Juniper-IP 網(wǎng)絡(luò)中自動(dòng)容量?jī)?yōu)化的經(jīng)濟(jì)效益

TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

PeterFetterolf,Ph.D.

TheEconomicBenefitsofAutomatingCapacity

OptimizationinIPNetworks

EXECUTIVE

SUMMARY

Internettrafficcontinuestogrowatarapidrateyearoveryear.Trafficgrowthisdrivenbyrecenttechnologiessuchas5GandfibertothehomeandalsonewapplicationssuchasAR/VR,cloudgaming,andvideoconferencing.IPaggregationandcorenetworkscarrythebulkofInternettraffic,andtheyaretypicallydesignedasmeshnetworkswithmultiplepathsconnectinganoriginwithadestinationsite.IntraditionalIPnetworkswithshortestpathroutingprotocols,networksareparticularlygoodatreroutingtrafficaroundlinkornodefailuresbutareveryineffectiveatoptimizinglinkutilization.Manycommunicationserviceproviders(CSPs)haveusedabruteforceapproachtonetworkcapacityplanningbyallocatingextrabandwidthtolinkstoensurethereisadequatebandwidthavailabletosupportunexpectedburstsoftrafficorshort-termtrafficgrowth.Typically,CSPswillengineernetworklinkssuchthataverageutilizationis50%orless.

Asnetworklinksgrowfrom100GEto400GEandlargeritisbecomingmoreimportanttouseautonomouscapacityoptimizationtooptimizenetworklinkcapacity.Linksthatareunderutilizedcansupportmoretraffic;linksthatareoverutilizationshouldsupportlesstraffic.InthispaperwepresentasolutiontothisproblemusingtheJuniperNetworksParagonAutomation,whichisanetworkautomationsuitethatincludesaPathComputationEngine(PCE)thatsimplifiestrafficengineering,makingitpossibletoleveragebenefitsprovidedbytransportservicepaths,suchasMPLS/RSVP,segmentrouting,andnetworkslicing.Itenablesoperationteamstomanagestricttransportservicelevelagreements(SLAs)moreefficientlyanddynamicallythroughautomatedplanning,provisioning,proactivemonitoring,andoptimizationoflargetrafficloadsbasedonuser-definedconstraints.Withthisautomation,operatorscanruntheirnetworkshigherutilizationwhileachievingpredictability,resiliency,andSLAguaranteesinserviceproviders’,cloudproviders’,andlargeenterprises’networks.OurstudyshowsthatParagonAutomationcanhelpoperatorsincreaseaveragelinkutilizationfrom50%to70%orhigher.

Autonomouscapacityoptimizationisevenmoreimportanttodaybecausesiliconshortageshaveresultedinsupplychainproblems.IncreasingnetworkcapacityrequiresCSPstoordernewcomponentsthataredeliveredviathesupplychain.

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TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

Delaysinthesupplychaincouldresultininadequatenetworkcapacity,causingseriousnetworkperformanceproblemsaswellasSLAviolations.InthispaperwepresenttheresultsofanACG

businessmodelthatcomparestwoscenarios:

?WithParagonAutomation

?Usingbrute-forcecapacitymanagement

Thetotalcostofownership(TCO)andreturnoninvestment(ROI)modelcomparesthecapitalexpenseandoperationsexpensesofahypotheticalnetworkandshowssignificantsavingsusingaPCEtooptimizetrafficengineering.ThecostofnetworkbandwidthisexceedinglyhighsuchthatTCOsavingsinoptimizingthenetworkpayforParagonAutomationmanytimesover.OurresultsshowanoverallTCOsavingsof27%.Wealsoshowthatevenaminorincreaseinaveragenetworkutilizationof0.5%willpayforthetotalcostoftheinvestmentinParagonAutomation.

NetworkChallenges

Overthelast20yearstheimportanceoftheInternethascontinuedtogrow,andtodaytheInternetisanessentialutilityformostbusinesses,households,andconsumers.ThePandemichasonlyamplifiedtheimportanceofInternetconnectivityaslargenumbersofbusinesses,schools,anduniversitiesmovedtoremoteworkandlearningovernight.

Internetconnectivityisprovidedbycommunicationserviceproviders(CSPs)onasetofdiverse,interconnectednetworks.MostCSPs’networksuseahierarchicalarchitectureconsistingofaccess,aggregation,andcorenodestoprovidenetworkmesharchitecture.ThemeshprovidesdiversepathsfromorigintodestinationthatallowsforresiliencyandscalabilityforIPservices.Typically,provideredge(PE)routersareusedattheedgeofthenetworktoprovideaninterfacebetweenacustomer’snetworkandtheCSP’snetwork.ThePErouterprovidesmultipleIPservicestoendcustomers.PEroutersgenerallyconnecttocoreroutersthatareoptimizedforhigh-speedIPtransportandscalability.CorerouterstypicallydonotprovidethesamelevelofservicesasPErouters.AnothercriticalcomponentofmostIPnetworksarepeeringnodes.ThesearerouterstheconnectaCSP’snetworktootherCSPs’networksusingtheBGProutingprotocol.PeeringnodesandBGPallowtheinterconnectionofmultiplenetworksintotheglobalinternet.

