Systems development using M&S based on the DEVS formalism

1. Systems development using M&S based on the DEVS formalism Gabriel A. Wainer Department of Systems and Computer Engineering. Carleton University. Ottawa, ON. Canada. gwainer@sce.carleton.ca

2. Simulation-based problem solving • Analysis of natural/artificial real systems. • Goal: learning through experimentation (developing, studying, training, analyzing, improving, enhancing, creating, defining). • Analytical solutions (natural systems). Artificial systems complexity: general solutions cannot be provided. • Simulation: particular solution to a given problem using certain experimental conditions.

3. Current practices • Evolution: based on technological advances (computing power, networks, graphical interfaces, standards). • Current practices: ad-hoc techniques, ignorance of previous recommendations for software engineering. • Tendency to encapsulate models, simulators and experiments into tightly coupled packages (written in programming languages such as Fortran, C, C++, Java). • Difficulties: testing, maintainability of the applications, integration, software reuse. • Relatively few examples of storing previously developed models to be adapted for interoperability and reuse

4. DEVS M&S methodology • Research in the last 15-20 years showed how DEVS can solve these issues: • Interoperability and reuse • Hybrid systems definition • Software engineering-based approach (different for different types kinds of life cycles) • Facilities for automated tasks • High performance/distributed simulation

5. Separation of concerns in DEVS modeling relation simulation relation Experimental Frame Device for executing model Real World Simulator Data: Input/output relation pairs Conditions under which the system is experimented with/observed Model Each entity can be formalized as a Mathematical Dynamic System (mathematical manipulations to prove system properties) Structure generating behavior claimed to represent real world Ref: Prof. B. Zeigler (ACIMS)

6. The DEVS Formalism • Discrete-Event formalism: time advances due to occurrence of events (improved performance when compared with time- based approaches). • Basic models that can be hierarchically coupled to build complex simulations.

7. Advantages of DEVS • Models, Simulators and EF: distinct entities with their own software representations. • Simulators can perform single host, distributed and real-time execution as needed (DEVS simulators over various middleware such as MPI, HLA, CORBA, etc.). • EF appropriate to a model distinctly identified; easier for potential users of a model to uncover objectives and assumptions that went into its creation. • Models/EF developed systematically for interoperability • Repositories of models and EF created and maintained (components for constructing new models). Models/EF stored in repositories with information to enable reuse.

8. Formalism transformation Ref: Prof. H. Vangheluwe (McGill)

9. Types of Models and their Formalisms Coupled Models Atomic Models Partial Differential Equations Ordinary Differential Equations Processing/ Queuing/ Coordinating Networks Collaborations Physical Space Phase Based Models Pulse Based Models (varGen, Sum) Digraph Models 1,2 Dim Cell Space Discrete Time/ Automata Quantum Based Models (DEVS Integrator, instantaneous Functions Cellular Automata can be components in a coupled model 2 Dim State Space 1 Dim State Space Self Organized Criticality Models Multi Agent Systems

10. DEVS Toolkits • ADEVS (University of Arizona) • CD++ (Carleton University) • DEVS-C++ (Kaist – Korea) • DEVS/HLA (ACIMS) • DEVSJAVA (ACIMS) • DEVSim++ (Kaist- Korea) • GALATEA (USB – Venezuela) • JDEVS (Université de Corse - France) • PyDEVS (McGill) • GDEVS (Aix-Marseille III, France) • SimBeams (University of Linz – Austria) • New efforts in China, France, Portugal, Spain.

11. DEVS Success Stories • Prototyping and testing environment for embedded system design (Schulz, S.; Rozenblit, J.W.; Buchenrieder, K.; Mrva, M.) • Urban traffic models (Lee, J.K.; Lee, J-J.; Chi, S.D.; et al.) • Watershed Modeling (Chiari, F. et al.) • Decision support tool for an intermodal container terminal (Gambardella, L.M.; Rizzoli, A.E.; Zaffalon, M.) • Forecast development of Caulerpa taxifolia, an invasive tropical alga (Hill, D.; Thibault, T.; Coquillard, P.) • Intrusion Detection Systems (Cho, T.H.; Kim, H.J.) • Depot Operations Modeling (B. Zeigler et al.) • Fire Spread (F. Barros, M. Vasconcelos) • Supply chain applications (Kim, D.; Cao H.; Buckley S.J.) • Solar electric system (Filippi, J-B.; Chiari, F.; Bisgambiglia, P.) • Joint Measure (Lockheed Martin): battlefield scenario specification, runtime visualization and data analysis. • Representation of hardware models developed with heterogeneous languages (Kim, J-K.; Kim, Y.G.; Kim, T.G.) • V-Lab: environment for robotic agents with physics, terrain and dynamics (M. Jamshidi et al.). • Sachem: large-scale monitor/diagnose control system for blast furnace operation (M. Le Goc, N. Giambiasi, et al.)

12. Advantages of DEVS • Reduced development times • Improved testing => higher quality models • Improved maintainability • Easy experimentation • Automated parallel/real-time execution • Verification/Validation • Interoperation and reuse • Multi-formalism modeling • High performance DEVS can be used as a base for systems development and execution

13. Where to go from now • Bridging the gap between research and practice • DEVS ready to take the leap • Critical mass of knowledgeable people • Large number of tools/researchers • Ready to go from Research to Development • Standardization of models (DEVS and non-DEVS) • Building libraries/user-friendly environments • Further research required; open areas.

14. Current developments

15. The DEVS Standardization Study group

16. New problems to solve • HLA focused in interoperatibility • Non DoD application of M&S • Popularity of other middleware applied in M&S applications (CORBA, PVM, MPI…) • Proposal: DEVS as supporting framework • July 2000: DEVS study group formed (80+ members)

17. Partial

18. Issues to investigate • Different approaches: compiling, translation, object orientation (standardization of the supporting classes) and combinations of these methods. • The relation to other applicable standards such HLA (hla.dmso.mil), CORBA (www.omg.org/corba), XML (www.w3.org/), Modelica (www.modelica.org). • Simulation interoperability of DEVS with non-DEVS simulation models. • Standardization of basic primitive and compound DEVS modeling constructs.

19. Summary of the discussion • Difficulties of modelling complex applications using HLA. Design, maintenance, integration. V&V. DEVS: complement HLA; other middleware. • Use the experience in previous experiences (HLA, Modelica). • Narrow the number of possibilities (DEVS flavors): provide a DEVS kernel. • Include terminology and ideas from industry. • Rely in the existing tools, and focus in interoperate them. • Building easy to use/install libraries • Defining a DEVS-based modeling language with focus in teaching.

20. Resources