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1、Self-SimilarTrafficandNetworkDynamicsASHOKERRAMILLI,MEMBER,IEEE,MATTHEWROUGHAN,MEMBER,IEEE,DARRYLVEITCH,MEMBER,IEEE,ANDWALTERWILLINGER,MEMBER,IEEEInvitedPaperOneofthemostsignificantfindingsoftrafficmeasurementonthetrafficflowsinthenetwork,andthemannerinstudiesoverthelastdecade
2、hasbeentheobservedself-similaritywhichtheyinteractwithnetworkelements.However,trafficinpacketnetworktraffic.Subsequentresearchhasfocusedonthemeasurementstudiesoverthepast10orsoyearshavecon-originsofthisself-similarity,andthenetworkengineeringsignif-tinuedtodemonstrateourlimite
3、dunderstandingofactualicanceofthisphenomenon.Thispaperreviewswhatiscurrentlyknownaboutnetworktrafficself-similarityanditssignificance.Wenetworktrafficbyrevealing“emergent”phenomena—mea-thenconsideramatterofcurrentresearch,namely,themannerinsurement-drivendiscoveriesaboutthedyn
4、amicnatureofwhichnetworkdynamics(specifically,thedynamicsoftransmissionactualnetworktrafficthatcomeasacompletesurprise,defycontrolprotocol(TCP),thepredominanttransportprotocolusedinconventionalwisdom,andcannotbeexplainednorpredictedtoday’sInternet)canaffecttheobservedself-simi
5、larity.Tothisend,withintheframeworkofthetraditionallyconsideredtrafficwefirstdiscusssomeofthepitfallsassociatedwithapplyingtra-ditionalperformanceevaluationtechniquestohighly-interacting,models.Anexampleofsuchanemergentphenomenonwaslarge-scalenetworkssuchastheInternet.Wethenpr
6、esentonethediscoveryoftheself-similarorfractalnatureofnetworkpromisingapproachbasedonchaoticmapstocaptureandmodeltraffic[18],[42],[54].thedynamicsofTCP-typefeedbackcontrolinsuchnetworks.NotThefocusofthispaperistheobservedscalingbehaviorofonlycanappropriatelychosenchaoticmapmod
7、elscapturearangenetworktraffic,andtheuseofdynamicalsystemsmodelstoofrealisticsourcecharacteristics,butbycouplingthesetonetworkstateequations,onecanstudytheeffectsofnetworkdynamicsonunderstandhowfeedbackmechanismsaffectit.Inparticular,theobservedscalingbehavior.Weconsidersevera
8、laspectsofTCPwesurveyherethedevelopmentofself-similartraffica