AnexampleofanIPmeshnetworkisprovidedinFigure1.ThenetworkconsistsofPErouters,corerouters,andpeeringroutersconnectedinamesh.Thebenefitsofthemesharethatifalinkornodefails,trafficcanbereroutedacrossadiversepath.Additionally,itispossibletousesophisticatedtrafficengineeringtechniquestooptimizelinkcapacityutilizationwhilemaintainingservicelevelagreements(SLAs)forIPservices.

3

PeeringNode

CoreNode

PENode

TheEconomicBenefitsofAutomatingCapacity

OptimizationinIPNetworks

Figure1.ExampleofanIPMeshNetwork

Networktrafficengineeringisbecomingincreasinglyimportantbecauseofthetremendousgrowthintrafficdrivenbyinnovativetechnologiesandapplications.ManyCSPshaveconvergedtheirIPnetworkstoprovidetransportformobile,business,andresidentialtraffic.Someofthekeydriversfortrafficgrowthare:

Mobiletrafficgrowth

?LTEmigrationtoDSS:averagecellsitetrafficincreasesfrom300Mbpsto700Mbps

?LTEmigrationto5G:averagecellsitetrafficincreasesto2.8Gbps

Businesstrafficgrowth

?Videoconferencingcontinuedgrowthduetothepandemic

?Videotraining

?AR/VRandothernewapplicationsaredrivingbandwidthgrowth

?EdgecomputingdrivingnewtrafficforIndustry4.0applications

Residentialtrafficgrowth

?SmartTVsandvideostreaming

?4K/8KTV

?Workathomewithvideoconferencing

?Cloudgaming

?Diversemixofdevices:laptops,smartphones,tablets,gamingconsoles,andsmartTVs

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TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

ACGprojectsaveragehouseholdtrafficof14.2Mbpsin2022growingto20.1Mbpsin20251.However,notalltrafficiscreatedequally.Diversesetsofapplicationshavedifferentrequirements:

?Delay-andjitter-sensitiveapplications

?High-availabilityapplications

?Bandwidth-intensiveapplications

?Best-effortapplications

Mostnetworksdonothavethecapabilitytodifferentiateservicesfortheseapplications,butmovingforwarddifferentiatedserviceswithSLAswillgrowinimportance,especiallyforbusinessandIndustry4.0applicationsandservices.

Trafficgrowthisdrivingnetworkcapacitygrowth.CSPshavetwooptions,brute-forcecapacityandintelligenttrafficengineeringandtrafficoptimization,tomanagenetworkcapacitygrowth.

Brute-ForceCapacityManagement

CSPscancontinuetouseshortestpathroutingwithminimaltrafficengineeringanduseabrute-forceapproachtoaddingcapacity.ThisrequiresCSPstomanagecapacitysuchthataveragelinkutilizationsare50%orlower.Thisallowsnetworkstomanagetrafficburstsandeventswithlargerthanexpecteddemand.Someofthedownsidestothisapproachare:

?Addingnetworkcapacitycantakemonths;settinglinkutilizationsto50%orlowerallowsCSPstimetoupgradethenetwork

?Underutilizedlinkscreatelargeandunnecessaryexpensesinnetworkbandwidth

IntelligentTrafficEngineeringandTrafficOptimization

Alternatively,CSPscanusenetworkintelligencetooptimizenetworkcapacityandrouting,reducingtheneedtoupgradenetworkcapacityandprovidingsignificantsavingsinnetworkcapitalexpense(CapEx)andoperationexpense(OpEx).AnexampleofthisapproachistheJuniperParagonAutomationsolution.

