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Complex Information Systems

Complex Information Systems. 21 Aug 2013. Robert J. Bonneau, Ph.D. AFOSR/RSL. Complex Networks and Systems. Goals: Preserve critical information structure and minimize latency over a heterogeneous distributed network and system

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Complex Information Systems

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  1. Complex Information Systems 21 Aug 2013 Robert J. Bonneau, Ph.D. AFOSR/RSL

  2. Complex Networks and Systems • 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 of • resource 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 comprehensively

  3. Foundations of Information Systems Program Objectives • 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 principles Payoff • 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

  4. Complex Networks and Information Systems Roadmap Complex networks and systems uses measured information to assure, manage, predict, and design distributed networks, systems, and architectures Dynamic, Heterogeneous, Air Force Systems Local Network/Systems Research • Assure Critical • Information Delivery Network/Systems Management Research Manage Information Flow Information Systems Research • Measure System • Information & Verify Properties Critical Information System Measurement Diverse Types of Systems Global Network/Systems Research • Predict Network Performance System Properties

  5. Local 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 structure Content Information Loss With Interference Content Information Recovery Less: Information Loss With Interference More: Latency/Computation/ Storage Information Timescale t Content Information Distribution Random Code (ex: Rateless Code) Information Loss Distributed Recover Using Coding t  packets, variables, registers, Recovered Information Information Source Recover With Code and Retransmit Information Loss Measurable Hybrid Code (ex: Network Code) Recover With Retransmission Information Loss Significant Deterministic/Minimal Coding (ex: Trellis Code) Less: Latency/Computation/ Storage More:Information Loss With Interference

  6. Network/System Management Research: Guaranteeing Information Transfer 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 infrastructure Less: Information Loss With Disruption More:Latency, Difficult to Control Protocol/Policy Information Loss With Interference Protocol/Policy Information Recovery Protocol/Policy Information Distribution Information Timescale t Information Sources Random Protocol (ex: Flooding) Information Loss Distributed t  groups of packets, subroutine, virtual mem. Recover With Redundancy Source 1 Message 1 Recover With Redundancy and Retransmit Hybrid Routing (ex: OLSR) Message 2 Information Loss Measurable Source 2 Recovered Information Message 3 Source 3 Deterministic Routing (ex: OSPF) Recover With Retransmission Information Loss Significant Less: Latency More: Information Loss With Disruption, Controllable

  7. Global Network/System Research: Architecture Performance Invariants and Prediction • We wish to develop information invariants that can be used to assess network/ • system performance Architecture Information Loss With Interference Architecture Information Recovery Architecture Information Distribution Information Timescale t Information Sources Less:Information Loss Under Disruption More: Latency, Resource Intensive t  blocks of information, program, virtual memory Source 1 Information Loss Distributed Message 1 Change Information Distribution Random Network (ex: Mobile Ad Hoc) Source 2 Message 2 Recovered Information Reroute and Change Distribution Information Loss Measurable Hybrid Network (Mesh) Source 3 Message 3 Deterministic Routing (ex: Core/Backbone) Reroute Information Information Loss Significant Less: Latency/Disruption Tolerant More: Controllable

  8. Example: 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 Design Principles According To Constraints Less: Information Loss Under Interference, Observable/Controllable More: Disruption Tolerant, Latency Less: Disruption Tolerant, Latency More:Information Loss Under Interference, Observable/Controllable AdaptAccording To Measurements

  9. Foundations of Information Systems Measure and verify information system properties among various system constraints Measured Performance Regions Heterogeneous Information (timescale/level of abstraction) Less:Information Loss Under Disruption/Live More: Latency, Resource Intensive/Safe Network States (packets, packet blocks, packet groups) Random Content Deterministic Content Hybrid Content Software States (variable, subroutine, program) Best Integrated Performance Region Deterministic Protocol Hybrid Protocol Random Protocol Hardware States (register, ram, virt. mem) Deterministic Architecture Hybrid Architecture Random Architecture System Measurements Less: Latency/Disruption Tolerant/Safe More: Controllable/Live Statistical Properties Global Properties Stable/Resourced Secure Unstable/Un-resourced Insecure

  10. Measuring Information Systems Fundamental Properties Units 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 Measured System Properties Basic Information Unit Scales Frequency Data Network Wireless Network Hardware/ Software Social Biological (1/information timescale) Heterogeneous Content Random Content Deterministic Content Register/ Variable DNA Packet Words Modulation Unit Packet Groups Phrases Ram/ Subroutine Protein Synth. Waveform Random Protocol Heterogeneous Protocol Deterministic Protocol Content (local) News Reports/ Blogs Packet Blocks Virtual Mem./ Program Signal Array Cell Function Deterministic System Heterogeneous System Random System Distribution System Policy/ Protocol (management) Deterministic Heterogeneous Random General Systems Digital Systems System Structure (global) Design Included Properties Design Excluded Properties Resourced, Stable, Secure, (Safe) Time Evolution (Global Properties) Not Resourced, Not Stable, Not Secure

  11. Algorithms 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 dynamicnetwork conditions Necessary Algorithm Properties Time Dynamic/ Non-stationary Extended Kalman Filter More: Robust to Change/Computationally Intensive Particle Filtering Kalman Filter Sequential Probability Ratio Tests Adaptive Matched Filters Bootstrap Methods Critical Space of Network Performance Less: Robust to Change/Computationally Intensive Stationary Markov Dec. Process Min-max Estimation Wiener Filter Static/ Stationary Random Deterministic Hybrid Statistics Architecture Protocol/ Policy Content Frequency/ Scale

  12. 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 Resource Policy Security Framework Database Arch. Operating System Prog. Languages Design Tools Mission Applications Physical Environ. Resource Const. Processing Hardware Network System of Interest Mathematical Models Unified Representation Modular, Composable, Scalable Model of Unified System Statistical, Algebraic, Geometric, …

  13. 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 measurement Measured Properties Mission Requirements Low Mission Risk Measurement Desirable Properties: (Example) Robustness to Disruption Medium Mission Risk Risk Assessment Desired Properties High Mission Risk Undesirable Properties: (Examples) Latency, Interference, Computational Overhead Performance Verification

  14. Measurement Validation Trade-space • Define general application architectural and policy validation principles through unified assessment of system operating risk • - Apply to existing architectures through policy implementation System Operating Trade-space Architecturally Validated Modalities (low mission risk) Architecturally Excluded Modalities (high mission risk)

  15. Complex Information SystemsCurrent & Future Architectures Introduce 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 distributed online system assessment Systems Components in Architecture + Future Space Mission Performance Guarantees Introduce Advanced Mathematical and Modeling Techniques Into System Components Airborne Cloud Component System Components Current & Future System Component Terrestrial Mathematical Systems Analysis Advanced Mathematical Algorithm

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