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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


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


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


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


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


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)



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



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

http://www.cise.nsf.gov/dddas

http://www.dddas.org/


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


• 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!)


• 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


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, …


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

http://eijournal.com/newsite/wp-content/uploads/2011/09/WARFIGHTER1.jpg


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


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




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-

http://www.afosr.af.mil/


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

….


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


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

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