Transcript DDDAS/AFOSR BAA and Technology Horizons

InfoSymbiotics/DDDAS:
From Big Data and Big Computing to New Capabilities
ICCS2013/DDDAS Workshop
Date: June 5-7, 2013
Frederica Darema, Ph. D., IEEE Fellow
AFOSR
Air Force Research Laboratory
Integrity  Service  Excellence
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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 ever
Research 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)
InfoSymbiotic Systems
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
Experiments
Field-Data
User
“revolutionary” concept enabling
design, build, manage, understand complex 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
Experiment
Measurements
Field-Data
(on-line/archival)
User
F. Darema
Dynamic
Challenges:
Application Simulations Methods
Algorithmic Stability
Measurement/Instrumentation Methods
Computing Systems Software Support
Feedback & Control
Loop
Synergistic, Multidisciplinary Research3
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Examples of Areas of DDDAS Impact
from the “nano”-scale to the “terra”&“extra-terra”-scale
•
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, …
“revolutionary”
conceptCommunications,
enabling to design,
build, manage and understand complex systems
• Homeland Security,
Manufacturing
NSF/ENG
Blue Ribbon
Panel (Report
2006 – Tinsley Oden)
Terrorist attacks, emergency
response;
Mfg planning
and control
“DDDAS … key concept in many of the objectives set in Technology Horizons”
• Dynamic Adaptive Systems-Software
Dr. Werner Dahm, (former/recent) AF Chief Scientist
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)
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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
DDDAS
key concept
many
of the objectives
setReport
in Technology Horizons
Top
KTAs…
identified
in thein2010
Technology
Horizons
• •
• •
Autonomous
systems
Autonomous
systems
Autonomous
reasoning
and
learning
Autonomous
reasoning
and
learning
• •
• •
Spectral
mutability
Spectral
mutability
Dynamic
spectrum
access
Dynamic
spectrum
access
• •
• •
Resilient
autonomy
Resilient
autonomy
Complex
adaptive
systems
Complex
adaptive
systems
• •
• •
Quantum
key
distribution
Quantum
key
distribution
Multi-scale
simulation
technologies
Multi-scale
simulation
technologies
• •
• •
V&V
complex
adaptive
systems
V&V
forfor
complex
adaptive
systems
Collaborative/cooperative
control
Collaborative/cooperative
control
• •
• •
Coupled
multi-physics
simulations
Coupled
multi-physics
simulations
Embedded
diagnostics
Embedded
diagnostics
• •
• •
Autonomous
mission
planning
Autonomous
mission
planning
Cold-atom
INS
Cold-atom
INS
• •
• •
Decision
support
tools
Decision
support
tools
Automated
software
generation
Automated
software
generation
• •
• •
Chip-scale
atomic
clocks
Chip-scale
atomic
clocks
hoc
networks
AdAd
hoc
networks
• •
• •
Sensor-based
processing
Sensor-based
processing
Behavior
prediction
and
anticipation
Behavior
prediction
and
anticipation
• •
• •
Polymorphic
networks
Polymorphic
networks
Agile
networks
Agile
networks
• •
• •
Cognitive
modeling
Cognitive
modeling
Cognitive
performance
augmentation
Cognitive
performance
augmentation
• •
• •
Laser
communications
Human-machine
interfaces
Laser
communications
• •
Human-machine
interfaces
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Frequency-agile
systems
Frequency-agile
RFRF
systems
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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
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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)
Distribution
A: Approved
for Public Release, Unlimited Distribution
– PI: Salim Hariri (Arizona
State
U. Tucson)
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(1998- … precursor Next Generation Software Program)
SystemsSoftware – Runtime Compiler – Dynamic Composition – Performance Engineering
(2005 DDDAS Multi-Agency Program - NSF/NIH/NOAA/AFOSR)
(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
•
•
•
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(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
•
•
•
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•
•
•
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•
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Ghattas - MIPS: A Real-Time Measurement-Inversion-Prediction-Steering Framework
for Hazardous Events
How - Coordinated Control of Multiple Mobile Observing Platforms for Weather
Forecast Improvement
Bernstein – Targeted Data Assimilation for Disturbance-Driven Systems: Space
weather Forecasting
McLaughlin - Data Assimilation by Field Alignment
Leiserson - Planet-in-a-Bottle: A Numerical Fluid-Laboratory
Chryssostomidis - Multiscale Data-Driven POD-Based Prediction of the Ocean
Ntaimo - Dynamic Data Driven Integrated Simulation and Stochastic Optimization for
Wildland Fire Containment
Allen - DynaCode: A General DDDAS Framework with Coast and Environment Modeling
Applications
Douglas - 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
•
•
•
•
•
•
(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)
•
•
•
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•
•
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
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 Maketo-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)
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Advances in Capabilities through DDDAS
DDDAS - Clearly articulated concept/paradigm:
• integration of application simulation/models with the application
instrumentation components in a dynamic feed-back control loop
speedup of the simulation, by replacing computation with data in specific
parts of the phase-space of the application
and/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 controllers
increased computation/communication capabilities; ubiquitous heterogeneous sensoring
• unification from the high-end to the real-time data acquisition and control
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What makes DDDAS(InfoSymbiotics)
TIMELY, NOW MORE THAN EVER?
