Approach and Techniques for Building Component-based Simulation Models Bernard P. Zeigler, Ph.D. Hessam S. Sarjoughian, Ph.D. Arizona Center for Integrative.

Slides:



Advertisements
Similar presentations
BioDEVS: System-Oriented, Multi-Agent, Disttributed/Parallel Framework for Modeling and Simulation of Biomimetic In Silico Devices Sunwoo Park 1 and C.
Advertisements

Eugene Syriani * † Hans Vangheluwe * ‡ Amr Al Mallah * † * ‡ Tuscaloosa, AL Montreal, Canada Antwerp, Belgium.
Review DEVS Formalism  Discrete-Event formalism: time advances using a continuous time base.  Basic models that can be coupled to build complex simulations.
Extended DEVSML as a Model Transformation Intermediary to Make UML Diagrams Executable Jianpeng Hu Dept. of Computer Science and Engineering Shanghai Jiao.
Parallel and Distributed Simulation Lookahead Deadlock Detection & Recovery.
Lookahead. Outline Null message algorithm: The Time Creep Problem Lookahead –What is it and why is it important? –Writing simulations to maximize lookahead.
Modeling & Simulation. System Models and Simulation Framework for Modeling and Simulation The framework defines the entities and their Relationships that.
© Yilmaz “Introduction to DEVS” 1 Introduction to Model Conceptualization and Design Dr. Levent Yilmaz M&SNet: Auburn M&S Laboratory Computer.
DEVS-Based Simulation Web Services for Net-Centric T&E Saurabh Mittal, Ph.D. Jose L. Risco-Martin*, Ph.D. Bernard P. Zeigler, Ph.D. Arizona Center for.
Replacing Hardware With Software Analysis and simulation of existing hardware using Discrete Event System Specification. By:Philip Felber Aman Gupta Imaduddin.
Simulation Waiting Line. 2 Introduction Definition (informal) A model is a simplified description of an entity (an object, a system of objects) such that.
DEVS and DEVS Model Dr. Feng Gu. Cellular automata with fitness.
Measuring Cooperative Robotic Systems Using Simulation-Based Virtual Environment Xiaolin Hu Computer Science Department Georgia State University, Atlanta.
Review. A_DA_A Ball_A Ball_B player_A B_DB_A Ball_B Ball_A player_B Ball_A Ball_B A_A, B_DA_D, B_A Ball_A Ball_B CFSM Player_A  : X  S  S X A = {Ball_A}
DEVS Today: Recent Advances in Discrete Event - Based Information Technology Bernard P. Zeigler Professor, ECE Arizona Center for Integrative Modeling.
By Manuel C. Salas Advisor: Dr. Bernard P. Zeigler University of Arizona 2008.
DEVSView: A DEVS Visualization Tool Wilson Venhola.
The Architecture of Secure Systems Jim Alves-Foss Laboratory for Applied Logic Department of Computer Science University of Idaho By, Nagaashwini Katta.
DEVS Namespace for Interoperable DEVS/SOA
Discrete Event Modeling and Simulation of Distributed Architectures using the DSSV Methodology E. de Gentili, F. Bernardi, J.F. Santucci University Pascal.
Develop DEVS Models Using DEVSJAVA Dr. Feng Gu. DEVS atomic model Elements of an atomic model input events output events state variables state transition.
DEVS-Centered Modeling and Simulation: Core Concepts for Engineering Education Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation.
DEVS Standard for Modeling and Simulation in Web- Centric Environments Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation University.
M&S Ontology Framework for Complex System Development and Testing Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation University.
Simulating Human Agropastoral Activities Using Hybrid Agent- Landscape Modeling M. Barton School of Human Evolution and Social Change College of Liberal.
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
By Ezequiel Glinsky Research Assistant, University of Buenos Aires, Argentina Supervisor: Prof. Gabriel A. Wainer SCE, Carleton University Thursday, November.
An Ontological Framework for Web Service Processes By Claus Pahl and Ronan Barrett.
Formalized Model Development & Test Generation: Key Role of Abstraction Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation (ACIMS)
Timed I/O Automata: A Mathematical Framework for Modeling and Analyzing Real-Time Systems Frits Vaandrager, University of Nijmegen joint work with Dilsun.
