scalability and performance analysis of sip based multimedia … ·...
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DOI: 10.4018/IJICST.2019010102
International Journal of Interactive Communication Systems and TechnologiesVolume 9 • Issue 1 • January-June 2019
Copyright©2019,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.
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Scalability and Performance Analysis of SIP based Multimedia Services over Mission Critical Communication SystemsAshraf A. Ali, University of South Wales, Pontypridd, UK; Hashemite University, Az-Zarqa, Jordan
Khalid Al-Begain, Kuwait College of Science and Technology, Kuwait City, Kuwait
Andrew Ware, Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, UK
ABSTRACT
Variousstudieshavesuggestedenhancingtheperformanceoflarge-scalesystems,suchasmissioncriticalcommunicationsystems(MCCSs).However,fewhavemodelledandevaluatedtheperformanceofsuchsystemsinawaythattargetsoverallsystemperformanceinrealtime.Moreover,itisnotenoughtodefinetheKeyPerformanceIndicators(KPIs)forasystemwithoutusingthemforsystemperformancemeasurementandperformanceevaluation.TheSessionInitiationProtocol(SIP)andIPMultimediaSubsystem(IMS)bothhaveasetofKPIs,suchastheregistrationprocessdelay,thatcanbeusedtomeasureandthusoptimizeoverallsystemperformance.Thisarticlearticulatesdifferentoptionsforsystemsimulationandevaluation.Theregistrationprocessaffectsperformanceandreflectstheoverallsystemperformance.Thearticleshowshowtheregistrationprocessisdelayedandhowtheoverallsystemscalabilityarenegativelyimpactedbysystemoverload.
KeywoRdSIMS, IP Multimedia Subsystem, Long Term Evolution, LTE, Modelling, Performance Evaluation, Session Initiation Protocol, SIP
INTRodUCTIoN
IPMultimediaSubsystems(IMS)(3GPP,2006)andSessionInitiationProtocol(SIP)(Rosenberg,2002)performanceplayamajorroleinmultimediacommunicationnetworksbyalteringtheKeyPerformanceIndicators(KPIs)relatedtotheQualityofExperience(QoE)metricsoftheend-to-endservice.RegistrationRequestDelay(RRD)isoneoftheSIPKPIsthatalsoinfluencebothIMSKPIsandenduserQoE.Therefore,itiscrucialtoevaluatetheperformanceofbothSIPandIMSbasedontheRRDmetricinordertogiveanindicationoftheoverallsystemcapacityandscalabilitypotential.
5Gcommunicationsisthenewtechnologythatwillintegratemultipleaccesstechnologyintooneintegratedsolutionadoptedbyallvendorsandmanufacturers.EndusersanddeviceswillbeabletocommunicateseamlesslywithfewerrestrictionsandmoreoptionscomparedtooldertechnologiesScalabilityisamongthechallengesthatlimittheexploitationofthefullcapabilitiesofthecurrenttechnologies.Manyrecentstudieshavetriedtoovercomethescalabilitychallengeassociatedwiththe5Gstandardsset.AnewintegratedsolutionwithexternalSIPapplicationthatisaccessedover
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LTEwithdifferentvoicecodecsisintroducedinotherstudies(Haibeh&Hakem,2017).However,thesolutionproposedlacksthesupportofstandardisedadoptedsolutionsthusintroducingcomplexitiesinimplementations.InasimilarSIPperformanceevaluationanenhancementtrial(Subramanian&Dutta,2009),transactionstatesoftheSIPserverarecharacterisedtomodeltheperformanceoftheserver.InthetrailM/M/cqueuingmodelwasusedalongwithabenchmarkingperformanceindicatorthat reflects system performance. The trial showed that the multi-threaded architecture utilisingparallelprocessingofSIPmessagesprovidesamorescalableandefficientsolutionforlargernumberofclients.However,thestudywasbasedonsimpleSIPserversthatdonotreflectmorecomplicatedprocessingrequiredbymultimediaservicesthattendtousemorethanoneSIPserver.
