BRIEFING TITLE - ALL CAPS 30 Jan 01

Complex InformationSystems21 Aug 2013Robert J. Bonneau, Ph.D.AFOSR/RSLIntegrity Service Excellence#Goals:

Preserve critical information structure and minimize latency over a heterogeneous distributed network and system Ensure network and system robustness and stability under a diverse set ofresource constraints and manage not assuming static models Find invariant properties for a given network and system from a distributed set of observations and predict network behavior Develop unifying mathematical approach to discovering fundamental principles of networks and system and use them in network and system design

Payoffs: Preserve information structures in a network rather than just delivering packets or bits Quantify likelihood of a given network management policy to support critical mission functions Predict and manage network and system failure comprehensivelyComplex Networks and Systems#2Foundations of Information Systems Model heterogeneous distributed systems using unified mathematical framework through previous measurement and validate

Verify the properties of a given system application through measurement of a limited set of system parameters and assess mission risk

Define general architectural principles of design through unified assessment of system operating properties

Generalize design properties to universal system architectural principlesProgram ObjectivesPayoff Assess and verify properties of a distributed heterogeneous system where there is limited access to its elements

Assess dynamic Air Force system mission performance and assess risk of failure#SystemProperties

Information SystemsResearchMeasure SystemInformation & Verify Properties

DiverseTypes of SystemsComplex networks and systems uses measured information to assure, manage, predict, and design distributed networks, systems, and architecturesComplex Networks and Information Systems RoadmapDynamic, Heterogeneous,Air Force Systems

CriticalInformation

System Measurement

Local Network/Systems ResearchAssure CriticalInformation Delivery

Network/Systems Management ResearchManage Information FlowGlobal Network/Systems ResearchPredict Network Performance

#4Local Network/System Research: Preserving Information Content Statistical geometric coding structures are used to transport diverse sets of information in a network and system and preserve its critical structureInformation Timescale tContent InformationDistributionContentInformation LossWith InterferenceContentInformationRecoveryLess: Latency/Computation/StorageMore: Information LossWith InterferenceLess: Information Loss WithInterferenceMore: Latency/Computation/ StorageRecoveredInformationInformationLoss DistributedInformation LossMeasurableInformation LossSignificantInformationSourceDeterministic/MinimalCoding(ex: Trellis Code)Hybrid Code(ex: Network Code) Random Code(ex: Rateless Code)t packets, variables, registers,Recover UsingCodingRecover WithCode and RetransmitRecover WithRetransmission#5Less: Information Loss WithDisruptionMore: Latency, Difficult toControlLess: LatencyMore: Information Loss WithDisruption, ControllableInformationSourcesInformation Timescale t

Protocol/Policy Information DistributionProtocol/PolicyInformation LossWith InterferenceProtocol/Policy InformationRecoverySource 1Source 2Source 3t groups of packets, subroutine,virtual mem.The state of information transfer on a network changes with network and system management policy and protocol Particularly important to the Air Force given its unique heterogeneous mobile infrastructureNetwork/System Management Research: Guaranteeing Information TransferRecoveredInformationMessage 1Message 2Message 3DeterministicRouting(ex: OSPF)Hybrid Routing(ex: OLSR)Random Protocol(ex: Flooding)Recover WithRedundancy and RetransmitRecover WithRedundancyRecover WithRetransmissionInformationLoss DistributedInformation LossMeasurableInformation LossSignificant#6Less: Latency/Disruption TolerantMore: ControllableLess: Information Loss UnderDisruptionMore: Latency, ResourceIntensiveInformationSourcesInformation Timescale tArchitecture InformationDistributionArchitectureInformation LossWith InterferenceArchitectureInformationRecoverySource 1Source 2Source 3RecoveredInformationMessage 1Message 2Message 3t blocks ofinformation,program, virtualmemory We wish to develop information invariants that can be used to assess network/system performanceGlobal Network/System Research: Architecture Performance Invariants and PredictionDeterministicRouting(ex: Core/Backbone)Hybrid Network (Mesh) Random Network(ex: Mobile Ad Hoc)Reroute InformationReroute and ChangeDistributionChange InformationDistributionInformationLoss DistributedInformation LossMeasurableInformation LossSignificant#7Example: Unified Mission Assured Architecture Current networks are managed with multiple protocols depending on their taxonomy Air Force networks, particularly Airborne Networks are heterogeneous A unified network approach should adapt to the conditions and provide design principles

Less: Disruption Tolerant, LatencyMore: Information Loss Under Interference, Observable/Controllable

