MODELING FOR SUSTAINABILITY Or How to Make Smart CPS Smarter? - - PowerPoint PPT Presentation

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MODELING FOR SUSTAINABILITY Or How to Make Smart CPS Smarter? - - PowerPoint PPT Presentation

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MODELING FOR SUSTAINABILITY Or How to Make Smart CPS Smarter? WORKSHOP MODELS@RUNTIME @ MODELS, OCTOBER, 2018 An earlier version of this talk is available at http://goo.gl/ksGq4N BENOIT COMBEMALE BENOIT COMBEMALE PROFESSOR, UNIV. TOULOUSE &

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WORKSHOP MODELS@RUNTIME @ MODELS, OCTOBER, 2018

An earlier version of this talk is available at http://goo.gl/ksGq4N

MODELING FOR SUSTAINABILITY

Or How to Make Smart CPS Smarter?

BENOIT COMBEMALE

PROFESSOR, UNIV. TOULOUSE, FRANCE

HTTP://COMBEMALE.FR BENOIT.COMBEMALE@IRIT.FR @BCOMBEMALE

BENOIT COMBEMALE

PROFESSOR, UNIV. TOULOUSE & INRIA, FRANCE

HTTP://COMBEMALE.FR BENOIT.COMBEMALE@IRIT.FR @BCOMBEMALE

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Complex Software-Intensive Systems

Software intensive systems

▸ Multi-engineering approach ▸ Domain-specific modeling ▸ High variability and customization ▸ Software as integration layer ▸ Openness and dynamicity

Modeling for Sustainability Benoit Combemale @ MRT 2018 2

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Aerodynamics Authorities Avionics Safety Regulations Airlines Propulsion System Mechanical Structure Environmental Impact Navigation Communications Human- Machine Interaction

3

Multiple concerns, stakeholders, tools and methods

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4 Aerodynamics Authorities Avionics Safety Regulations Airlines Propulsion System Mechanical Structure Environmental Impact Navigation Communications Human- Machine Interaction Heterogeneous Modeling

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Model-Driven Engineering

Distribution

« Service Provider Manager » Notification Alternate Manager « Recovery Block Manager » Complaint Recovery Block Manager « Service Provider Manager » Notification Manager « Service Provider Manager » Complaint Alternate Manager « Service Provider Manager » Complaint Manager « Acceptance Test Manager » Notification Acceptance Test Manager « Acceptance Test Manager » Complaint Acceptance Test Manager « Recovery Block Manager » Notification Recovery Block Manager « Client » User Citizen Manager

Fault tolerance

Roles Activities Views Contexts

Security Functional behavior

Book state : String User borrow return deliver setDamaged res erv e

Use case Platform Model

Design Model

Code Model

Change one Aspect and Automatically Re-Weave: From Software Product Lines… ..to Dynamically Adaptive Systems

  • J. Whittle, J. Hutchinson, and M. Rouncefield, “The State of Practice in Model-

Driven Engineering,” IEEE Software, vol. 31, no. 3, 2014, pp. 79–85.

"Perhaps surprisingly, the majority of MDE examples in our study followed domain-specific modeling paradigms"

Modeling for Sustainability Benoit Combemale @ MRT 2018 5

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From Software Systems

▸software design models for functional

and non-functional properties

Engineers

System Models Software

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To Cyber-Physical Systems

▸multi-engineering design models

for global system properties

▸models @ runtime (i.e., included

into the control loop) for dynamic adaptations

Engineers

System Models Cyber-Physical System sensors actuators Physical System Software

<<controls>> <<senses>>

Modeling for Sustainability Benoit Combemale @ MRT 2018 14

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To Smart Cyber-Physical Systems

▸ an

anal alysis mo models (incl. large-scale simulation, constraint solver) of the surrounding context related to global phenomena (e.g. physical, economical, and social laws)

▸ pr

predic ictive ive mo models (predictive techniques from AI, machine learning, SBSE, fuzzy logic)

▸ us

user mo models (incl., general public/community preferences) and re regulations (political laws)

Engineers

System Models Smart Cyber-Physical System Context sensors actuators Physical System Software

<<controls>> <<senses>>

Modeling for Sustainability Benoit Combemale @ MRT 2018 15

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What about Scientific Modeling?

▸Models (computational and data-intensive sciences) for

analyzing and understanding physical phenomena

Context

Heuristics-Laws

Scientists

Physical Laws (economic, environmental, social)

Modeling for Sustainability Benoit Combemale @ MRT 2018 16

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Simulator Context Simulation Processes

Heuristics-Laws

Scientists

Physical Laws (economic, environmental, social)

<<represents>>

What about Scientific Modeling?

▸ Sim

imul ulator tors for tradeoff analysis,

what-if scenarios, analysis of alternatives and adaptations to environmental changes, etc.

Modeling for Sustainability Benoit Combemale @ MRT 2018 17

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Towards Unifying Modeling Foundations

▸ Convergence of en

engin gineer eerin ing and scien scientif ific ic models

▸ Prescriptive requires descriptive models ▸ Descriptive requires prescriptive models

▸ Grand Challenge: a modeling framework to support the integration

  • f data from sensors, open data, laws, regulations, scientific models

(computational and data-intensive sciences), engineering models and preferences.