Anotherstudy(Ono&Schulzrinne,2008)usedtheStreamControlTransmissionProtocol(SCTP)asatransportprotocolforSIPmessagesinsteadofTCPandUDP.TheimplicationsofusingdifferenttransportprotocoloverSIPscalabilityandperformanceispresentedanditisshownthatSCTPhasanegativeimplicationoverSIPscalabilityduetotheaddedoverhead.Otherresearch(Yavas,Hokelek,&Gunsel,2016)hasfocusedontheschedulingmechanismstopreventSIPsystemoverloadandtoincreaseitsscalability,theproposedsolutionusesapriority-basedmechanismtodynamicallyestimatethebehaviouroftheserverandprovideamorescalablesolutioncomparedtotheconventionalSIPservers.Again,thisstudyevaluatesonesingleSIPserver.
Thispaperanalyseshowoverallsystemcapacityandscalabilityisaffectedbyadditionaltrafficgeneratedwhenmoreuserstrytoaccessthesystemsservices.Thiscouldhappeninamissioncriticalcommunicationsystemduringnaturaldisasterorlarge-scaleattack,resultinginasuddenincreaseinthenumberofusers.
Itwasfoundthat,withinlimits,thesystem’sabilitytoprocesstheregistrationrequestspertimeunitincreasesexponentiallywhenthenumberofusersisincreased.Oncethelimitisreachedhowever,thenumberofprocessedrequestsstartstodecreaseandeventuallydegradeleadingtosystemfailure.Thesimulationresultsshowthatthesystemwasabletohandleamaximumof7,400registrationspersecond,aworkloadthatcouldoccurduringanationwidedisasterwithmanyuserstryingtoaccesstheMissionCriticalSystem(MCS).
TheneedforamoredetailedstudyofotherSIPandIMSKPIstoprovideabetterunderstandingoftheoverallsystemperformanceisthusclear.Thestudywillenablefurtherprogresstowardssystemperformanceenhancementandoptimizationinordertoavoidsinglepointoffailureofthesystem.
ReSeARCH MeTHodoLoGy
Apreviouslydevelopedresearchmethodology(Creswell,2009)wasfollowedforboththequalitativeandquantitativeapproacheswhensettingtheparametersforallmeasurementsandsimulations.Themethodologyfordecidingthequalitativevaluesthatneedtobeinvestigatedcanbesummarizedasfollows:
1. Determinethechallengesthatneedtobeinvestigatedwithinthescopeofthestudy.Whiletheprojectembedsseveralchallenges, thefocuswasplacedonthesignallingdomainespeciallybetweentheenduserandthecorenetworkandthesignallinginterfacebetweenthecorenetworkandIMS.
2. DeterminethebenchmarkforwhatisconsideredacceptableSIPperformanceanddecideonthemetricsthatwillbemeasuredandusedtojudgeandcomparetheperformanceofsetup.
3. Decidetheappropriatesimulationtoolstogeneratetheresultsfrommultiplesourcesthatmeettheappropriatecomparisoncriteriabasedontheselectedtool.
4. Determine the key factors that affect the SIP signalling, in addition to multimedia servicesoperationinLTEandIMSthataffecttheoverallQoSfortheMissionCriticalsystem.
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TheQuantitativeMethodologytoacquiretheneededmeasurementsissummarizedasfollows:
1. Developatest-bedfortheIMStodeterminetheperformanceofthesystem.Thendecidetheperformance metrics that need to be measured in order to facilitate comparison with otherimplementationsandscenarios.
2. Develop a virtual machine to generate virtual clients along with IMS in order to facilitatecomparisonbetweentheperformanceofthesystemwiththerealrunningtest-bed.
3. DevelopasimulationprojectforbothLTEandIMSoverOPNETtobenchmarktheirperformanceagainstthetest-bedimplementationperformancemetrics.
4. Determinethevariables,models,scenariosandparametersinOPNETthatneedtobeadjustedandanalysedtoenableoverallsystem’sperformanceevaluation.
Insummary,asshowninfigure1,therearethreesimulation/testingoptions.Thissectionpresentsthetest-bedsandsimulationresultsthatrelatedtotheproject.First,IMS
test-bedscenarioandresultsaregiven.Secondly,theOPNETsimulationscenariosandresultsaredemonstrated.Thirdly,thelimitationsandchallengesofbothexperimentsarediscussed.