•
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)
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 systems
then 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 resources
Ubiquitous sensoring
opportunity: “smarter systems” – InfoSymbiotics DDDAS - the way for such capabilities
is important component of BIG DATA
(BigData – Wave #2!)
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Fundamental Science and Technology
Challenges for Enabling DDDAS Capabilities
• Application modeling (in the context of dynamic data inputs)
 dynamically invoke/select appropriate application components (models/algorithms)
depending on streamed data
 multi-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
• Measurements
 multiple modalities, space/time-distributed
 heterogeneous data management
• Systems supporting dynamic runtime environments
 extended spectrum of platforms
-- beyond traditional computational grids, beyond the “traditional” cloud,
to include sensor/instrumentation grids
 dynamic execution support on heterogeneous environments
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A while back we talked about Computational
Grids…
Heterogeneity within and across Platforms
Multicores in “measurement/data” Systems
• Multiple levels of hierarchies of processing nodes,
memories, interconnects, latencies
•Instruments, Sensors, Controllers, Networks, …
….
alg accelerator
tac-com
data
base
data
base
fire
cntl
High-End:
Grids-in-a-Box
(GiBs)
MPP
Clusters
fire
cntl
SAR
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
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Integrated Information Processing Environments
Multicores EVERYWHERE !!!
….
MPP
NOW
Radar&On-BoardProcessing
End-to-End Methods
Across System Layers/Components
from Data-Computation-Communication to Knowledge-Decision-Action
DDDAS - Integrated/Unified Application Platforms
Adaptable Computing and Data Systems Infrastructure
spanning the high-end to real-time data-acquisition & control systems
manifesting heterogeneous multilevel distributed parallelism
system architectures – software architectures
High-End Computing (peta-, exa-) ……. Sensors/Controls
overlapping multicore needs – power-efficiency, fault-tolerance
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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
Technological
Advances
for exascale
–
•
–
•
•
•
–
•
Trickle-down
low-end/UserDevices
Trickle-down
to
Sensors/UserDevices
Ubiquitous to
Sensors/
User Devices
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
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Distribution
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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
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– effort has started; need
to update/add & complete; set a timeline
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Summary
on 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
InfoSymbiotics/DDDAS
Key
for new capabilities
in many Scientific, Engineering, Societal fields
-BigData
& BigComputingTransformative Innovations through
University-Industry/Business partnerships catalyzed by Government
International component is important!
DDDAS/InfoSymbiotics
AFOSR BAA www.afosr.af.mil
www.dddas.org
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Back-ups
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Dynamic Runtime Support (NSF/NGS Program ‘98-’04; ’05-’07)
Runtime Compiling System (RCS) and Dynamic Application Composition
Application
Model
Dynamic Analysis
Situation
Distributed
Programming
Model
Application
Program
Compiler
Front-End
Launch
Application (s)
Interacting with Data Systems
(archival data and on-line instruments)
Application
Intermediate
Representation
Compiler
Back-End
Dynamically
Link
&
Execute
Performance
Measuremetns
&
Models
Application
Components
&
Frameworks
Distributed Computing Resources
Distributed Platform
tac-com
alg accelerator
….
F. Darema
MPP
NOW
fire
cntl
data
base
data
base
fire
cntl
SAR
SP
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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
Multidisciplinary Research
Systems engineering requires novel methods that can:
& Technology Development
– model, monitor, & analyze all components of such systems
– at multiple levels of abstraction
Models
& Resource
Monitoring
– individuallyPerformance
and composed
as a system
architectural
framework
<->Operation Cycle, System Evolution
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Systems Engineering
Example:
sw/hw Performance Modeling and Analysis Framework
Application
Models
Sys.Software
Models
(IO/File)
Sys.Software
Models
(OS scheduler)
Sys.Software
Models
(Nets Resources)
Hardware
Models
Application
Distributed Applications
Advanced
Execution
Collaboration
Systems
Visualization
Environments
Prog.Models
/
Parallel and Distributed
Compilers
Operating Authenication
Systems
Scalable I/O
Libraries
Authorization
Data Management
Fault Recovery
Tools
Archiving/Retrieval
Services
Services
Distributed Systems Management
(NetsArchitecture)
Hardware
Models
Distributed, Heterogeneous, Dynamic, Adaptive
Computing Platforms and Networks
(CPU&Mem Arch)
Hardware
Models
(Platform
Architecture)
Memory
Technology
CPU
Technology
Device
Technology
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...
Layer/Component
s
Application
Support/Services
Layer/Component
s
OS/Middleware
Support/Services
Layer/Component
s
Nets/Middleware
Support/Services
Layer/Component
s
CPU&Memory
Layer/Component
s
Platform/Nets
Layer/Component
s
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Modeling Multiple views of the system
The Operating Systems’ view
Application
Models
...
IO / File
Models
Distributed Applications
Languages
Compilers
Libraries
Tools
Visualization Collaboration
Environments
Scalable I/O Authenication
/
Data Management Authorization
Archiving/Retrieval
Dependability
Services
Services
Other Services
...
OS
Scheduler
Models
Distributed Systems Management
Architecture /
Network
Models
Distributed, Heterogeneous, Dynamic, Adaptive
Computing Platforms and Networks
Memory
Models
CPU
Device
Memory
Technology Technology Technology
...
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