1 distribution a: approved for public release, unlimited distribution integrity service excellence...
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1Distribution A: Approved for Public Release, Unlimited Distribution
Integrity Service Excellence
Frederica Darema, Ph. D., IEEE Fellow
AFOSR
Air Force Research Laboratory
InfoSymbiotics/DDDAS: From Big Data and Big Computing to New Capabilities
ICCS2013/DDDAS Workshop
Date: June 5-7, 2013
2Distribution A: Approved for Public Release, Unlimited Distribution
OUTLINE
InfoSymbiotic Systems – BigData and BigComputing• The essence of Dynamic Data Driven Applications Systems (DDDAS)• Examples of new capabilities through DDDAS (aerospace & other)
Why now timely more than everResearch and Technology Development Modalities:• Multidisciplinary R&D
• Fostering Transformative Innovations• Expanding Fundamental Knowledge and Capabilities• Transformative Partnerships across Academe-Industry
Technology Advances/Trends:• Multicores - Exascale – Unified High-End with RT/DA&Control• Ubiquitous Sensoring - New Wave in Data Intensive• Increased emphasis in multiscale modeling and UQ; Analytics • Systems Engineering
Summary
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Dynamic Data Driven Applications Systems(DDDAS)
F. Darema
ExperimentMeasurements
Field-Data(on-line/archival)
User
Theory
(First Principles) Simulations
(Math.Modeling
Phenomenology
DesignModeling)
Dynamic Feedback & Control
Loop
DDDAS: ability to dynamically incorporate additional data into an executing application, and in reverse, ability of an application to dynamically steer the measurement process
Measurements ExperimentsField-Data
User
Theory
(First P
rincip
les)
OLD
(serialized and static)
Simula
tions
(Math
.Modelin
g
Phenomenolo
g
y)
“revolutionary” concept enablingdesign, build, manage, understand complex systems
InfoSymbiotic Systems
Dynamic Integration of Computation & Measurements/Data
Unification of Computing Platforms & Sensors/Instruments
(from the High-End to the Real-Time,to the PDA)DDDAS – architecting & adaptive mngmnt of sensor systems
Challenges:Application Simulations MethodsAlgorithmic Stability Measurement/Instrumentation MethodsComputing Systems Software Support
Synergistic, Multidisciplinary Research
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Examples of Areas of DDDAS Impact
• Physical, Chemical, Biological, Engineering Systems
Materials, system health monitoring, molecular bionetworks, protein folding.. chemical pollution transport (atmosphere, aquatic, subsurface), ecological systems, …
• Medical and Health Systems
MRI imaging, cancer treatment, seizure control
• Environmental (prevention, mitigation, and response)
Earthquakes, hurricanes, tornados, wildfires, floods, landslides, tsunamis, …
• Critical Infrastructure systems
Electric-powergrid systems, water supply systems, transportation networks and vehicles (air, ground, underwater, space), …
condition monitoring, prevention, mitigation of adverse effects, …
• Homeland Security, Communications, Manufacturing
Terrorist attacks, emergency response; Mfg planning and control
• Dynamic Adaptive Systems-Software
Robust and Dependable Large-Scale systems
Large-Scale Computational Environments
List of Projects/Papers/Workshops in www.dddas.org
(+ recent/August2010 MultiAgency InfoSymbtiotics/DDDAS Workshop)
“revolutionary” concept enabling to design, build, manage and understand complex systems
NSF/ENG Blue Ribbon Panel (Report 2006 – Tinsley Oden) “DDDAS … key concept in many of the objectives set in Technology Horizons”
Dr. Werner Dahm, (former/recent) AF Chief Scientist
from the “nano”-scale to the “terra”&“extra-terra”-scale
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DDDAS/AFOSR BAA and Technology Horizons
• Context of Key Strategic Approaches of the Program– Multidisciplinary Research– Focus of advancing capabilities along the Key Areas identified
in the Technology Horizons, and the Energy Horizons and Global Horizons Reports
• Autonomous systems
• Autonomous reasoning and learning
• Resilient autonomy
• Complex adaptive systems
• V&V for complex adaptive systems
• Collaborative/cooperative control
• Autonomous mission planning
• Cold-atom INS
• Chip-scale atomic clocks