TeST-Bed eXPeRIMeNT
Figure2showstheexperimentaltopologyofthetest-bed.Thetest-bediscomposedoffourmainparts: thePacketGenerator; IMScorewhich isbasedonOpen-IMS-Coremodel (Fokus,2004);PacketAnalyser,andDomainNameServer(DNS).Thepartsfunctionandoperationareasfollows:
• PacketGenerator:Thepacketgeneratorisresponsibleforsimulatingvirtualclientsthatthengenerateconcurrentcallsthataretransmittedinaserialorparallelmannerbyatheoreticallyunlimitednumberofusers.DuetothefocusonSIPandIMSperformance,thePacketGeneratorisdesignedtosendSIPRegisterMessage(asdefinedbyRFC3261)inadditiontoSIPinviteandbyemessages.AllthemessagesaretransportedusingtheUDPwherethesenderportaddressisdynamicallyallocatedsoastoavoidusingrestrictedportsatthesenderorserversides.TheGUIinterfaceofthePacketGeneratorenablestheusertoselectapredefinedsetofusersandtheProxyIPaddress.Finally,theSIPrequest-sendingpatternisselectedtobeeitherserialorparallel.
• IMSNetwork:TheIMScoreisbasedonOpen-IMS-Core(Fokus,2004)developedbytheFOKUSInstituteforOpenCommunicationSystem.TheserverembedtheIMSCallSessionControlFunctionsCSCFs;suchasPCSCF,I-CSCF,andS-CSCF,inadditiontothehomeSubscriber
Figure 1. Available simulation and testing tools
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StationHSS.Allareconsideredpartofthecorearchitectureforthenextgenerationofnetworksasspecifiedby3GPP.ThepurposeoftheexperimentistotestthecapacityoftheIMSintermsofthemaximumnumberofusersthatcanbeadoptedbythesystemwithoutcausingstabilityissues.
• PacketAnalyser:ThepacketssentbythePacketGeneratoraremonitoredusingWiresharkasapacketanalyseratthesenderside.ThetracefilesextractedfromthepacketanalyzerhelpincalculatingtheKPIvaluesforbothSIPandIMS.
• DNS:anexternalDomainNameServer(DNS)toresolvetheIPaddressesofallserversinthesystemsetup.
BasedontheprevioussetuptheexperimentaimedtoevaluatetheSIPperformanceovertheIMSusingeitherawiredorawirelessconnectivitywiththeserver.Forthispurpose,theregistermessagedelaywascalculatedbyrunningWiresharkatthepacketgeneratorsideandcalculatingthedifferencebetweenthesentregistrationrequesttimeandthe2.00OKresponsereceptiontime.ThedatawasthenexportedusingMATLABandanalysedtheProbabilityDensityFunction(PDF)andCumulativeDensityFunction(CDF)curvescalculated.TheseprovideabetterunderstandingofthevarianceinRegistrationdelaywithinthesamescenarioandamongdifferentrunningscenarios.
Figure3showstheGUIinterfaceforthePacketGenerator.Theuserfirstselectsapredefinedsetofusers’databasesandtheProxyIPaddress(whichistheIMSserverIP).Inaddition,thedomainnameisinsertedandtheSIPrequestsendingpatternselected(eitherseriesorparallel).PressingREGISTERinitiatesthesendingoftheregistrationrequests(oneperuser)consecutivelyanddynamically.ThepacketssentbythePacketGeneratoraremonitoredusingWiresharkatthesendersidewhilethelog
Figure 2. Experiment test-bed
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screenatthepacketgeneratorrecordsthetimestampofthesentrequestsandreceivedresponses,whichhelpsincalculatingtheend-to-endapplicationdelay(thetimebetweenpeerapplicationlayers).