▸ Domain-specific languages (DSLs) for socio-technical coordination

▸ to engage engineers, scientists, decision makers, communities and the general public ▸ to integrate analysis/predictive/user models into the control loop of smart CPS

Modeling for Sustainability Benoit Combemale @ MRT 2018 18

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Sustainability Systems

▸ Sustainability systems are smart-CPS managing resource production,

transport and consumption for the sake of sustainability

▸ Ex: smart grids, smart city/home/farming, etc. ▸ Sustainability systems ▸ must balance trade-offs between the social, technological,

economic, and environmental pillars of sustainability

▸ involve complex decision-making with heterogeneous analysis

models, and large volumes of disparate data varying in temporal scale and modality

Modeling for Sustainability Benoit Combemale @ MRT 2018 19

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MDE for Sustainability Systems

▸ Scientific models are used to understand sustainability concerns and

evaluate alternatives (what-if/for scenarios)

▸ Engineering models are used to support the development and runtime

adaptation of sustainability systems.

How to integrate engineering and scientific models in a synergistic fashion to support informed decisions, broader engagement, and dynamic adaptation in sustainability systems?

Modeling for Sustainability

  • B. Combemale, B. Cheng, A. Moreira, J.-M. Bruel, J. Gray

In MiSE @ ICSE , 2016

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The Sustainability Evaluation ExperienceR (SEER)

▸Sm

Smar art Cy Cyber er-Physi sical Sy al Syst stem ems

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>>

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The Sustainability Evaluation ExperienceR (SEER)

▸ Based

ed o

  • n i

info formed med d dec ecisions

▸ with environmental, social and economic laws ▸ with open data

Heuristics

  • Laws

Scientists

Open Data Scientific Models / Physical Laws (economic, environmental, social) SEER

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>> <<supplement field data>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>>

Modeling for Sustainability Benoit Combemale @ MRT 2018 22

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The Sustainability Evaluation ExperienceR (SEER)

▸ Provi

viding a a b broader er en engagemen ement

▸ with "what-if" scenarios for general public and policy makers

Heuristics

  • Laws

Scientists

Open Data

General Public (e.g., individuals) Policy Makers (e.g., mayor)

MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>>

Communities (e.g., farmers)

<<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>>

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The Sustainability Evaluation ExperienceR (SEER)

▸ Supporting a

automa matic a adaptation

▸ for dynamically adaptable systems

Heuristics

  • Laws

Scientists

Open Data

General Public (e.g., individuals) Policy Makers (e.g., mayor)

MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>>

Communities (e.g., farmers)

<<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>>

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The Sustainability Evaluation ExperienceR (SEER)

▸ Application to health, farming system, smart grid…

Heuristics

  • Laws

Scientists

Open Data

General Public (e.g., individuals) Policy Makers (e.g., mayor)

MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>>

Communities (e.g., farmers)

<<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>>

Modeling for Sustainability Benoit Combemale @ MRT 2018 25

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The Sustainability Evaluation ExperienceR (SEER)

Heuristics

  • Laws

Scientists

Open Data

General Public (e.g., individuals) Policy Makers (e.g., mayor)

MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER

Sustainability System (e.g., smart farm) (

Context

sensors actuators

Production/ Consumption System (e.g. farm) Software

<<controls>> <<senses>>

Communities (e.g., farmers)

<<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>>

Farmers Agronomist Irrigation System

in collaboration with

MDE in Practice for Computational Science

Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal In International Conference on Computational Science (ICCS), 2015

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FARMING SYSTEM MODELING

  • 27

ht https:/ ://gi github. b.com com/ge gemoc

  • c/fa

farmingmodeling

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  • 28

WATER FLOOD PREDICTION

in collaboration with

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Take Away Messages

▸ From MDE to SLE

▸ Language workbenches support DS(M)L development

▸ On the globalization of modeling languages

▸ Integrate heterogeneous models representing different engineering concerns ▸ Language interfaces to support structural and behavioral relationships

between domains (i.e., DSLs)

▸ From software systems to smart CPS

▸ Interactions with the physical world limited to (i.e., fixed, in closed world)

control laws and data from the sensors

▸ What about the broader context in which the system involves?

▸ Physical / social / economic laws ▸ Predictive models ▸ Regulations, user preferences

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Conclusion

▸Integration of scientific models in the control loop of

smart CPS is key to provide more informed decisions, a broader engagement, and eventually relevant runtime reconfigurations

▸SEER is a particular instantiation of such a vision for

sustainability systems

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Open Challenges

▸ Diversity/complexity of DSL relationships

▸ Far beyond structural/behavioral alignment, refinement, decomposition ▸ Separation of concerns vs. Zoom-in/Zoom-out

▸ Live and collaborative (meta)modeling

▸ Minimize the round trip between the DSL specification, the model, and its

application (interpretation/compilation)

▸ Model experiencing environments (MEEs): what-if/for scenarios, trade-off

analysis, design-space exploration

▸ Integration of analysis and predictive models into DSL semantics

▸ Towards unpredictable languages

▸ Specify the correctness envelope to avoid over-specification ▸ Identify plastic computation zones ▸ Vary the execution flow of the program

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