Less: Information Loss Under Interference, Observable/Controllable More: Disruption Tolerant, LatencyDesign PrinciplesAccordingTo ConstraintsAdapt AccordingTo Measurements#Foundations of Information SystemsHybrid ArchitectureHybrid ProtocolHybrid Content Random ArchitectureRandom ProtocolRandom ContentDeterministicArchitectureDeterministicProtocolDeterministic ContentMeasured Performance RegionsHeterogeneousInformationNetwork States(packets, packet blocks,packet groups)Software States(variable, subroutine,program)Hardware States(register, ram, virt. mem)SystemMeasurementsLess: Information Loss UnderDisruption/LiveMore: Latency, ResourceIntensive/SafeLess: Latency/Disruption Tolerant/SafeMore: Controllable/LiveBest IntegratedPerformance Region(timescale/level of abstraction) GlobalPropertiesStatisticalPropertiesStable/ResourcedSecureUnstable/Un-resourcedInsecureMeasure and verify information system properties among various system constraints#Measuring Information Systems Fundamental PropertiesUnits of information translate across heterogeneous domains and can be used to measure and quantify system performance- Taking this approach can lead to a unified systems and security strategy

DeterministicProtocolDistributionTime Evolution(GlobalProperties)Deterministic Heterogeneous RandomContent(local)System Policy/Protocol(management)SystemStructure(global)

DeterministicContent

HeterogeneousSystemHeterogeneousProtocolDeterministicSystem(1/informationtimescale) FrequencyDataNetworkPacketPacketGroupsPacketBlocksWirelessNetworkModulation UnitWaveformSignalArrayHardware/SoftwareRegister/VariableRam/SubroutineVirtual Mem./ProgramSocialWordsPhrasesNewsReports/BlogsBiologicalDNAProteinSynth.CellFunctionBasic Information Unit ScalesDigitalSystemsGeneral SystemsRandomProtocol

RandomContentHeterogeneousContent

RandomSystem Measured System Properties

Not Resourced,Not Stable,Not Secure

DesignExcluded Properties

Resourced,Stable,Secure,(Safe)DesignIncluded Properties#10Algorithms for Information Networks If we would like to estimate, detect, control, or predict networks, there are many algorithms that have been adapted to the relevant network conditions We would like new classes of integrated algorithms that can adapt across many dynamic network conditionsDynamic/Non-stationaryStatic/StationaryRandomDeterministicTimeFrequency/ScaleStationaryMarkov Dec.ProcessMin-maxEstimationWienerFilterAdaptiveMatchedFiltersKalmanFilterBootstrapMethodsExtendedKalmanFilterSequentialProbabilityRatio TestsParticleFilteringArchitectureHybridStatisticsProtocol/PolicyContentMore: Robust to Change/ComputationallyIntensiveLess: Robust to Change/ComputationallyIntensiveCritical Space ofNetwork PerformanceNecessaryAlgorithm Properties#Comprehensive Systems Modeling Model heterogeneous distributed systems using unified, modular, composable and scalable mathematical framework from previous measurement and system specification- Use new statistical, algebraic, and geometric representations and theory for modularized representations and composable into a modeling framework Unified RepresentationModular, Composable, Scalable Model of Unified SystemResource PolicySecurity FrameworkDatabase Arch.Operating SystemProg. LanguagesDesign ToolsMissionApplicationsPhysical Environ.Resource Const.ProcessingHardwareNetworkSystem of InterestMathematical ModelsStatistical, Algebraic, Geometric, #Measurement-Based System Verification Verify the properties of a given unified system through measurement of a limited set of parameters and calculate system risk of not meeting mission requirements Assess risk by distance between properties of desired representation (model) and measured properties - Incorporate risk of sparse measurementDesirable Properties:(Example) Robustness to DisruptionUndesirable Properties:(Examples) Latency, Interference,Computational OverheadMeasurementMissionRequirementsPerformance VerificationLow MissionRiskMedium MissionRiskHigh MissionRiskRisk AssessmentMeasured PropertiesDesired Properties#13Measurement Validation Trade-space Define general application architectural and policy validation principles through unified assessment of system operating risk- Apply to existing architectures through policy implementationArchitecturally Validated Modalities(low mission risk)Architecturally Excluded Modalities(high mission risk)System Operating Trade-space#MissionPerformanceGuarantees

Cloud ComponentSpace

AirborneTerrestrialIntroduce AdvancedMathematical and ModelingTechniques Into SystemComponents

AdvancedMathematicalAlgorithmCurrent & FutureSystem Component

SystemComponentsComplex Information Systems Current & Future ArchitecturesIntroduce measurement algorithms and components into existing systems architectures Use measurement based verification strategies to assure mission performance Statistical invariants for modeling based on measured data to validate models Incorporate algorithms into new generations of semiconductors for distributedonline system assessment

Systems Components inArchitecture + Future

MathematicalSystemsAnalysis

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