Link Delay Inference in ANA Network Ebenezer Paintsil - DUO

105 Pages·2008·1.05 MB·English

UNIVERSITY OF OSLO
Department of Informatics
Link Delay Inference in
ANA Network
Ebenezer Paintsil
Network and System
Administration
Oslo University College
May 28, 2008
Link Delay Inference in ANA Network
Ebenezer Paintsil
Network and System Administration
Oslo University College
May 28, 2008
Abstract
Estimating quality of service (QoS) parameters such as link delay distribution from the
end-to-enddelayofamulticasttreetopologyinnetworktomographycannotbeachievedwith-
out multicast probing techniques or designing unicast probing packets that mimic the char-
acteristics of multicast probing packets. Active probing is gradually giving way to passive
measurementtechniques. WiththeemergenceofnextgenerationnetworkssuchasAutonomic
NetworkArchitecture(ANA)network,whichdonotsupportactiveprobing,anewwayofthink-
ingisrequiredtoprovidenetworktomographysupportforsuchnetworks. Thisthesisisabout
investigating the possible solution to such problem in network tomography. Two approaches,
queuemodelandadaptivelearningmodelwereimplementedtominimizetheuncertaintyinthe
end-to-end delay measurements from passive data source so that we could obtain end-to-end
delay measurements that exhibit the characteristics of unicast or multicast probing packets.
The result shows that the adaptive learning model performs better than the queue model. In
spiteofitsgoodperformanceagainstthequeuemodel,itfailstooutperformtheunicastmodel.
Overall, the correlation between the adaptive learning model and multicast probing model is
quite weak when the traffic intensity is low and strong when the traffic intensity is high. The
adaptive model may be susceptible to low traffic. In general, this thesis is a paradigm shift
from the investigation of ”deconvolution” algorithms that uncover link delay distributions to
howtoestimatelinkdelaydistributionswithoutactiveprobing.
Acknowlegements
There are special people who are behind the success of this work and deserve my
appreciationandrecognition. IacknowledgethesupportofmyadvisorProf. Demissie
Aredo and co-advisor Professor Mark Burgess. Their support was very helpful to see
methroughthisundertaking. IowespecialgratitudetoProfessorMarkBurgessforhis
dedicationto thisworkandthepromptness withwhichall mydifficultquestions were
answered.
Iamalsogratefultotherestofmylecturerswhogavemethefoundationtobeable
to carry out this work. I am thankful to Kyrre Begnum, Ismail Hassan and others who
indiversewayshelpedtomakethisasuccess.
Iacknowledgethegoodcompanyofmyfellowclassmatesfromvariouspartofthe
world in this work. Your presence made me experienced the dynamics of the whole
world. Iamthankfulforyoursupportandamazingfriendship,thetwoStiens,Lu,Iman,
Saedar,Jeang,MariusandalltheothersespeciallyKashif.
Lastly, I want to thank my family (Annabella, Sela (wife) and others ) for their
amazing support. Their support for me has been unconditionally in every way and at
everyturn. AlsoIthankAbraham,Lizforbeinggoodfriends.
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Contents
1 Introduction 1
1.1 NetworkMonitoringInGeneral . . . . . . . . . . . . . . . . . . . . 2
1.1.1 ChallengesofMonitoring . . . . . . . . . . . . . . . . . . . . 3
1.2 MotivationandResearchQuestions . . . . . . . . . . . . . . . . . . . 5
2 BackgroundandTheory 8
2.1 BuildingblocksofANA . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.1 CommunicationBetweenClients . . . . . . . . . . . . . . . . 12
2.2 Autonomictheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 ANASelf-ManagementConcepts . . . . . . . . . . . . . . . . . . . . 16
2.4 ServiceOrientedArchitecture . . . . . . . . . . . . . . . . . . . . . . 17
2.5 PromiseTheoryandANA . . . . . . . . . . . . . . . . . . . . . . . 19
2.6 NetworkTomographyMonitoringConcepts . . . . . . . . . . . . . . 21
2.7 TestingANANetwork . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.8 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3 ModelandMethodology 26
3.1 ExperimentalGoal . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.2 TheExperimentalModelandAnalysis . . . . . . . . . . . . . . . . . 26
3.2.1 StatisticalInference . . . . . . . . . . . . . . . . . . . . . . . 26
3.2.2 NetworkTomography . . . . . . . . . . . . . . . . . . . . . 27
3.2.3 ModelAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.4 TheQueueModel . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.5 Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.6 AdaptiveLearningModel . . . . . . . . . . . . . . . . . . . . 32
3.3 ExperimentalDesign . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.1 Simulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.2 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4 ExperimentalSetup . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.1 TestPlan . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.2 HowtoObtainResults . . . . . . . . . . . . . . . . . . . . . 35
3.4.3 HowtoObtainTheEnd-to-endDelayofPassiveDataSource . 38
3.4.4 PerformanceMetrics . . . . . . . . . . . . . . . . . . . . . . 42
3.4.5 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
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CONTENTS