Why building models? n Cannot experience on the real system of interest n Cost n Danger n The real system does not exist Why using simulation? n Reduced.
ECE 449/549 Class Notes #1 Introduction to System Modeling Concepts and DEVS Sept
Definition of cell-shaped spaces. CCA = n C cell’s state variables; n S finite alphabet to represent each cell’s state; n n dimensional space; n N neighboring.
The Need for a Theory of Modeling and Simulation to Support the M&S COI Mission Bernard P. Zeigler, Ph.D., Arizona Center for Integrative Modeling and.
Unifying Discrete and Continuous Simulation with Discrete Events: DEVS as the Next Modeling Standard Bernard P. Zeigler Arizona Center for Integrative.
DEVS Based Modeling and Simulation of the CORBA POA F. Bernardi, E. de Gentili, Pr. J.F. Santucci {bernardi, gentili, University.
DEVS M&S Tutorial 2 Chungman Seo
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
Modeling with Parallel DEVS Serialization in DEVS models Select function Implicit serialization of parallel models E-DEVS: internal transition first,
Performance Analysis and Simulation of Service Based Applications Rajapaksage Jayampathi DM Rasanjalee Himali Instructor: Dr Xaolin Hu CSC 8350.
M&S Services at the Crossroads of Service Oriented Architecture and the DoD Architectural Framework Bernard P. Zeigler, Ph.D., Arizona Center for Integrative.
Simulator Protocol. coordinator simulator Component tN tN. tL After each transition tN = t + ta(), tL = t simulator Component tN tN. tL simulator Component.
DEVS and SES as a Framework for Modeling and Simulation Tool Development Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation University.
Computer Simulation of Networks ECE/CSC 777: Telecommunications Network Design Fall, 2013, Rudra Dutta.
Transforming DEVS to Non-Modular Form For Faster Cellular Space Simulation Arizona Center for Integrative Modeling and Simulation Electrical and Computer.
ECE 449/549 Class Notes #3 DEVS Simulators and Executors / Methodology : How to model and simulate Sept
DEVS-based Modeling and Simulation References: 1.B. P. Zeigler, Hessam S. Sarjoughian, Introduction to DEVS Modeling and Simulation with JAVA: Developing.
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
Review n System dynamics : A sequence of state transition n model : A set of rules for state transition System S X Y Discrete event system FSM (Automata)
 Simulation enables the study of complex system.  Simulation is a good approach when analytic study of a system is not possible or very complex.  Informational,
The Abstract simulator Simulator/Simulation Concepts n Simulator: responsible for executing a model’s dynamics (resented as instructions) in a given.
ECE 449/549 Class Notes #2 Introduction to Discrete-Event Systems Specification (DEVS) Sept
Agenda  Quick Review  Finish Introduction  Java Threads.
PDEVS Protocol Performance Prediction using Activity Patterns with Finite Probabilistic DEVS DEMO L. Capocchi, J.F. Santucci, B.P. Zeigler University of.
Extension du formalisme SES pour l’intégration de la hiérarchie d’abstraction et la granularité temporelle au sein de la modélisation et la simulation.
DEVS modeling of Traffic in AToM3 Presented by Ximeng Sun April 11, 2005.
Continuity and Change (Activity) Are Fundamentally Related In DEVS Simulation of Continuous Systems Bernard P. Zeigler Arizona Center for Integrative Modeling.
Simulation Engine (Simulator)‏. Outline Review of discrete time and discrete event simulation Standard simulator for DEVS atomic model Standard simulator.
Before we start Literature review abstract DEVSJAVA license form
Parallel DEVS & DEVSJAVA
Chapter16 Methodology: How to Model and Simulate
SyDEVS Introduction Theory – Paradigm – Implementation
DEVS Background DEVS = Discrete Event System Specification
Atomic Model Simulator
Approach and Techniques for Building Component-based Simulation Models
Chapter 4 DEVS Formalism
DEVS Background DEVS = Discrete Event System Specification
Presented By: Darlene Banta
Links to Websites.
Links to Websites.
Presentation transcript:

