Talk given at the CPS Day, organized by CPSE-Labs, SysML France and the GDR GPL in Toulouse, January 26th, 2016

1. Benoit Combemale (Inria & Univ. Rennes 1)
http://people.irisa.fr/Benoit.Combemale
benoit.combemale@irisa.fr
@bcombemale
Jean-Michel Bruel (Univ. Toulouse)
http://jmb.c.la
bruel@irit.fr
@jmbruel
in collaboration with INRA and OBEO,
and the support of the GEMOC initiative
Modeling for Smart Cyber-Physical Systems
Application to Sustainability Systems

2. 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

3. Aerodynamics
Authorities
Avionics
Safety
Regulations
Airlines
Propulsion
System
Mechanical
Structure
Environmental
Impact
Navigation
Communications
Human-
Machine
Interaction
3
Multiple
concerns,
stakeholders,
tools and methods

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

5. Model-Driven Engineering (MDE)
Distribution
« Service Provider
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« Service
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« Acceptance
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« Acceptance
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Manager
« Recovery
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Notification
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Manager
« Client »
User Citizen
Manager
Fault tolerance Roles
Activities
Views
Contexts
Security
Functional behavior
Book
state : StringUser
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
- 5Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

6. 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
« 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 : StringUser
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

7. 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

8. To Cyber-Physical Systems
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 8
Engineers
System Models Cyber-Physical
System
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

9. To Smart Cyber-Physical Systems
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 9
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>
• 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)

10. • 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
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 10
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software
<<controls>><<senses>>
An MDE-Based Approach for
Data Integration and Socio-
Technical Coordination in
Smart CPS Development

11. A MDE-based approach to develop Smart CPS
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 11
• Convergence of engineering and scientific models
• A modeling framework to support the integration of data from
sensors, 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

12. 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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

13. Using MDE in Smart-CPS Development
- 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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

14. Using MDE in Smart-CPS Development
- 14
• Providing a broader engagement
• with "what-if" scenarios for general public and policy makers
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

15. Using MDE in Smart-CPS Development
- 15
• Supporting automatic adaptation
• for dynamically adaptable systems
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

16. Using MDE in Smart-CPS Development
- 16
• Application to health, farming system, smart grid…
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

17. Farming System Modeling
- 17
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Farmers
Agronomist
Irrigation
System
in collaborationwith
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal, "MDE in
Practice for Computational Science," International Conference on
Computational Science (ICCS), 2015.
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016

18. Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
Farming System Modeling
- 18
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Farmers
Agronomist in collaborationwith
climate serieVegetal and
animal lifecycle
farm definition
activity description
hydric stress
Irrigation
System
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal, "MDE in
Practice for Computational Science," International Conference on
Computational Science (ICCS), 2015.

19. Farming System Modeling
- 19
Heuristics-Laws
Scientists
Open Data
Engineers
General Public
Policy Makers
MEEs
("what-if" scenarios)
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
<<controls>><<senses>>
Farmers
Agronomist in collaborationwith
climate serieVegetal and
animal lifecycle
farm definition
activity description
hydric stressbiomass growth,
water consomption
and activity schedule
water to be
irrigated
Irrigation
System
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal, "MDE in
Practice for Computational Science," International Conference on
Computational Science (ICCS), 2015.

20. - 20
https://github.com/gemoc/farmingmodeling
FarmingSystemModeling
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
• 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

21. Open Challenges
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 21
• 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

22. towards a
LiveModelingEnvironment
enabling a
broader engagement and informed decisions in
dynamicallyadaptableresource
managementsystems
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 22

23. - 23Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
"If you believe that language design can significantly
affect the quality of software systems, then it should
follow that language design can also affect the
quality of energy systems. And if the quality of such
energy systems will, in turn, affect the livability of
our planet, then it’s critical that the language
development community give modeling languages
the attention they deserve."
− Bret Victor (Nov., 2015), http://worrydream.com/ClimateChange