3.4.6 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.4.7 L RelativeErrorNorm . . . . . . . . . . . . . . . . . . . . . 43
1
3.4.8 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.9 PossibleExperimentalError . . . . . . . . . . . . . . . . . . 44
3.4.10 TheKindofDatatoCompare . . . . . . . . . . . . . . . . . 45
4 Results 47
4.1 DescriptionofResultsandTestProcedures . . . . . . . . . . . . . . . 47
4.1.1 End-to-EndDelay . . . . . . . . . . . . . . . . . . . . . . . . 48
4.1.2 LinkDelays . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.3 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.1.4 L RelativeErrorNorm . . . . . . . . . . . . . . . . . . . . . 55
1
5 Discussion 57
5.1 ExperimentalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2 PerformanceEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3 Performanceunicast/multicastversuspassive/multicast . . . . . . . . 65
6 ConclusionandFutureWork 67
6.1 ns-2Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
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List of Figures
2.1 figure 2.1differentviewsofANAcompartment,theredsquareblocksaretheFB,thereisonlyoneFBinthisdiagrambutinpracticetherecouldbemoreFBsinasinglecompartment. ThesolidgreenbarsarethelogicalICs. ThebottomlogicalICrepresentthechainoffunctionsorservicesprovidedbytheunderlyingsystem(OS).ThetoplogicalICrepresentthechainofservicesorfunctionalitiesprovidedbythecompartmenttotheoutsideworld. ThesolidblacklinesrepresentshephysicalICormediumthroughwhichinformationtravelswithinthecompartment. ThesolidblackdotrepresentsIDP.[1] 11
2.2 Internalresolutionofcompartmentmember[1] . . . . . . . . . . . . . . . 12
2.3 Clientcommunication[1] . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.4 Clientcommunication[1] . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.5 Networkmonitoring[1] . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1 topologyoftheexperiment . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1 UnicastEnd-to-EndDelayforNode4 . . . . . . . . . . . . . . . . . . . 49
4.2 PassiveEnd-to-EndDelayforNode4 . . . . . . . . . . . . . . . . . . . 50
4.3 Link1Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4 Link4Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.5 Convergelink1and2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.1 Link2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.2 Link3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.3 Link5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.4 Link6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.5 Link7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.6 Converge3and4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.7 converge5and6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.8 converge7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.9 End-to-EndDelayNode5Unicast . . . . . . . . . . . . . . . . . . . . 78
6.10 End-to-EndDelayNode6Unicast . . . . . . . . . . . . . . . . . . . . 79
6.11 End-to-EndDelayNode7Unicast . . . . . . . . . . . . . . . . . . . . 80
6.12 End-to-EndDelayNode5Passive . . . . . . . . . . . . . . . . . . . . . 81
6.13 End-to-EndDelayNode6Passive . . . . . . . . . . . . . . . . . . . . . 82
6.14 End-to-EndDelayNode7Passive . . . . . . . . . . . . . . . . . . . . . 83
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Acronyms
ANA AutonomicNetworkArchitecture
IC InformationChannel
IDP InformationDispatchPoint
FB FunctionalBlock
cfEngine ConfigurationEngine
SLA ServiceLevelAgreement
TCP/IP TransmissionControlProtocol/InternetProtocol
MANet MobileAdhocNetwork
SNMP SimpleNetworkManagementProtocol
NAT NetworkAddressTranslation
ISP InternetServiceProvider
ICMP InternetControlMessageProtocol
MCMC MarkovchainMonteCarlo
OGC OfficeofGovernmentCommerce
ITIL InformationTechnologyInfrastructureLibrary
TTL TimeToLive
AI ArtificialIntelligent
OSPF OpenShortestPathFirst
Wi-Fi WirelessFidelity
TCL ToolCommandLanguage
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LISTOFFIGURES
OTCL ObjectToolCommandLanguage
I.I.D IndependentandIdenticallyDistributed
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List of Tables
3.1 howtoderivetopologymatrix . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 digitalizationtable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3 end-to-enddelays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4 inter-arrivaltime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.5 end-to-enddelayestimationwithqueuemodel . . . . . . . . . . . . . . . 40
3.6 end-to-enddelayestimationwithqueuemodel . . . . . . . . . . . . . . . 41
4.1 absolutedifferenceofvarianceforendnodes . . . . . . . . . . . . . . . . 51
4.2 Correlationtableofvariouslinkmeasurements . . . . . . . . . . . . . . . 54
4.3 Absolutedifferenceofvariancesofvariousmeasurementsfor400probes . . 55
4.4 atableofvariouslink1and2L relativenormmeasurements . . . . . . . 56
1
6.1 Correlationtableofvariousmeasurementfor1000probesmeasurements . . 72
6.2 Correlationtableofvariousfor400probesmeasurements . . . . . . . . . 73
6.3 Absolutedifferencesofvariancesvariousmeasurementsfor400probeexperiments 76
6.4 Normtableofvariousmeasurementscomputedover50probes . . . . . . . 77
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