Modeling For Sustainability: Or How to Make Smart CPS Smarter?

SEMINAR @ CWI (ALE MEETING), AMSTERDAM, NL.
MODELING FOR SUSTAINABILITY
Or How to Make Smart CPS Smarter?
BENOIT COMBEMALE
PROFESSOR, UNIV. TOULOUSE, FRANCE
HTTP://COMBEMALE.FR
BENOIT.COMBEMALE@IRIT.FR
@BCOMBEMALE

Complex Software-Intensive Systems
Modeling for Sustainability
Benoit Combemale @ CWI, Nov. 2017
Software
intensive
systems
▸ Multi-engineering approach
▸ Domain-specific modeling
▸ High variability and customization
▸ Software as integration layer
▸ Openness and dynamicity

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

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 : 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
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"

Model-Driven Engineering
"A clear challenge, then,
is how to integrate
multiple DSLs."
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.

Globalization of Modeling Languages
Supporting coordinated use of modeling
languages leads to what we call the globalization
of modeling languages, that is, the use of multiple
modeling languages to support coordinated
development of diverse aspects of a system.
Benoit Combemale, Julien DeAntoni, Benoit Baudry, Robert B. France, Jean-Marc Jezequel,
Jeff Gray, "Globalizing Modeling Languages," Computer, vol. 47, no. 6, pp. 68-71, June, 2014

Globalization of Modeling Languages
• DSMLs are developed in an independent manner
to meet the specific needs of domain experts,
• DSMLs should also have an associated framework
that regulates interactions needed to support
collaboration and work coordination across
different system domains.
- 8
Benoit Combemale, Julien DeAntoni, Benoit Baudry, Robert B. France, Jean-Marc Jezequel,
Jeff Gray, "Globalizing Modeling Languages," Computer, vol. 47, no. 6, pp. 68-71, June, 2014

Globalization of Modeling Language
- 9
• Context: new emerging DSML in open world
⇒impossible a priori unification
⇒require a posteriori globalization
• Objective: socio-technical coordination to support interactions across different system
aspects
⇒Language-based support for technical integration of multiples domains
⇒Language-based support for social translucence
• Community: the GEMOC initiative (cf. http://gemoc.org)
"Globalizing Domain-Specific Languages," Combemale, B., Cheng, B.H.C., France, R.B.,
Jézéquel, J.-M., Rumpe, B. (Eds.). Springer, Programming and Software Engineering,
Vol. 9400, 2015.

The GEMOC Initiative
-
10
An open and international initiative to
• coordinate (between members)
• disseminate (on behalf the members)
worldwide R&D efforts on the globalization of modeling languages
http://gemoc.org
@gemocinitiative

The GEMOC Studio
-
11
Design and integrate your
executable DSMLs
http://gemoc.org/studio
Language
Workbench
Modeling
Workbench
Edit, simulate and animate your
heterogeneous models

The GEMOC Community
-
12

From Software Systems
▸ software design models for functional
and non-functional properties
Engineers
System Models
Software

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>>

To Smart Cyber-Physical Systems
▸ analysis models (incl. large-scale simulation,
constraint solver) of the surrounding context
related to global phenomena (e.g. physical,
economical, and social laws)
▸ predictive models (predictive techniques from
AI, machine learning, SBSE, fuzzy logic)
▸ user models (incl., general public/community
preferences) and regulations (political laws)
Engineers
System Models Smart
Cyber-Physical System
Context
sensors actuators
Physical
System
Software

MDE for Smart CPS Development
▸ Convergence of engineering and scientific models
▸ A modeling framework to support the integration of 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

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

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?
B. Combemale, B. Cheng, A. Moreira, J.-M. Bruel, J. Gray
In MiSE @ ICSE , 2016

MDE for Sustainability Systems
1st Modelling For Sustainability Workshop @ Bellairs, 2016
Policy makers
(e.g., mayor)
Communities
(e.g., farmers)
General public
(e.g., individuals)
open data regulations
physical laws
explore the future
and
make it happen
The Sustainability Evaluation ExperienceR (SEER)

Sustainability System
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
▸ Smart Cyber-Physical Systems

Heuristics-Laws
Scientists
Open Data
Scientific Models / Physical Laws
(economic, environmental, social)
SEER
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
<<supplement
field data>>
<<feed>>
<<integrate>>
<<explore model
relations (tradeoff,
impact and conflict)>>
▸ Based on informed decisions
▸ with environmental, social and economic laws
▸ with open data

Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
("what-if" scenarios)
SEER
(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
▸ Providing a broader engagement
▸ 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
SEER
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
(e.g., farmers)
<<adapt>>
<<supplement
ﬁeld data>>
<<feed>>
<<integrate>>
<<explore model
▸ Supporting automatic adaptation
▸ for dynamically adaptable systems

Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
SEER
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
(e.g., farmers)
<<adapt>>
<<supplement
ﬁeld data>>
<<feed>>
<<integrate>>
<<explore model
▸ Application to health, farming system, smart grid…

Heuristics-Laws
Scientists
Open Data
General Public
(e.g., individuals)
Policy Makers
(e.g., mayor)
MEEs
SEER
(e.g., smart farm)
(
Context
sensors actuators
Production/
Consumption
System
(e.g. farm)
Software
(e.g., farmers)
<<adapt>>
<<supplement
ﬁeld data>>
<<feed>>
<<integrate>>
<<explore model
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

- 26
https://github.com/gemoc/farmingmodeling
FARMING SYSTEM MODELING
Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

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

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

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

"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

Abstract.
Various disciplines use models for different purposes. An engineering model, including a
software engineering model, is often developed to guide the construction of a non-existent
system. A scientific model is created to better understand an existing phenomenon (i.e., an
already existing system or a physical phenomenon). An engineering model may
incorporate scientific models to build a smart cyber-physical system (CPS) that require an
understanding of the surrounding environment to decide of the relevant adaptation to
apply. Sustainability systems, i.e., smart CPS managing resource production, transport and
consumption for the sake of sustainability (e.g., smart grid, city, farming system…), are
typical examples of smart CPS. Due to the inherent complex nature of sustainability that
must delicately balance trade-offs between social, environmental, and economic concerns,
modeling challenges abound for both the scientific and engineering disciplines.
In this talk, I will present a vision that promotes a unique approach combining engineering
and scientific models to enable informed decision on the basis of open and scientific
knowledge, a broader engagement of society for addressing sustainability concerns, and
incorporate those decisions in the control loop of smart CPS. I will introduce a research
roadmap to support this vision that emphasizes the socio-technical benefits of modeling.

Hack your own languages?
Join us in the MDE/SLE group of the CNRS IRIT lab, in
a freshly rebuilt campus of the warm city of Toulouse!
Open Positions
for PhD and Postdoc
BENOIT COMBEMALE
PROFESSOR, UNIV. TOULOUSE, FRANCE
HTTP://COMBEMALE.FR
BENOIT.COMBEMALE@IRIT.FR
@BCOMBEMALE

Modeling For Sustainability: Or How to Make Smart CPS Smarter?

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