an analytical model for network flow analysis ernesto gomez, yasha karant, keith schubert institute...
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![Page 1: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/1.jpg)
An Analytical Model for An Analytical Model for Network Flow AnalysisNetwork Flow Analysis
Ernesto Gomez, Yasha Karant, Keith SchubertErnesto Gomez, Yasha Karant, Keith SchubertInstitute for Applied SupercomputingInstitute for Applied Supercomputing
Department of Computer ScienceDepartment of Computer ScienceCSU San BernardinoCSU San Bernardino
The authors gratefully acknowledge the support of the The authors gratefully acknowledge the support of the NSF under award CISE 98-10708NSF under award CISE 98-10708
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
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One View of NetworkOne View of Network
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Network FlowsNetwork Flows
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
![Page 6: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/6.jpg)
Brief HistoryBrief History
Shannon-Hartley (classical channel Shannon-Hartley (classical channel capacity)capacity)C=B logC=B log22(1+SNR)(1+SNR)
Leland, Taqqu, Willinger, Wilson, Paxon, …Leland, Taqqu, Willinger, Wilson, Paxon, …Self-similar trafficSelf-similar traffic
Cao, Cleveland, Lin, Sun, RamananCao, Cleveland, Lin, Sun, RamananPoisson in limitPoisson in limit
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Stochastic vs. AnalyticStochastic vs. Analytic
Stochastic best tools currentlyStochastic best tools currentlyOpnet, NSOpnet, NSProblemsProblems
limiting caseslimiting casesImproving estimatesImproving estimates
Analytic (closed form equations)Analytic (closed form equations)Handles problems of stochasticHandles problems of stochastic Insight into structureInsight into structureFluid modelsFluid modelsStatistical MechanicsStatistical Mechanics
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
![Page 9: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/9.jpg)
OverviewOverview
Large number of entitiesLarge number of entitiesBulk propertiesBulk properties
Equilibrium or non-equilibrium propertiesEquilibrium or non-equilibrium propertiesTime-dependenceTime-dependence
Conservation over ensemble averagesConservation over ensemble averagesCan handle classical and quantum flowsCan handle classical and quantum flows
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Density Matrix FormalismDensity Matrix Formalism
Each component Each component Label by stateLabel by state
n = node source and destinationn = node source and destinationf = flow indexf = flow indexc = flow characteristicsc = flow characteristicst = time stept = time step
tcfn ,,,
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Density Matrix IIDensity Matrix II
Probability of a flow Probability of a flow
Element in Density Matrix isElement in Density Matrix is
Averaged PropertiesAveraged Properties
tcfn ,,,Pr
tcfntcfntcfn ,,,,,,,,,Pr
StcfntcfnStcfnxqxq ,,,,,,,,,Pr
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
![Page 13: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/13.jpg)
Poisson DistributionPoisson Distribution
!
,t
etp
t
is mean
Thin Tail
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Problem with PoissonProblem with Poisson
BurstBurstExtended period above the meanExtended period above the meanVariety of timescalesVariety of timescales
Long-range dependenceLong-range dependencePoisson or Markovian arrivalsPoisson or Markovian arrivals
Characteristic burst lengthCharacteristic burst lengthSmoothed by averaging over timeSmoothed by averaging over time
Real distribution is self-similar or multifractalReal distribution is self-similar or multifractalProven for EthernetProven for Ethernet
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Real versus PoissonReal versus Poisson
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Pareto DistributionPareto Distribution
Shape parameter (Shape parameter ())Smaller means heavier tailSmaller means heavier tail Infinite varience when 2 Infinite varience when 2 ≥ ≥ Infinite mean when 1 Infinite mean when 1 ≥ ≥
Location parameter (k)Location parameter (k) tt≥k≥k
1 tktp
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Pareto DistributionPareto Distribution
1 tktp
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
![Page 19: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/19.jpg)
Flow OriginationFlow Origination
UnicastUnicastOne sourceOne sourceOne destinationOne destinationMany segmentsMany segments
MulticastMulticastOne sourceOne sourceMany destinationsMany destinations
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Multicast PossibilitiesMulticast Possibilities
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OutlineOutline
Networks and FlowsNetworks and FlowsHistoryHistoryStatistical MechanicsStatistical MechanicsSelf-similar trafficSelf-similar trafficTraffic creation and destructionTraffic creation and destructionMaster Equation and traffic flowMaster Equation and traffic flow
![Page 22: An Analytical Model for Network Flow Analysis Ernesto Gomez, Yasha Karant, Keith Schubert Institute for Applied Supercomputing Department of Computer Science](https://reader030.vdocument.in/reader030/viewer/2022032516/56649c7d5503460f949320c6/html5/thumbnails/22.jpg)
Probability in Density MatrixProbability in Density Matrix
Tr = eTr = eHtHt (H is energy function)(H is energy function) Tr= (1+t/tTr= (1+t/tnsns))-1-1 Cauchy Boundary conditions Cauchy Boundary conditions
hypersurface of flow spacehypersurface of flow space Ill behavedIll behaved Gaussian quadrature, Monte Carlo, Pade ApproximationGaussian quadrature, Monte Carlo, Pade Approximation
tptcfnTrtcfn
tptcfnTrtcfndt
tcfnd
outinout
ininoutin
,,,,,,
,,,,,,,,,Pr
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Unicast Flow TimeUnicast Flow Time
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Future DirectionsFuture Directions
More detailed networkMore detailed networkBulk propertiesBulk propertiesOnline toolOnline tool