Modeling for Smart Cyber-Physical Systems (Jan 26th, 2016)

Benoit Combemale (Inria & Univ. Rennes 1)http://people.irisa.fr/[email protected]@bcombemale

Jean-Michel Bruel (Univ. Toulouse)http://[email protected]@jmbruel

in collaboration with INRA and OBEO, and the support of the GEMOC initiative

Modeling for Smart Cyber-Physical SystemsApplication to Sustainability Systems

Complex Software-Intensive Systems

Software intensive systems

- 2Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

• Multi-engineering approach• Some forms of domain-specific modeling • Software as integration layer• Openness and dynamicity

Aerodynamics

Authorities

Avionics

SafetyRegulations

Airlines

PropulsionSystem

MechanicalStructure

EnvironmentalImpact

NavigationCommunications

Human-Machine

Interaction

3

Multipleconcerns,

stakeholders, tools and methods

4

Aerodynamics

Authorities

Avionics

SafetyRegulations

Airlines

PropulsionSystem

MechanicalStructure

EnvironmentalImpact

NavigationCommunications

Human-Machine

Interaction

Heterogeneous Modeling

Model-Driven Engineering (MDE)

Distribution

« Service Provider Manager »

Notification Alternate Manager

« Recovery Block Manager »

ComplaintRecovery Block

Manager

« Service Provider

Manager »

Notification Manager


Complaint Alternate Manager

« Service Provider

Manager »

Complaint Manager

« Acceptance Test Manager »

Notification Acceptance Test

Manager


Complaint Acceptance Test

Manager


NotificationRecovery Block

Manager

« Client »

User Citizen Manager

Fault tolerance Roles

ActivitiesViews

Contexts

Security

Functional behavior

Bookstate : StringUser

borrow

return

deliver

setDamagedreserve

Use case

PlatformModel Design

ModelCode

Model

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


Model-Driven Engineering (MDE)

- 6

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.

Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

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

Distribution


Notification Alternate Manager


ComplaintRecovery Block

Manager

« Service Provider

Manager »

Notification Manager


Complaint Alternate Manager

« Service Provider

Manager »

Complaint Manager


Notification Acceptance Test

Manager


Complaint Acceptance Test

Manager


NotificationRecovery Block

Manager

« Client »

User Citizen Manager

Fault tolerance Roles

ActivitiesViews

Contexts

Security

Functional behavior

Bookstate : StringUser

borrow

return

deliver

setDamagedreserve

Use case

PlatformModel Design

ModelCode

Model

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

From Software Systems

Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 7

Engineers

System Models

Software

• software design models for functional and non-functional properties

To Cyber-Physical Systems


Engineers

System Models Cyber-PhysicalSystem

sensors actuators

Physical System

Software

<<controls>><<senses>>

• multi-engineering design models for global system properties

• runtime models (i.e., included into the control loop) for dynamic adaptations

To Smart Cyber-Physical Systems


Engineers

System Models SmartCyber-Physical System

Context

sensors actuators

Physical System

Software


• analysis models (incl. large-scale simulation, constraint solver) of the surrounding context related to global phenomena (e.g. physical laws)

• probabilistic models (predictive techniques from AI, machine learning, SBSE)

• user models (incl., general public/community preferences) and regulations (incl., economic/social/political laws)

• analysis models (incl. large-scale simulation, constraint solver) of the surrounding context related to global phenomena (e.g. physical laws)

• probabilistic models (predictive techniques from AI, machine learning, SBSE)

• user models (incl., general public/community preferences) and regulations (incl., economic/social/political laws)

To Smart Cyber-Physical Systems


Engineers

System Models SmartCyber-Physical System

Context

sensors actuators

Physical System

Software


An MDE-Based Approach for Data Integration and Socio-Technical Coordination in Smart CPS Development

A MDE-based approach to develop Smart CPS


• Convergence of engineering and scientific models

• A modeling framework to support the integration of data fromsensors, open data, laws/regulation, scientific models, engineeering models and preferences.

• Domain-specific languages for socio-technical coordination• to engage engineers, scientist, decision makers, communities

and general public• to integrate analysis/probabilistic/user models into the control

loop of smart CPS

Using MDE in Smart-CPS Development

- 12

• Cyber-Physical Systems

Engineers

System Models Sustainability System(e.g., smart grid)

Context

sensors actuators

Energy Production/

Consumption System

Software




- 13

• Based on informed decisions• with environmental, social and economic laws• with open data

Heuristics-Laws

Scientists

Open Data

Engineers

System Models

Physical Laws (economic, environmental, social)

Simulation Tool(incl. constraint solver,

prediction tool, etc.)

Sustainability System(e.g., smart grid)

Context

sensors actuators

Energy Production/

Consumption System

Software




- 14

• Providing a broader engagement• with "what-if" scenarios for general public and policy makers

Heuristics-Laws

Scientists

Open Data

Engineers

General PublicPolicy Makers

MEEs("what-if" scenarios)

System Models





Context

sensors actuators

Energy Production/

Consumption System

Software




- 15

• Supporting automatic adaptation• for dynamically adaptable systems

Heuristics-Laws

Scientists

Open Data

Engineers



System Models





Context

sensors actuators

Energy Production/

Consumption System

Software




- 16

• Application to health, farming system, smart grid…

Heuristics-Laws

Scientists

Open Data

Engineers



System Models





Context

sensors actuators

Energy Production/

Consumption System

Software



Farming System Modeling

- 17

Heuristics-Laws

Scientists

Open Data

Engineers



System Models





Context

sensors actuators

Energy Production/

Consumption System

Software


Farmers

Agronomist

IrrigationSystem

in collaboration with

Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal, "MDE in Practice for Computational Science," International Conference on Computational Science (ICCS), 2015.




- 18

Heuristics-Laws

Scientists

Open Data

Engineers



System Models





Context

sensors actuators

Energy Production/

Consumption System

Software


Farmers

Agronomistin collaboration with

climate serieVegetal and animal lifecycle

farm definition

activity description

hydric stress

IrrigationSystem



- 19

Heuristics-Laws

Scientists

Open Data

Engineers



System Models





Context

sensors actuators

Energy Production/

Consumption System

Software


Farmers

Agronomistin collaboration with

climate serieVegetal and animal lifecycle

farm definition

activity description

hydric stressbiomass growth, water consomption and activity schedule

water to beirrigated

IrrigationSystem



- 20

https://github.com/gemoc/farmingmodeling



• Heterogeneous modeling and simulation• Graphical animation and debugging (incl.

breakpoints, timeline, step forward/backward, stimuli management, etc.)

• Multi-dimensional and efficient trace management

• Concurrency simulation and formal analysis

Open Challenges


• Diversity/complexity of DSL relationships• far beyond structural and behavioral alignment, refinement,

decomposition• Separation of concerns vs. Zoom-in/Zoom-out

• Live and collaborative modeling• minimize the round trip between the DSL specification, the

model, and its application (interpretation/compilation)• Model experiencing environnements

• Integration of analysis and probabilistic models into DSL semantics

towards a

Live Modeling Environmentenabling a

broader engagement and informed decisions in

dynamically adaptable resource management systems



"If you believe that language design can significantlyaffect the quality of software systems, then it shouldfollow that language design can also affect the quality of energy systems. And if the quality of suchenergy systems will, in turn, affect the livability of our planet, then it’s critical that the languagedevelopment community give modeling languagesthe attention they deserve." − Bret Victor (Nov., 2015), http://worrydream.com/ClimateChange