• Ad hoc networks
• Polymorphic networks
• Agile networks
• Laser communications
• Frequency-agile RF systems
• Spectral mutability
• Dynamic spectrum access
• Quantum key distribution
• Multi-scale simulation technologies
• Coupled multi-physics simulations
• Embedded diagnostics
• Decision support tools
• Automated software generation
• Sensor-based processing
• Behavior prediction and anticipation
• Cognitive modeling
• Cognitive performance augmentation
• Human-machine interfaces
Top KTAs identified in the 2010 Technology Horizons Report
• Autonomous systems
• Autonomous reasoning and learning
• Resilient autonomy
• Complex adaptive systems
• V&V for complex adaptive systems
• Collaborative/cooperative control
• Autonomous mission planning
• Cold-atom INS
• Chip-scale atomic clocks
• Ad hoc networks
• Polymorphic networks
• Agile networks
• Laser communications
• Frequency-agile RF systems
• Spectral mutability
• Dynamic spectrum access
• Quantum key distribution
• Multi-scale simulation technologies
• Coupled multi-physics simulations
• Embedded diagnostics
• Decision support tools
• Automated software generation
• Sensor-based processing
• Behavior prediction and anticipation
• Cognitive modeling
• Cognitive performance augmentation
• Human-machine interfaces
DDDAS … key concept in many of the objectives set in Technology Horizons
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Scope of AFOSR Supported DDDAS Projects
Materials modeling• Development of a Stochastic Dynamic Data-Driven System for Prediction of Materials
Damage– PI: Tinsley Oden (UT Austin), and Team
• Developing Data-Driven Protocols to study Complex Systems: The case of Engineered Granular Crystals (EGC)
– PI: Yannis Kevrekidis (Princeton Univ)• Dynamic Data-Driven Modeling of Uncertainties and 3D Effects of Porous Shape Memory
Alloys– PI: Craig Douglas (U of Wyoming), and Team
• Dynamic, Data-Driven Modeling of Nanoparticle Self Assembly Processes– Y. Ding (TAMU) and Team
AirVehicle Structural HealthMonitoring – Environment Cognizant• Advanced Simulation, Optimization, and Health Monitoring of Large Scale Structural
Systems– Y. Bazilevs (UCSD) and Team
• Dynamic Data-Driven Methods for Self-Aware Aerospace Vehicles – PI: K Willcox (MIT) and Team
• Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring– PI: Thomas Henderson (U. of Utah)
• Stochastic Logical Reasoning for Autonomous Mission Planning– Carlos A. Varela (RPI)
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Scope of AFOSR Supported DDDAS Projects
Spatial Situational Awareness (UAV Swarms + Ground Systems Coordination ) • Application of DDDAS Principles to Command, Control and Mission Planning for UAV
Swarms– PI: G. Madey (U. Of Notre Dame) and Team
• DDDAMS-based Urban Surveillance and Crowd Control via UAVs and UGVs– Young-Jun Son, Jian Liu, University of Arizona;
Spatial Situational Awareness (Co-operative Sensing UAV-Ground-Space)• Dynamic Data Driven Adaptation via Embedded Software Agents for Border Control
Scenario– Shashi Phoha, Doina Bein, Penn State
• Multiscale Analysis of Multimodal Imagery for Cooperative Sensing – Erik Blasch, Guna Seetharaman, RI Directorate, AFRL
• DDDAS for Object Tracking in Complex and Dynamic Environments (DOTCODE)– Anthony Vodacek , John Kerekes, Matthew Hoffman (RPI)
• New Globally Convex Models for Vision Problems using Variational Methods (LRIR)– PI: Guna Sheetharanam, AFRL-RI
• Symbiotic Partnership between Ground Observers and Overhead Image Analysis (LRIR)– PI: Brian Tsou, AFRL-RH
• Adaptive Stream Mining: A Novel Dynamic Computing Paradigm for Knowledge Extraction
– PI: Shuvra Bhattacharyya, U,. Of Maryland
8Distribution A: Approved for Public Release, Unlimited Distribution
Scope of AFOSR Supported DDDAS Projects
Energy Efficiencies• Energy-Aware Aerial Systems for Persistent Sampling and Surveillance
– E. W. Frew (U of Colorado-Boulder) and Team • DDDAMS-based Real-time Assessment and Control of Electric-Microgrids
– Nurcin Celik (University of Miami)
Space Weather and Atmospheric Events• Transformative Advances in DDDAS with Application to Space Weather Modeling
– D. Bernstein (U. of Michigan) and Team• DDDAS Approach To Volcanic Ash Transport & Dispersal Forecast