Giventhegrowthofnetworktraffic,autonomouscapacityoptimizationprovidesCSPswithopportunitiesto:

?MinimizelinkcapacityCapExandOpEx

?Supporttrafficgrowthandseasonaltrafficbursts

?Expandtonewmarkets

1/reports/middle-mile-networks-capacity-requirements-for-fix/

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TheEconomicBenefitsofAutomatingCapacity

OptimizationinIPNetworks

AutonomousCapacityOptimization

JuniperParagonAutomationprovidesacomprehensivesolutiontonetworkoptimizationandcapacitymanagement.ThekeyfinancialbenefitsofParagonAutomationthatareconsideredinthepaperare:

?Optimizingnetworklinkcapacity,whichreducesbothCapExandOpEx

?Simplifyingnetworkoperations,whichreduceslaborOpEx

ParagonAutomationenablesCSPstosimplify,automate,andoptimizetrafficengineeringusingacentralizedcloud-nativecontroller.Networkpathdesign,provisioning,andmanagementarefullyautomatedusingcentralizedpathcalculationwithacompleteviewofthenetworktopologyandreal-timetraffic.Withthisautomation,operatorscanincreasesignificantlytheutilizationoftheirnetworkswhileachievingpredictability,resiliency,andservice-levelguaranteesinserviceproviders’,cloudproviders’,andlargeenterprises’networks.TheworkflowinParagonisdepictedinFigure2.ThekeyprocessesimplementedbyParagonare:

?Deploy:Automaticallyconfigureandprovisionthenetworkusingsegmentroutingand/orMPLS-TEtooptimizetransportwhilemaintainingSLAs

?Monitor:On-goingmonitoringofnetworkperformanceandSLAs

?Analyze:Discovernetworktopology,routing,traffic,andservicerequirements

?Optimize:Optimalpathcomputationbasedonnetworktopology,traffic,andservicerequirements

Figure2.AutonomousCapacityOptimizationWorkflow

Thenextsectionsprovidesanoverviewofabusinessmodelthatshowsthefinancialbenefitsandreturnoninvestment(ROI)ofdeployingParagonAutomationinaCPS’smedium-sizenetwork.

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TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

BusinessModelFrameworkandAssumptions

ACGResearchdevelopedadetailedTCOandROImodeltoanalyzethecostsavingsandROIfordeployingParagonAutomation.ThekeybenefitsarereducingCapExandOpExassociatedwithnetworkcapacityandreducinglaborexpenses,whicharerequirementsfornetworkoperationsandmanagement.

Inouranalysiswecomparetwoscenarios:

?WithJuniperNetworksautonomoustrafficoptimization

?Usingbrute-forcecapacitymanagement

Inanetworkwithbrute-forceapproachtocapacityplanninghasanaveragenodelinkandpeeringlinkutilizationof50%.Thisisbecausenetworktrafficishighlybursty.ToguaranteeSLAsforhigh-prioritytrafficthereneedstobeextracapacitytoallowfortrafficburstsaswellasunexpectedtrafficgrowth.Inanetworkwithautonomoustrafficoptimizationweassumecentraltrafficengineeringandoptimizationallowslinksandpeeringpointstorunwithhigherutilization.ThisisbecauseParagonAutomationwillautomaticallyrerouteandoptimizetraffictoguaranteeSLAswhilealsorunningthelinkswithhigherutilization.Inouranalysisweconsiderseveralscenariosforlinkcapacityimprovement,depictedinTable1.Eachnetworkisunique,andsomenetworkswillachievegreaterimprovementsinlinkutilizationthanothers.Forthisreasonweconsidersixscenarioswherelinkutilizationimprovesfrom5%upto30%.

BaseLinkUtilizationwithno

LinkUtilizationwithAutonomous

Utilization

Optimization

CapacityOptimization

Improvement

50%

55%

5%

50%

60%

10%

50%

65%

15%

50%

70%

20%

50%

75%

25%

50%

80%

30%

Table1.ScenariosforLinkUtilizationImprovement

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TheEconomicBenefitsofAutomatingCapacity

OptimizationinIPNetworks

Wemodelahypotheticalmeshnetworkconsistingof35nodes,depictedinTable2.

NodeType

PENodes

CoreNodes

PeeringNodes

Quantity

9

21

5

Table2.BreakdownofNodeTypesinHypotheticalNetwork

ProviderEdge(PEnodesarethepointsdemarcationwheretheCSP’snetworkinterfaceswithacustomerorenterprise’sIPnetwork.ThePEroutersprovideedgeIPservices,andthecostperportofPEroutersistypicallyhigherthanthecostperportofcorerouters,whichareprimarilyusedforIPtransport.PeeringnodesareusedtointerconnectwithotherCSPsandtheglobalInternetusingtheBGPprotocol.Peeringnodesaretypicallyscalablehigh-capacitynodessimilartocorenodes.

Weassumethenetworksupportsmobile,business,andresidentialbroadbandserviceswiththeexpectationsfordemand,presentedinTable3.Trafficisdrivenbythenumberofendpoints(basestations,businessservices,andbroadbandsubscribersandthetrafficperendpoint.OurmodelassumestrafficgrowthdrivenbytheinputsinTable3.