Basedontheprevioussetup,theexperimentaimedtoevaluatetheSIPperformanceovertheIMSusingeitherwiredorwirelessconnectivitytotheserver.Forthispurpose,theregistermessagedelayiscalculatedbyrunningWiresharkatthepacketgeneratorsideandcalculatingthedifferencebetweenthesentregistrationrequesttimeandthe2,00OKresponsereceptiontime.Thedataisthen,usingMATLAB,exportedandmanipulatedinordertogeneratecurvesforabetterindicationofthevarianceinRegistrationdelaywithtime.Theexperimentwasrepeatedmultipletimes,eachtimethenumberofuserswasincrementedinboththewiredandwirelessscenarios.
ReSULTS
Inthisscenario,thepacketgeneratorwaswireddirectlytotherouterandthenumberofuserssendingtheregistrationrequestwereincrementedinstepsof200intherangefrom100to1,300users.Figure4showsthePDFandCDFoftheregistrationdelayfor100whileFigure5showsthePDFandCDFfor500users.
Figure 3. Packet generator GUI
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Figure 4. CDF and Density functions of the Registration delay for 100 users
Figure 5. CDF and Density functions of the Registration delay for 500 users
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Fromfigures4and5, it isclear that theregistrationdelayfor500usersmore thandoubledcomparedtothatachievedfor100users,andincreasedevenfurtherwhenincreasingthenumberofusersineachstep.Similarly,sevenvaryingscenarioswereimplementedusingthetest-bed.Inthefirstscenario,100userregistrationrequestsweretransmitted,whileinthesubsequentscenariosthenumberofuserswasincrementedinstepsof200until1,300userswereconsideredintheseventhscenario.Totestthescalabilityofthesystem,thenumberofuserswasgraduallyincreasedinordertogainabetterunderstandingoftherelationbetweenthenumberofusersandtheKPIvaluesforbothSIPandIMS.
Figure 6 shows the Probability Distribution Function (PDF) and figure 7 the CumulativeDistributionFunction(CDF)fortheRRDofthefirstscenariowithonly100userseachsendingoneregistrationrequestatatimeinsequentialorder.Asshowninfigure7,90%ofRegistrationrequestsneedlessthan40mstobecompletedwhichmeetstherequirementsofmissioncriticalapplicationsandreal-timeservices.Asshowninfigure6,thehighestfrequencyoftheregistrationtrialsneedsonaverage20mstobecompleted.Thisisconsideredthebest-casescenarioandwasusedasabenchmarkfortheotherscenariosinordertoenablecomparisonofboththeRRDtimeandthepercentageoftrialsthatfinishatcertaintimethreshold.
Similarly,thePDFsandCDFsforallsevenscenariosweregeneratedasshowninfigure8and9.It isclear thatwhenthenumberofclients increases, thesystemneedsmoretimetoservetheregistration requests.Thishappensdue toaccumulationofbothSIPandDIAMETERsignallingmessagesinthequeuesoftheCSCFsinterfaces(especiallyinS-CSCF)andtheHSSinterface.BothS-CSCFandHSSareconsideredbottleneckpointsofcongestionthatareaffectedsignificantlyasthenumberofregistrationrequestsincrease.Thisleadstoaqueuingdelaythatemergesrapidlyinthesysteminterfaces,whichcaneventuallycausesystemfailure.
Twoperformancemetricswereusedtofacilitatecomparisonofthesevenscenarios.Thefirstwasthetimeneededtoprocesssuccessfully90percentofrequests,referredtoas90%completiontime(90CT).Whilethesecondwasthepercentageofsuccessfullycompletedregistrationrequestswithin40msseconds(whichisthemaximumRRDtimeneededtoprocess90%ofrequestsinthe100-usersscenario),referredtoas40msCompletionRatio(40msCR).
Figure 6. PDF of RRD for 100 users
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Figure 7. CDF of RRD for 100 users
Figure 8. PDF of RRD values for all scenarios
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Table1showstheaverageRRDvaluesalongwith90CTand40msCRforeachscenarioandtheregistrationrequestspersecond(RRps)processingrate(whichisthenumberofregistrationrequeststhataresuccessfullyprocessedbythetest-bedperunittime).TheRRpsvaluesreplicatearealworlddisasterscenario,wherethousandsofusersmaysendregistrationrequesttogainaccesstothesystem-whichissupposedtobescalableandreliable-atthesametime.