– A. Patra (Univ at Buffalo) and Team• Fluid SLAM and the Robotic Reconstruction of Localized Atmospheric Phenomena
– PI: Sai Ravela (MIT)• A Framework for Quantifying and Reducing Uncertainty in InfoSymbiotic Systems Arising
in Atmospheric Environments– PI: Adrian Sandu (Virginia Tech )
Systems Software• PREDICT: Privacy and Security Enhancing Dynamic Information Collection and
Monitoring– PI: Vaidy Sunderam (Emory U. )
• An Adaptive Property-Aware HW/SW Framework for DDDAS– PI: Philip Jones (Iowa State U. )
• DDDAS-based Resilient Cyberspace (DRCS)– PI: Salim Hariri (Arizona State U. Tucson)
9
(2000 -Through NGS/ITR Program)Pingali, Adaptive Software for Field-Driven Simulations
(2001 -Through ITR Program)• Biegler – Real-Time Optimization for Data Assimilation and Control of
Large Scale Dynamic Simulations• Car – Novel Scalable Simulation Techniques for Chemistry, Materials
Science and Biology• Knight – Data Driven design Optimization in Engineering Using
Concurrent Integrated Experiment and Simulation• Lonsdale – The Low Frequency Array (LOFAR) – A Digital Radio
Telescope• McLaughlin – An Ensemble Approach for Data Assimilation in the Earth
Sciences• Patrikalakis – Poseidon – Rapid Real-Time Interdisciplinary Ocean
Forecasting: Adaptive Sampling and Adaptive Modeling in a Distributed Environment
• Pierrehumbert- Flexible Environments for Grand-Challenge Climate Simulation
• Wheeler- Data Intense Challenge: The Instrumented Oil Field of the Future
(2002 -Through ITR Program)• Carmichael – Development of a general Computational Framework for the
Optimal Integration of Atmospheric Chemical Transport Models and Measurements Using Adjoints
• Douglas-Ewing-Johnson – Predictive Contaminant Tracking Using Dynamic Data Driven Application Simulation (DDDAS) Techniques
• Evans – A Framework for Environment-Aware Massively Distributed Computing
• Farhat – A Data Driven Environment for Multi-physics Applications • Guibas – Representations and Algorithms for Deformable Objects• Karniadakis – Generalized Polynomial Chaos: Parallel Algorithms for Modeling
and Propagating Uncertainty in Physical and Biological Systems • Oden – Computational Infrastructure for Reliable Computer Simulations• Trafalis – A Real Time Mining of Integrated Weather Data
(2003 -Through ITR Program)• Baden – Asynchronous Execution for Scalable Simulation in Cell Physiology• Chaturvedi– Synthetic Environment for Continuous Experimentation (Crisis
Management Applications)• Droegemeier-Linked Environments for Atmospheric Discovery (LEAD)• Kumar – Data Mining and Exploration Middleware for Grid and Distributed
Computing• Machiraju – A Framework for Discovery, Exploration and Analysis of
Evolutionary Data (DEAS)• Mandel – DDDAS: Data Dynamic Simulation for Disaster Management (Fire
Propagation)• Metaxas- Stochastic Multicue Tracking of Objects with Many Degrees of
Freedom• Sameh – Building Structural Integrity • {Sensors Program: Seltzer – Hourglass: An Infrastructure for Sensor
Networks}(2004 -Through ITR Program)
• Brogan – Simulation Transformation for Dynamic, Data-Driven Application Systems (DDDAS)
• Baldridge – A Novel Grid Architecture Integrating Real-Time Data and Intervention During Image Guided Therapy
• Floudas-In Silico De Novo Protein Design: A Dynamically Data Driven, (DDDAS), Computational and Experimental Framework
• Grimshaw: Dependable Grids• Laidlaw: Computational simulation, modeling, and visualization for understanding
unsteady bioflows• Metaxas – DDDAS - Advances in recognition and interpretation of human motion: An
Integrated Approach to ASL Recognition• Wheeler: Data Driven Simulation of the Subsurface: Optimization and Uncertainty
Estimation
Ghattas - MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework for Hazardous EventsHow - Coordinated Control of Multiple Mobile Observing Platforms for Weather Forecast ImprovementBernstein – Targeted Data Assimilation for Disturbance-Driven Systems: Space weather ForecastingMcLaughlin - Data Assimilation by Field AlignmentLeiserson - Planet-in-a-Bottle: A Numerical Fluid-Laboratory Chryssostomidis - Multiscale Data-Driven POD-Based Prediction of the OceanNtaimo - Dynamic Data Driven Integrated Simulation and Stochastic Optimization for Wildland Fire ContainmentAllen - DynaCode: A General DDDAS Framework with Coast and Environment Modeling ApplicationsDouglas - Adaptive Data-Driven Sensor Configuration, Modeling, and Deployment for Oil, Chemical, and Biological Contamination near Coastal Facilities