Approach and Techniques for Building Component-based Simulation Models Bernard P. Zeigler, Ph.D. Hessam S. Sarjoughian, Ph.D. Arizona Center for Integrative Modeling and Simulation (ACIMS) Arizona State University University of Arizona Google: ACIMS and Joint Interoperability Test Command (JITC) Fort Huachuca, AZ

Outline Basic principles of M&S from a system-theoretic worldview Component-based characterization of dynamic systems Unified framework for discrete and continuous models Discrete event system specification (DEVS) formalism Modular, hierarchical model composition Systems theory support of model reusability and composability Experimental Frame and model validation Simulation verification using the concept of abstract simulators DEVSJAVA, a modular, hierarchical simulation environment DEVS component-based M&S over network centric middleware

M&S Entities and Relations Real World SimulatorSimulator modeling relation simulation relation Each entity is represented as a dynamic system Each relation is represented by a homomorphism or other equivalence Data: Input/output relation pairs structure for generating behavior claimed to represent real world Device for executing model Model

M&S Entities and Relation(cont’d) Real World modeling relation simulation relation Experimental frame specifies conditions under which the system is experimented with and observed captures modeling objectives needed for validity, simplification justifications Simulator Model Experimental Frame

DESS: differential equation DEVS: discrete event DTSS: discrete time System Specification: Dynamic Systems Framework for Continuous and Discrete Models System Simulator: Event Processor DEVS model time to next event, state at next event... Simulator: Recursive Algorithm System DTSS model: q(t+1) = a*q(t)+ b*x(t) System Simulator: Numerical Integrator DESS model: dq/dt = a*q +bx

Dynamic Systems Framework for Continuous and Discrete Models (cont’d)

x 0 x 1 X S Y y0y0 e t0t0 t1t1 t2t2 Discrete Event Time Segments

DEVS = Discrete Event System Specification Provides formal M&S framework: specification,simulation Derived from Mathematical dynamical system theory Supports hierarchical, modular composition Object oriented implementation Supports discrete and continuous paradigms Exploits efficient parallel and distributed simulation techniques Theory of Modeling and Simulation, 2nd Edition, Academic Press, 2000, Bernard P. Zeigler, Herbert Praehofer, Tag Gon Kim DEVS Background

DEVS Hierarchical Modular Composition Atomic: lowest level model, contains structural dynamics -- model level modularity + coupling Coupled: composed of one or more atomic and/or coupled models Hierarchical construction

Atomic Models Ordinary Differential Equation Models Spiking Neuron Models Coupled Models Petri Net Models Cellular Automata n-Dim Cell Space Partial Differential Equations Self Organized Criticality Models Processing/ Queuing/ Coordinating Processing Networks Networks, Collaborations Physical Space DEVS Component-Based Expressability can be components in a coupled model Multi Agent Systems Discrete Time/ StateChart Models Quantized Integrator Models Spiking Neuron Networks Stochasti c Models Reactive Agent Models Fuzzy Logic Models

DEVS Theoretical Properties Closure Under Coupling Universality for Discrete Event Systems Representation of Continuous Systems –quantization integrator approximation –pulse representation of wave equations Simulator Correctness, Efficiency

DEVS Atomic Model Ports are represented explicitly – there can be any number of input and output ports on which values can be received and sent The time advance function determines the maximum lifetime in a state A bag can contain many elements with possibly multiple occurrences of its elements. Atomic DEVS models can handle bags of inputs and outputs. The external transition function handles inputs of bags by causing an immediate state change, which also may modify the time advance. The output function can generate a bag of outputs when the time advance has expired. The internal transition function is activated immediately after the output function causing an immediate state change, which also may modify the time advance. The confluent transition function decides the next state in cases of collision between external and internal events.

receptive refract Input fire Firing delay >0 Output Fire-once Neuron Atomic Model Examples Pulse Generator out pulse time passive active start interPulseTime >0 Output Pulse Generator start external event Internal eventoutput event ta = ∞

Internal Transition /Output Generation s Generate output output Make a transition s’s’ Time advance using the internal transition function using the output function

Time advance input Make a transition Response to External Input elapsed time using the external transition function