Basedontheresults,itcanbeseenthattheRRpsincreasesexponentiallyasthenumberofusersincreasesuptoalimit(of1,100users)beforebeginningtodecrease,leadingeventuallytosystemdegradationandfailure.ThisisshownclearlybyRRpsforbothscenarios6and7,wheretheRRpsofscenario7ismuchlessthantheRRpsforscenario6althoughthenumberofusershasincreasedby200.
Asexpected,the90CTincreasesasthenumberofusersrises,startingfrom40msforscenario1throughto150msforscenario7.Thisistobeexpectedduetotheincreasedprocessingtimeneededfortheadditionalreceivedregistrationrequest.Moreover,itwasfoundthatthe40msCRdecreaseswithanincreasednumberofusers.Comparingthevalueswithscenario1(thebenchmark)showsthatonly15%ofregistrationrequestsneededlessthan40msRRDvaluetobecompleted,whichagainimpliesthatthesystemisnotabletoprocessthereceivedrequestwithinverystricttimelimit.
Figure 9. CDF of RRD values for all scenarios
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oPNeT SIMULATIoN
TofacilitateinvestigationoftheLTEsystem,especiallytheSIPsignallingperformanceoverLTEcommunicationnetwork,theOPNETsimulatorwasusedtocreateascenariowithmultipleusersinitiatingcalls.ThisenabledtheSIPperformancemetricstobeusedtomeasuretheefficiencyofthesystemanditscapacitytolerance.Figure10showsthecreatedsetup.
Simulation Setup and ScenariosIn this researchstudy,OPNETModellerprovides therequired levelofsimulationcapabilities toimplement and model different multimedia applications over LTE. The system design that wasimplementedandinvestigatedisshowninfigure10andisbasedontheconfigurationparametersshowninTable2.TheimplementationoftheLTEnetworksystemisbasedonasingleEvolvedPacketCore(EPC)thatservestwoeNBs,eachwithfourclients.TheclientsineNB1makeSIP-basedVoIPcallstotheclientsineNB2throughtheEPCinaNormaldistributioncallgenerationsystem,usingafixed-lengthcall.TheEPCisthenconnectedtotheSIPserver,whichreflectstheperformanceoftheP-CSCFintheIMS,thatmanagetheregistration,callinitiationandcallterminationprocessesusingtheSIPsignalingsystemusingtheIPcloud.Inthisreseach,thesimulationswereperformedwithoutanybackgroundtrafficintheLTEsystemandtheIPnetwork.ThisenablesustostudytheactualperformancelevelforSIP-basedVoIPapplicationswithinabesteffortenvironmentwhichhelpswiththeresultsaccuracy.Itshouldbenotedthatthisresearchhasnotconsideredanyclientsmobilityperformanceimplicationoversignallingdelays,itisleftasafutureworktodiscussitfurther.
Thesimulationimplementationshasconsideredfourscenariosbasedonthedesignshowninfigure10andthesimulationparametersshowninTable2.ThefirstscenariorepresentsthebasicimplementationforVoIPapplicationsoverLTEusingasinglepairofUEsbetweenclientA-1ineNB1andclientB-1ineNB2.Thisscenarioexaminesthebest-caseimplementationoftheassignednetworksystemwithonlyonesinglecallatatime.ThesecondscenariohasanadditionalconnectionwithmultiplecallswithanotherpairofUEs(clientA-2andclientB-2)addedtothefirstscenario.Asimilarthingistrueofthethirdscenario,whereadditionalpairsofcallsareadded(clientA-3andclientB-3).Finally,thefourthscenariohasyetanotheradditionalpairbetweenclientA-4andB-4.ThisgradualincreaseinthepairsofSIP-basedVoIPcallsallowedtheperformanceoftheSIPsignallingsystemoverLTEbasedcommunicationswithadditionalVoIPcallsbetweendifferentclientstobechecked.ThehighestloadofVoIPcallsisrepresentedinthefourthscenariothatconsumeshigherbandwidthoverLTEwhereallclientsineacheNBarecallingonesingleclientintheothereNB.Therefore,theresultsoftheseimplementedscenarioscanbecomparedandstudiedthroughouttheresearchstudyintermsoftheperformanceforSIPsignallingandefficiencyforLTEsystem.