• Clark - Dynamic Sensor Networks - Enabling the Measurement, Modeling, and Prediction of Biophysical Change in a Landscape
• Golubchik - A Generic Multi-scale Modeling Framework for Reactive Observing Systems
• Williams - Real-Time Astronomy with a Rapid-Response Telescope Grid
• Gilbert - Optimizing Signal and Image Processing in a Dynamic, Data-Driven Application System
• Liang - SEP: Intergrating Multipath Measurements with Site Specific RF Propagation Simulations
• Chen - SEP: Optimal interlaced distributed control and distributed measurement with networked mobile actuators and sensors
• Oden - Dynamic Data-Driven System for Laser Treatment of Cancer• Rabitz - Development of a closed-loop identification machine for
bionetworks (CLIMB) and its application to nucleotide metabolism• Fortes - Dynamic Data-Driven Brain-Machine Interfaces • McCalley - Auto-Steered Information-Decision Processes for
Electric System Asset Management• Downar - Autonomic Interconnected Systems: The National Energy
Infrastructure• Sauer- Data-Driven Power System Operations • Ball - Dynamic Real-Time Order Promising and Fulfillment for
Global Make-to-Order Supply Chains• Thiele – Robustness and Performance in Data-Driven Revenue
Management• Son - Dynamically-Integrated Production Planning and Operational
Control for the Distributed Enterprise
+…* projects, funded through other sources and “retargeted by the researchers to incorporate DDDAS”
* ICCS/DDDAS Workshop Series, yearly 2003 – todate•other workshops organized by the community…
•2 Workshop Reports in 2000 and in 2006, in www.cise.nsf.gov/dddas & www.dddas.org
* www.dddas.org (maintained by Prof. Craig Douglas)
(2005 DDDAS Multi-Agency Program - NSF/NIH/NOAA/AFOSR)
(1998- … precursor Next Generation Software Program) SystemsSoftware – Runtime Compiler – Dynamic Composition – Performance Engineering
10Distribution A: Approved for Public Release, Unlimited Distribution
DDDAS - Clearly articulated concept/paradigm: • integration of application simulation/models with the application
instrumentation components in a dynamic feed-back control loopspeedup of the simulation, by replacing computation with data in specific
parts of the phase-space of the applicationand/or
augment model with actual data to improve accuracy of the model, improve analysis/prediction capabilities of application models
enable ~decision-support capabilities w simulation-modeling accuracy dynamically manage/schedule/architect heterogeneous resources, such as: networks of heterogeneous sensors, or networks of heterogeneous controllersincreased computation/communication capabilities; ubiquitous heterogeneous sensoring
• unification from the high-end to the real-time data acquisition and control
Advances in Capabilities through DDDAS
DDDAS/InfoSymbiotics
is the unifying paradigm
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• Emerging scientific and technological trends/advances ever more complex applications – systems-of-systems increased emphasis in complex applications modeling
increased computational capabilities (multicores) increased bandwidths for streaming data
Sensors– Sensors EVERYWHERE… (data intensive Wave #2) Swimming in sensors and drowning in data - LtGen Deptula (2010)
What makes DDDAS(InfoSymbiotics) TIMELY, NOW MORE THAN EVER?
Analogous experience from the past: “The attack of the killer micros(microprocs)” - Dr. Eugene Brooks, LLNL (early 90’s)
about microprocessor-based high-end parallel systemsthen seen as a problem – have now become an opportunity - advanced capabilities
Back to the present and looking to the future: “Ubiquitous Sensoring – the attack of the killer micros(sensors) – wave # 2”
Dr. Frederica Darema, AFOSR (2011, LNCC) challenge: how to deal with heterogeneity, dynamicity, large numbers of such resourcesopportunity: “smarter systems” – InfoSymbiotics DDDAS - the way for such capabilities
Ubiquitous sensoring is important component of BIG DATA
(BigData – Wave #2!)
