Post on 22-Jan-2018
Benoit Combemale (Inria & Univ. Rennes 1)http://people.irisa.fr/Benoit.Combemalebenoit.combemale@irisa.fr@bcombemale
Jean-Michel Bruel (Univ. Toulouse)http://jmb.c.labruel@irit.fr@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
« 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 »
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
- 5Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
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 »
ComplaintRecovery 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 »
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
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 8
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
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 9
Engineers
System Models SmartCyber-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)
• 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 SmartCyber-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
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 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
<<controls>><<senses>>
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
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
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 PublicPolicy 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
Using MDE in Smart-CPS Development
- 15
• Supporting automatic adaptation• for dynamically adaptable systems
Heuristics-Laws
Scientists
Open Data
Engineers
General PublicPolicy 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
Using MDE in Smart-CPS Development
- 16
• Application to health, farming system, smart grid…
Heuristics-Laws
Scientists
Open Data
Engineers
General PublicPolicy 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
Farming System Modeling
- 17
Heuristics-Laws
Scientists
Open Data
Engineers
General PublicPolicy 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
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.
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
Farming System Modeling
- 18
Heuristics-Laws
Scientists
Open Data
Engineers
General PublicPolicy 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
Agronomistin collaboration with
climate serieVegetal and animal lifecycle
farm definition
activity description
hydric stress
IrrigationSystem
Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal, "MDE in Practice for Computational Science," International Conference on Computational Science (ICCS), 2015.
Farming System Modeling
- 19
Heuristics-Laws
Scientists
Open Data
Engineers
General PublicPolicy 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
Agronomistin collaboration with
climate serieVegetal and animal lifecycle
farm definition
activity description
hydric stressbiomass growth, water consomption and activity schedule
water to beirrigated
IrrigationSystem
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
https://github.com/gemoc/farmingmodeling
Farming System Modeling
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
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
towards a
Live Modeling Environmentenabling a
broader engagement and informed decisions in
dynamically adaptable resource management systems
Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016 - 22
- 23Modeling for Smart CPS – B. Combemale (INRIA & Univ. Rennes 1) – Jan. 2016
"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