Table 1. Calls statistics from simulation results
Scenario no. RRps RRD Avg. Value
90CT (ms) 40msCR (%)
Scenario1(100users) 1,800 20 40 100%
Scenario2(300users) 3,600 28 47 80%
Scenario3(500users) 5,900 44 65 40%
Scenario4(700users) 4,900 22 55 73%
Scenario5(900users) 5,500 64 105 23%
Scenario6(1100users) 7,400 77 125 17%
Scenario7(1300users) 5,800 88 150 15%
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Simulation ResultsAsthemainconsiderationsinthisstudyareSIPsignallingandLTEperformanceformissioncritical
systems, the results focus on the call setup time and the related LTE performance metrics. Theoptimumnumberofinitiatedcallsforeachpairofcallsfallsbetween150and180for30minutesofsimulationtimewithauniformbaseddistributionsystemforcallsinitiation.Table3showsthenumberofrejectedcallsintheoverallsystemforthefourscenarioswiththeimplementednormalbasedsystem.Thenumberofrejectedcallshasincreasedwiththeincreasednumberofinitiatedcallpairs.ForcallsimplementedfromCallerA-1,thenumberoffailedcallsinitiationprocesseshadbeenincreasedwiththeincreasednumberofcallpairswithscenariosS2,S3,andS4,wherethetotalinitiatedcallsoverallscenariosis56.ThisincreasedfailrateduringthecallinitiationstageismainlyrelatedtotheinferiorprocessingperformanceoftheSIPservers’andLTEsystemperformance.
Table 2. Simulation parameters in OPNET
A. LTE Network System
NumberofSimulations 4 SimulationSeedNumber 128
SimulationDuration: 30Minutes=1800Seconds
NumberofEPC: 1 BackgroundTraffic 0%
NumberofeNB: 2 NumberofnodesforeacheNB: 4
AntennaGainforeNB: 15dBi eNBMaximumTransmissionPower:
0.5W
eNBReceiverSensitivity: -200dBm eNBSelectionThreshold: -110dBm
B.Applications:SIPBasedVoIP
VoIPCalls(Unlimited)
CallDuration Caller Callee
10Sec NodeA NodeB
MaximumSimultaneousCalls
SIPServer UserAgent(Caller/Callee)
VoiceCodec:
UnlimitedCall/Second 1callattimebetweeneachpair
GSM13Kbps
CallsStartTimeOffset: Normal(150sec,100sec)
CallsInter-repetitionTime: Normal(20sec,5sec)
Figure 10. System design and implementation for SIP-based VoIP applications over LTE network system in OPNET
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Call Setup PerformanceThepurposeofstudyingthecallsetuptimeistofacilitateanalysisoftheSIPsignallingperformanceduringthemainSIPsignallingstageoverdifferentcallsessions.AslongasthecallsetuptimeforthemajorityofinitiatedSIP-basedcallswereintheacceptablerange,theperformanceoftheSIPsignallingsystemfallsinitsacceptablelevel(D.Malas,2011)(Voznak&Rozhon,2010).Figure11representstheaveragecallsetuptimeforallsuccessfulVoIPcallsforthefourimplementedscenarios.
Theresultsshowthatthescenariowithonlyonepairofcallshadthelowestaveragecallsetupwith times ranging frombetween46and47ms, and increasedup to48.5mswhen the scenarioinvolvedtwopairs.WiththreepairsofVoIPcalls,theaveragecallsetuptimeincreasedfrom47msto49.5ms.ThelongestcallsetuptimeregisteredwasforthefourthscenarioinwhichfourpairsofVoIPconnectionswereactiveatthesametime.ThesesimultaneouscallsaffectedtheSIPsignallingperformanceandincreasedtheaveragedelaybyupto50.5ms.Ingeneral,thecallsetuptimeforsuccessfully initiatedcallsover all scenarios is still at anacceptable levelwhenconsidering theperformanceoftheSIPsignallingsystem.ThiswasduetoimplementingtheLTEnetworksystemwithouthavingextraoverloadsduetoaddedbackgroundtraffic.