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• Application modeling (in the context of dynamic data inputs)dynamically invoke/select appropriate application components (models/algorithms)
depending on streamed datamulti-modal, multi-scale – dynamically invoke multiple scales/modalities dynamic hierarchical decomposition (computational platform - sensor) and partitioning interfacing applications with measurement systems
• Algorithms tolerant to perturbations of dynamic data inputs UQ, uncertainty propagation
• Measurementsmultiple modalities, space/time-distributed heterogeneous data management
• Systems supporting dynamic runtime environmentsextended spectrum of platforms -- beyond traditional computational grids, beyond the “traditional” cloud, to include sensor/instrumentation grids dynamic execution support on heterogeneous environments
Fundamental Science and Technology Challenges for Enabling DDDAS Capabilities
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A while back we talked about Computational Grids…
Heterogeneity within and across Platforms•Multiple levels of hierarchies of processing nodes, memories, interconnects, latencies
MPP Clusters
SAR
tac-com
database
firecntl
firecntl
alg accelerator
database
SP
….
Grids: Adaptable Computing Systems Infrastructure
Fundamental Research Challenges&Needs in Applications and Systems Software• Map the multilevel parallelism in applications to the platforms multilevel parallelism and
for multi-level heterogeneity and dynamic resource availability• New programming models and environments, new compiler/runtime technology• Adaptively compositional software at all levels (applications/algorithms/sys-sw)• Systematic “performance-engineering” methods – systems & their environments
High-End: Grids-in-a-Box
(GiBs)
Multicores in “measurement/data” Systems•Instruments, Sensors, Controllers, Networks, …
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Integrated Information Processing Environmentsfrom Data-Computation-Communication to Knowledge-Decision-Action
En
d-t
o-E
nd
Met
ho
ds
Acr
oss
Sys
tem
Lay
ers/
Co
mp
on
ents
MPP NOW
….
Rad
ar&
On
-Bo
ard
-P
roce
ssin
g
Multicores EVERYWHERE !!!
High-End Computing (peta-, exa-) ……. Sensors/Controlsoverlapping multicore needs – power-efficiency, fault-tolerance
Adaptable Computing and Data Systems Infrastructurespanning the high-end to real-time data-acquisition & control systems
manifesting heterogeneous multilevel distributed parallelism system architectures – software architectures
DDDAS - Integrated/Unified Application Platforms
BigComputing
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Another Example of Driving Needs
• It has clearly been articulated that achieving exascale poses significant challenges, and requires paradigm changing approaches
• Achieving exascale amounts to climbing several walls!Technological Challenges --- $$$ Challenges
Technological Challenges– Power constraints• -> exploit multicores at reduced clock cycles -> need many of them – significant heterogeneity - multiple levels of hierarchy & heterogeneity • multicore unit, multicores on a chip, multilevel chip architecture • memory hierarchy heterogeneity (architecture, latency)• interconnect hierarchy heterogeneity (architecture, latency) – Scalability Challenge • exploit staggering numbers of processing nodes, and the complex
hierarchy– Accessing Data Challenge• Accessing memory – moving data across chips – high latency & power
expense– Fault tolerance / resilience
• Many more failure opportunities • past detection/recovery methods awfully inadequate 15
Technological Advances for exascale Trickle-down to low-end/UserDevicesTrickle-down to Sensors/UserDevicesUbiquitous Sensors/ User Devices
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DDDAS Outreach Repositories of DDDAS work
Presently: • www.dddas.org
– contains reports of funding agencies sponsored workshops– slides and papers of community organized DDDAS Workshops
• DDDAS Workshop at ICCS (10-year history)• Other DDDAS workshops organized by the community
• Papers in ICCS Proceedings of the DDDAS Workshop • Papers published by the research community
What we can do more? • Bridge with other funding agencies in the US, EU + OtherEurope, Asia(?)• More systematic outreach to additional research communities; e.g.:
– Dennis Bernstein: DDDAS Workshop at 2014 American Controls Conference– Darema: DDDAS Panel at 2013 American Controls Conference (June 17, 2013)– Ana Cortes, et al: DDDAS Workshop on Fire Modeling - EU-US-Asia(?)
