinglydemandingandgrowingconsumerpopulation[24].Byplesof“next-generation”Internet
3、applicationscitedasagoalbyleveragingtheincreasingavailabilityofbroadbandaccess,VODnetworkingandmultimediaresearchers.Withoutempiricaldata,systemsofferuserstheabilitytobrowse,select,view,andscanresearchershavegenerallyreliedonsimulatedmodelstodrivetheirthroughmediacontent
4、fromalargecontentrepositoryonanon-designanddevelopmentalefforts.Inthispaper,wepresentoneofdemandbasis,allfromthecomfortoftheirhomes.WithrecentthefirstmeasurementstudiesofalargeVODsystem,usingdatastudies[16,4,19]thatshowasignificantshiftinInternettrafficcovering219daysandmor
5、ethan150,000usersinaVODsystemfromthewebtomultimediacontent,itisclearthatboththeneces-deployedbyChinaTelecom.Ourstudyfocusesonuserbehav-sarycapacityanddemandforstreamingmultimediahavearrived.ior,contentaccesspatterns,andtheirimplicationsonthedesignofWhilecurrentVODsystems
6、remainintheprototypeordesignmultimediastreamingsystems.Ourresultsalsoshowthatwhenstages,akeychallengecompaniesfaceishowtodesignanarchitec-usedtomodeltheuser-arrivalrate,thetraditionalPoissonmodelturethatscalessmoothlytoalargenumberofcustomers,allwhileisconservativeandove
7、restimatestheprobabilityoflargearrivalmaintaininglowaccesslatency,highvideoqualityandreasonablegroups.WeintroduceamodifiedPoissondistributionthatmoreoperationalcosts.Designersusesimulationsbasedoncommonas-accuratelymodelsourobservations.Wealsoobserveasurpris-sumptionstoev
8、aluateanddrivetheirarchitectures.Unfortunately,ingresult,thatvideosessionlengthshasaweakinversecorrelat
