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 & INRIA, FRANCE PROFESSOR, UNIV. TOULOUSE, FRANCE HTTP://COMBEMALE.FR HTTP://COMBEMALE.FR BENOIT.COMBEMALE@IRIT.FR BENOIT.COMBEMALE@IRIT.FR @BCOMBEMALE @BCOMBEMALE
Complex Software-Intensive Systems ▸ Multi-engineering approach ▸ Domain-specific modeling Software ▸ High variability and customization intensive systems ▸ Software as integration layer ▸ Openness and dynamicity Modeling for Sustainability 2 Benoit Combemale @ MRT 2018
Mechanical Airlines Structure Human- Machine Avionics Interaction Environmental Aerodynamics Impact Multiple concerns, stakeholders, tools and methods Propulsion Safety System Regulations Authorities Communications Navigation 3
Mechanical Airlines Structure Human- Machine Avionics Interaction Environmental Aerodynamics Impact Heterogeneous Modeling Propulsion Safety System Regulations Authorities Communications Navigation 4
Model-Driven Engineering Security Change one Aspect and Distribution Automatically Re-Weave: Fault tolerance Contexts From Software Product Lines… Roles « Acceptance Views « Acceptance « Client » Activities ..to Dynamically Adaptive Systems Test Manager » Test Manager » User Citizen Manager Notification Functional behavior Complaint Acceptance Test Acceptance Test Manager Manager Use case « Recovery Block Manager » « Recovery Block Manager » Complaint Notification Recovery Block Recovery Block Manager Manager borrow « Service Provider « Service Manager » « Service « Service Provider Provider Provider Manager » Manager » Complaint Alternate Manager » Notification return Manager Alternate Manager Complaint Notification Platform Manager Manager deliver Design Model setDamaged res Book erv User e state : String Model Code Model "Perhaps surprisingly, the majority of MDE examples in our study followed domain-specific modeling paradigms" 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 Sustainability 5 Benoit Combemale @ MRT 2018
From Software Systems System Models Software Engineers ▸ software design models for functional and non-functional properties Modeling for Sustainability 13 Benoit Combemale @ MRT 2018
To Cyber-Physical Systems System Models Cyber-Physical System Software Engineers <<senses>> <<controls>> sensors actuators ▸ multi-engineering design models for global system properties Physical ▸ models @ runtime (i.e., included System into the control loop) for dynamic adaptations Modeling for Sustainability 14 Benoit Combemale @ MRT 2018
To Smart Cyber-Physical Systems System Models Smart Cyber-Physical System Software Engineers <<senses>> <<controls>> sensors actuators ▸ an models (incl. large-scale simulation, anal alysis mo constraint solver) of the surrounding context related to global phenomena (e.g. physical, economical, and social laws) Physical System ▸ pr models (predictive techniques from predic ictive ive mo AI, machine learning, SBSE, fuzzy logic) Context ▸ us models (incl., general public/community user mo regulations (political laws) preferences) and re Modeling for Sustainability 15 Benoit Combemale @ MRT 2018
What about Scientific Modeling? ▸ Models (computational and data-intensive sciences) for analyzing and understanding physical phenomena Physical Laws (economic, environmental, social) Heuristics -Laws Context Scientists Modeling for Sustainability 16 Benoit Combemale @ MRT 2018
What about Scientific Modeling? ▸ S im imul ulator tors for tradeoff analysis, Simulator what-if scenarios, analysis of alternatives and adaptations to Simulation environmental changes, etc. Processes <<represents>> Physical Laws (economic, environmental, social) Heuristics -Laws Context Scientists Modeling for Sustainability 17 Benoit Combemale @ MRT 2018
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 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 Modeling for Sustainability 18 Benoit Combemale @ MRT 2018
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 19 Benoit Combemale @ MRT 2018
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 Modeling for Sustainability 20 Benoit Combemale @ MRT 2018
The Sustainability Evaluation ExperienceR (SEER) ▸ Sm Smar art Cy Cyber er-Physi sical Sy al Syst stem ems Sustainability System (e.g., smart farm) Software <<controls>> <<senses>> sensors actuators Production/ Consumption ( System (e.g. farm) Context Modeling for Sustainability 21 Benoit Combemale @ MRT 2018
The Sustainability Evaluation ExperienceR (SEER) ▸ Based ed o on i info formed med d dec ecisions ▸ with environmental, social and economic laws ▸ with open data Sustainability System (e.g., smart farm) Software SEER <<supplement field data>> <<controls>> <<senses>> <<explore model sensors actuators relations (tradeoff, <<feed>> impact and conflict)>> <<integrate>> Open Data Scientific Models / Physical Laws Production/ (economic, environmental, social) Consumption ( System Heuristics -Laws (e.g. farm) Context Scientists Modeling for Sustainability 22 Benoit Combemale @ MRT 2018
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 MEEs ("what-if" scenarios) Sustainability System <<provide configuration, (e.g., smart farm) preferences, questions>> <<present possible future Software General Public and variable indicators>> Policy Makers (e.g., individuals) SEER (e.g., mayor) <<supplement Communities field data>> <<controls>> <<senses>> (e.g., farmers) <<explore model sensors actuators relations (tradeoff, <<feed>> impact and conflict)>> <<integrate>> Open Data Scientific Models / Physical Laws Production/ (economic, environmental, social) Consumption ( System Heuristics -Laws (e.g. farm) Context Scientists Modeling for Sustainability 23 Benoit Combemale @ MRT 2018
The Sustainability Evaluation ExperienceR (SEER) ▸ Supporting a automa matic a adaptation ▸ for dynamically adaptable systems MEEs ("what-if" scenarios) Sustainability System <<provide configuration, (e.g., smart farm) preferences, questions>> <<adapt>> <<present possible future Software General Public and variable indicators>> Policy Makers (e.g., individuals) SEER (e.g., mayor) <<supplement Communities field data>> <<controls>> <<senses>> (e.g., farmers) <<explore model sensors actuators relations (tradeoff, <<feed>> impact and conflict)>> <<integrate>> Open Data Scientific Models / Physical Laws Production/ (economic, environmental, social) Consumption ( System Heuristics -Laws (e.g. farm) Context Scientists Modeling for Sustainability 24 Benoit Combemale @ MRT 2018
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