2. 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

3. 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.

4. 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

5. 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

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

7. 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.

8. 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.

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

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

11. 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.

12. 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.)

13. 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

14. 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.

15. Current developments

16. The DEVS Standardization Study group

17. 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)

18. Partial

19. 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.

20. 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.

21. Resources