• Include in www.dddas.org more systematically pointers to all DDDAS papers published by the research community
• A book on DDDAS – chapters representing projects; – uniform conceptual format of chapters; not a compendium of papers– effort has started; need to update/add & complete; set a timeline
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Summaryon Status of DDDAS/InfoSymbiotics
The DDDAS/InfoSymbiotics paradigm engenders:New discoveries and research and technology advances
at the interface and confluence of multiple science and engineering areas through multidisciplinary approaches and multidisciplinary efforts
Key role in BigData and BigComputing
Key for new capabilities in many Scientific, Engineering, Societal fields
Transformative Innovations through University-Industry/Business partnerships catalyzed by Government
International component is important!
DDDAS/InfoSymbioticsAFOSR BAA www.afosr.af.mil
www.dddas.org
InfoSymbiotics/DDDAS-BigData & BigComputing-
18
Back-ups
19F. Darema
DynamicallyLink
&Execute
Dynamic Runtime Support (NSF/NGS Program ‘98-’04; ’05-’07) Runtime Compiling System (RCS) and Dynamic Application Composition
ApplicationModel
Application Program
ApplicationIntermediate
Representation
CompilerFront-End
CompilerBack-End Performance
Measuremetns&
Models
DistributedProgramming
Model
ApplicationComponents
&Frameworks
Dynamic AnalysisSituation
LaunchApplication (s)
Interacting with Data Systems(archival data and on-line instruments)
Distributed Platform
Ada
ptab
leco
mpu
ting
Syst
ems
Infr
astr
uctu
re
Distributed Computing Resources
MPP NOW
SAR
tac-com
database
firecntl
firecntl
alg accelerator
database
SP
….
20Distribution A: Approved for Public Release, Unlimited Distribution
Systems Engineering
• Methods to design, build, and manage the operation, maintenance, extensibility, and interoperability of complex systems
• in ways where the systems’ performance, fault-tolerance, adaptability, interoperability and extensibility is considered throughout this cycle.
• Such complex systems include:
• heterogeneous and distributed sensor networks
• large platforms & other complex instrumentation systems & collections thereof
– need to exhibit:
• adaptability and fault tolerance under evolving internal and external conditions
• extensibility/interoperability with other systems in dynamic and adaptive ways
• Systems engineering requires novel methods that can:
– model, monitor, & analyze all components of such systems
– at multiple levels of abstraction
– individually and composed as a system architectural framework
Syst
ems Le
vel M
odel
ing
and
Analy
sis
<–>
Per
form
ance
Fra
mew
orks
Performance Models & Resource Monitoring <->Operation Cycle, System Evolution
New Directions in
Systems Engineering
Multidisciplinary Research& Technology Development
21Distribution A: Approved for Public Release, Unlimited Distribution
Authenication
/
Authorization
Fault Recovery
Services
Distributed Systems Management
Distributed, Heterogeneous, Dynamic, AdaptiveComputing Platforms and Networks
DeviceTechnology . . .
CPUTechnology
Visualization
Scalable I/OData Management
Archiving/Retrieval
Services
Collaboration Environments
Distributed Applications
MemoryTechnology
Systems EngineeringExample:
sw/hw Performance Modeling and Analysis Framework
Prog.Models
Libraries
Tools
Compilers
Advanced Execution Systems
Parallel and Distributed
Operating Systems
Syst e
m M
od
el in
g a
nd
An
al y
sis
ApplicationModels
Sys.Software Models(IO/File)
Sys.Software Models
(OS scheduler)
HardwareModels
(NetsArchitecture)
HardwareModels
(CPU&Mem Arch)
HardwareModels
(Platform Architecture)
Sys.Software Models
(Nets Resources)
ApplicationLayer/Components
Application Support/Services
Layer/Components
OS/MiddlewareSupport/Services
Layer/Components
Nets/MiddlewareSupport/Services
Layer/Components
CPU&MemoryLayer/Components
Platform/NetsLayer/Components
22
Authenication/ Authorization
DependabilityServices
Distributed Systems Management
VisualizationScalable I/O
Data ManagementArchiving/Retrieval
ServicesOther Services . . .
Collaboration Environments
Distributed Applications
Distributed, Heterogeneous, Dynamic, AdaptiveComputing Platforms and Networks
DeviceTechnology . . .CPU
TechnologyMemory
Technology
Application Models
Architecture /Network Models
MemoryModels
OSScheduler
Models
IO / FileModels
. . . Languages
LibrariesTools
Compilers
Modeling Multiple views of the systemThe Operating Systems’ view