DemandInput

Year1

Year2

Year3

Year4

Year5

NumberofBaseStationsperNode

500

550

600

650

700

MobileTrafficperBaseStation(Mbps)

300

800

2000

2400

2600

NumberofBusinessServicesperNode

150

200

250

300

350

BusinessServiceTraffic(Mbps)

200

300

350

400

450

NumberofBroadbandSubscribersperNode

15000

18000

20000

22000

25000

AverageTrafficperBroadbandSubscriber(Mbps)

13

14.5

16

18

20.1

Table3.TrafficDemandAssumptions

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TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

Tocalculatethecostofnetworkcapacitywithandwithoutautonomouscapacityoptimizationwealsouseassumptionsforthecostofrouterports,opticaltransport,andmonthlypeeringexpenses.Specifically,weaccountfor:

?100GEand400GEPErouterportexpenses

?100GEand400GEcorerouterportexpenses

?100GEand400GEopticalunderlayexpenses

?Monthlypeeringexpenses

Inadditiontonetworkcapacityexpenseswealsoconsiderlaboroperationalexpenses.Weexaminethecostofnetworkcapacityplanningandoperationsfull-timeequivalents.ThefinancialmodelcalculatestheTCO(CapExandOpEx)ofanetworkwithandwithoutParagonAutomationandalsocalculatestheROIofaninvestmentinParagonAutomation.

BusinessCaseResults

ForthenetworkconfigurationanddemandspecifiedinTable2andTable3wehavecalculatedtheTCOsavingsforsixutilizationscenarioswithandwithoutautonomouscapacityoptimizationasspecifiedinTable1.Thecumulativefive-yearTCOsavingsforeachscenarioisdepictedinFigure3.ThisanalysisshowsthatregardlessoftheleveloflinkutilizationimprovementssignificantTCOsavingscanbeachieved.AslinkcapacityutilizationimprovesTCOsavingscontinuetogrow.

ThesavingsoflinkcapacityimprovementsareextremelyhighcomparedtothecostofdeployingParagonAutomation.Wedeterminedthatwithanaveragelinkcapacityimprovementof0.5%theTCOsavingswillpayforthecostofParagonAutomation.ThecostofParagonAutomationincludes:

?Paragonautomationsoftwarelicenses

?JuniperprofessionalservicestodeployParagonAutomation

?Operator'slaborexpensestodeployParagonAutomation

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TheEconomicBenefitsofAutomatingCapacity

OptimizationinIPNetworks

Figure3.Five-YearTCOSavingsforDifferentLevelsofLinkCapacityImprovement

Inthespecificscenariowherelinkutilizationimprovesfrom50%to70%moredetailispresentedontheTCOresults.Specifically,thecumulativeTCOresultscomparingthetwoscenarioswithandwithoutParagonAutomationaresummarizedinTable4.

Five-YearCumulativeResults

CapEx

OpEx

TCO

WithParagonAutomation

$17,507,864

$19,292,103

$36,799,967

WithoutParagonAutomation

$24,518,712

$26,014,899

$50,533,611

Savings

$7,010,848

$6,722,796

$13,733,644

SavingsPercentage

29%

26%

27%

Table4.Five-YearCumulativeTCOResultswithandwithoutParagonAutomation

TheannualTCOspendcomparisonfornetworkswithandwithoutParagonAutomationarepresentedinFigure4.TheincreaseinTCOfromYear1toYear5isdrivenbythegrowthinnetworktrafficspecifiedinTable3.Astrafficgrowsthebenefitofautonomouscapacityoptimizationbecomesincreasinglymoreimportant.ThismeansthatthebenefitofParagonAutomationwillbegreaterinthefutureastrafficandnetworkcapacitycontinuetogrow.

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TheEconomicBenefitsofAutomatingCapacityOptimizationinIPNetworks

Millions

$14

$12

$10

$8

$6

$4

$2

AnnualTCOComparison

$16

$-

Year3

Year1

Year4

Year5

Year2

TCOwithParagonAutomationTCOwithoutParagonAutomation

Figure4.AnnualTCOComparisonofNetworkswithandwithoutParagonAutomation

AbreakdownofTCOexpensesispresentedinFigure5.ThisbreakdownshowsthekeydriversofexpenseandsubsequentTCOsavingsarePEandcorerouter100GEand400GEportCapExandpeeringlinktransportOpEx.TherouterportCapExsavingsareadirectresultofrunningthelinkswithhigherutilization,andthepeeringnodeOpExsavingsresultfromoptimizingtrafficdistributiontopeeringsites.ThetotalcostofdeployingParagonAutomationissmallincomparisonwiththesavings.

ParagonAutomationExpenses

Power&CoolingOpEx

LaborOpEx

PeeringLinkTransportOpEx

OpticalLinkCapEx

PeeringNodeLinkCapEx

GeneralNodeLinkCapEx

FiveYearCumulativeCostBreakdown

$-

$20

$25Millions

$5$10