Table 3. Calls statistics from simulation results
SIP calls statistics for the Implemented Scenarios
Scenario S1: 1Pair
S2: 2Pairs
S3: 3Pairs
S4: 4Pairs
NumberofCallsRejectedintheoverallsystem 45 95 152 218
NumberofCallsInitiatedfromCallerA-1 56 56 56 56
NumberoffailedcallsinitiationforcallsfromCallerA-1 27 30 38 34
Figure 11. Call setup delay
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LTE Downlink Packets DroppedTheLTEparametersoftheimplementedsystemhaveadirecteffectontheperformanceoftherunningapplications.Real-timeapplicationscanbeenhancediftheLTEsystemperformancebehaviourhasbeenconsidered.Theaveragenumberofpacketsdroppedstartsbetween1and3packets/secforthesinglepairscenarioandincreasestobetween6and18packets/secforthefourpairsscenarioasshowninFigure12.ThedownlinkpacketsdroppedofLTEsystemhasadirectlinktosuccessfulrateoftheSIPsessionsinwhichitisdirectlyproportionallyincreasing.
TheLTEsystemdelaysinthetransferreddatabetweenLTEcomponentsaffecttheperformanceforreal-timeapplications.TheaverageLTEdelayswithoneandtwopairsofVoIPcallsisbetween2msand2.7ms,asshowninFigure13.TheaverageLTEdelaysforthreepairsofVoIPcallsisfrom2.4msto3.5ms,andbetween2.5msand4.3mswithfourpairsofcalls.Thelongestdelaysmostlyoccuratthesystemstart-uptimeandstabiliselaterduringthesimulationtime.
CoNCLUSIoN
Based on the test-bed results, it has been shown that the scalability of the system is negativelyaffectedbytheincreasingnumberofregistrationrequestssenttothesystem.ItwasfoundtheRRpsincreasesexponentiallywhenthenumberofusersisincreased(uptoalimit1,100users)beforetheRRpsstartstodecreaseleadingeventuallytosystemdegradationandfailure.ThisisclearlyshownbyRRpsforbothscenarios6and7,wheretheRRpsofscenario7ismuchlessthantheRRpsforscenario6(althoughthenumberofusersincreasedby200users).
Basedonthesimulationresults,itisclearthatthereisincreasingdelayinthecallsetuptimewhentheLTEcommunicationsystemisused.Thisdelayincreasesasthenumberofservedclientalsoincreases,whichindicatesthattheDelayrequirementorthemaximumnumberofusersthatcanbeservedata timemaynotmeet themissioncriticalservicerequirements.Hence, theneedfordecreasingthegapofcallsetupdelayforcommercialbroadbandsystemscomparedwithotherdedicatedmission-criticalcommunicationssystemsisofgreatimportanceandconsideredoneofthemainchallengesformission-criticalcommunications.Thismeansthatthereisaneedforanew
Figure 12. Average packets dropped
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mechanismthatminmisesaccessdelayoverheadbyexploringtheLTEandIMSdomainsinadditiontotheinterfacesbetweenLTEandIMSandtheinterfacebetweenLTEandUserElement.
Thesimulationhasbeenimplementedusingstaticmobilenodes(thatis,thepositionsofthenodesarefixed).Hence,thereisnohandoffaddedcomplexityforthenodesmovingbetweentwocelldomains.Ifmobilityweretobeconsidered(thatdoesnotimplymovingnodesonlybutratheradynamictopology)thensupportforhandoffmechanismsbetweenthesubscriberstationsanddifferentbasestationswouldneedtobeconsidered.Therefore,further testingofdifferentcommunicationscenariosforanend-to-endconnectivityoverLTEcommunicationsystemisneeded.Forsuchdynamictopology,theneedformeasuringtheoverallperformanceofthesystemintermsofSIPsignallinganddatastreamingdelayiscrucial.
Figure 13. Average LTE delays in second for caller A-1 node
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