Time advance input Make a transition Response to Simultaneous External Input and Internal Event elapsed time Generate output output using the confluent transition function

DEVS Coupled Model Components Interconnections –Internal Couplings –External Input Couplings –External Output Couplings Elements of coupled model:

A B AB Coupling in Action Coupling (internal) Output port Input port State output external internal time advance State output external internal time advance

FireOnce Neuron 1 FireOnce Neuron 3 FireOnce Neuron 2 pulseInpulseOut pulseIn FireOnce Neuron 4 pulseOut pulseIn Pulse Generator Coupled Model Example – Neuron net FireOnce Neuron Network can compute the shortest path in a directed graph by mapping distances of edges to equivalent time values.

We separated models and simulators – now we bring them together Simulator Single processor Distributed Simulator Real-Time Simulator C++ Non DEVS Java Other Representation DEVS Simulation Protocol The DEVS simulation protocol is the agreement between the DEVS modeler and the implemented simulator

DEVS Simulation Protocol Classes Atomic Simulator coordinator coupledSimulator 1:n coupledCoordinator 1:n Stand alone atomic models are assigned to AtomicSimulators Coupled models are assigned to coordinators Atomic models as components within coupled models are assigned to coupledSimulators Coupled models as components within coupled models are assigned to coupledCoordinators genDevs. simulation. coordinator genDevs. simulation. coupledSimul ator

Atomic Model Simulator Every atomic model has a simulator assigned to it which keeps track of the time of the last event, tL and the time of the next event, tN. Initially, the state of the model is initialized as specified by the modeler to a desired initial state, s init. The event times, tL and tN are set to 0 and ta(s init ), respectively. If there are no external events, the clock time, t is advanced to tN, the output is generated and the internal transition function of the model is executed. The simulator then updates the event times as shown, and processing continues to the next cycle. If an external event is injected to the model at some time, t ext (no earlier than the current clock and no later than tN), the clock is advanced to t ext. If t ext == tN the output is generated. Then the input is processed by the confluent or external event transition function, depending on whether t ext coincides with tN or not. tL =: t tN =: t + ta(s) When receive m if m!= null and t < tN, s :=  ext (s,t-tN,m) if m!= null and t == tN, s :=  con (s,t-tN,m) if m= null and t == tN, s =:  int (s) s =: s init tL =: 0 tN =: ta(s init ) m timeline (abstract or logica) inject at time t tN tL Legend: m = message s = state t = clock time tL = time of last event tN = time of next event If t == tN generate output (s)

coordinator simulator Component tN tN. tL After each transition tN = t + ta(), tL = t simulator Component tN tN. tL simulator Component tN tN. tL Coupled Model 1 nextTN 2. outTN 3 getOut 4 sendOut 5 applyDelt Basic DEVS Simulation Protocol Each simulator reacts to the incoming message as follows: If it is imminent and its input message is empty, then it invokes its model’s internal transition function If it is imminent and its input message is not empty, it invokes its model’s confluence transition function If is not imminent and its input message is not empty, it invokes its model’s external transition function If is not imminent and its input message is empty then nothing happens. Coupled Model Coordinator: 1. Coordinator sends nextTN to request tN from each of the simulators. 2.All the simulators reply with their tNs in the outTN message to the coordinator 3.Coordinator sends to each simulator a getOut message containing the global tN (the minimum of the tNs) 4. Each simulator checks if it is imminent (its tN = global tN) and if so, returns the output of its model in a message to the coordinator in a sendOut message. 5. Coordinator uses the coupling specification to distribute the outputs as accumulated messages back to the simulators in an applyDelt message to the simulators – for those simulators not receiving any input, the messages sent are empty. For a coupled model with atomic model components, a coordinator is assigned to it and coupledSimulators are assigned to its components. In the basic DEVS Simulation Protocol, the coordinator is responsible for stepping simulators through the cycle of activities shown.

DEVS as the basis for Network Centric M&S Network Centric Middleware – NCES message DEVS Model Translator DEVS Model Translator message DEVS Model Translator Diff Eqn. Systems Diff Eqn. Systems Discrete Time Systems Discrete Event Formalisms Simulator