DEVS%20Today:%20%20Recent%20Advances%20in%20Discrete%20Event%20-%20%20%20%20%20%20%20%20Based%20Information%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Technology - PowerPoint PPT Presentation

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DEVS%20Today:%20%20Recent%20Advances%20in%20Discrete%20Event%20-%20%20%20%20%20%20%20%20Based%20Information%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Technology

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DEVS Today: Recent Advances in Discrete Event - Based Information Technology Bernard P. Zeigler Professor, ECE Arizona Center for Integrative Modeling and Simulation – PowerPoint PPT presentation

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Title: DEVS%20Today:%20%20Recent%20Advances%20in%20Discrete%20Event%20-%20%20%20%20%20%20%20%20Based%20Information%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Technology


1
DEVS Today Recent Advances in Discrete Event
- Based Information
Technology
  • Bernard P. Zeigler
  • Professor, ECE
  • Arizona Center for Integrative Modeling and
    Simulation
  • University of Arizona
  • Tucson
    www.acims.arizona.edu

Keynote Talk to Majestic
2
Outline
  • Framework for MS
  • Discrete Event Processing
  • DEVS Formalism
  • Implications for Current Practice
  • Application Examples
  • MS as a Bridge Discipline

3
Framework for MS Entities and Relations
Experimental Frame
Device for executing model
Real World
Simulator
Data Input/output relation pairs
Experimental frame specifies conditions under
which the system is experimented with and
observed
Model
Each entity is formalized as a Mathematical
Dynamic System Each relation is represented by
a homomorphism or other equivalence
Structure for generating behavior claimed to
represent real world
4
Discrete Event Time Segments
X
t1
t0
t2
S
e
y0
Y
5
DEVS Background
  • DEVS Discrete Event System Specification
  • Based on formal MS framework
  • 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

6
DEVS Hierarchical Modular Composition
  • Atomic lowest level model, contains structural
    dynamics -- model level modularity

Coupled composed of one or more atomic and/or
coupled models
coupling
Hierarchical construction
7
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

8
DEVS Expressability
Coupled Models
Atomic Models
Partial Differential Equations
can be components in a coupled model
Ordinary Differential Equation Models
Processing/ Queuing/ Coordinating
Networks, Collaborations
Physical Space
Spiking Neuron Networks
Spiking Neuron Models
Processing Networks
Petri Net Models
n-Dim Cell Space
Discrete Time/ StateChart Models
Stochastic Models
Cellular Automata
Quantized Integrator Models
Self Organized Criticality Models
Fuzzy Logic Models
Reactive Agent Models
Multi Agent Systems
9
Coupled model structure
Cell Space
Ignite
Wind
Water
Potential neighbor cells to ignite by fire from
center cell.
10
Atomic model structure
Forest Cell State Transitions
Fireline Intensity FI





input

input

input


Make a transition

Make a transition

Make a transition

elapsed

elapsed

time

time

Time advance

Time advance

Phase unburned

If (FI gt Threshold)

Phase burning
holdIn (burning,

else


Compute new spread ( using

passiva
teIn( Unburned)

Rothermels eq)

Compute remaining distance to

reach center of neighbor cell

Compute time delays

11
Experimentation
experimental frame
Cell Space
Ignite
Wind
Water
12
wind across valley floor experiments

13

water meets fire experiment



14
MS Framework Implications for Current Practice
  • Separate Models From Simulators
  • Separate Models From Experimental Frames
  • Use the DEVS Formalism for Developing Models,
    Experimental Frames, and Simulators
  • Experimental Frames Support Defense Certification
    Testing
  • Maintain Repositories of Reusable Models and
    Frames

15
Separate Models From Simulators
  • Models are goal oriented abstractions of reality.
  • Simulators are the computational engines that
    drive the models to obtain results.

Currently Simulation software tends to
encapsulate models and simulators in tightly
coupled packages.
  • In the MS-Framework-based approach..
  • Models and Simulators are treated as distinct
    entities with their own software representations.
  • There are simulators for different kinds of
    models that can be selected according to the
    needs of the simulation,
  • For example, a simulator might be chosen for its
    efficiency on a single host, or for its ability
    to execute the model on multiple hosts
    (distributed simulation)

16
Separate Models From Experimental Frames
  • Experimental Frames are specifications of the
    experimentation to be done on a model
  • Frames represent the objectives of the
    experimenter, tester, or analyst

Currently Simulation software tends to
encapsulate models, simulators and experimental
frames into tightly coupled packages.
  • In the MS-Framework-based approach..
  • Models and Experimental Frames are treated as
    distinct entities with their own software
    representations.
  • Since the experimental frames appropriate to a
    model are distinctly identified, it is easier for
    potential users of a model to uncover the
    objectives and assumptions that went into its
    creation.

17
Use the DEVS Formalism for Developing Models,
Experimental Frames, and Simulators
  • The DEVS formalism enables users to develop
    models separately from experimental frames .
  • Models and frames can then be coupled together
    and given to an appropriate simulator to execute.

Currently Programming languages such as Fortran,
C, C or Java are used to develop software
packages of strongly coupled models, frames and
simulators.
  • In the MS-Framework-based approach..
  • The DEVS formalism Is employed for all simulation
    software development.
  • DEVS simulators are employed to perform single
    host, distributed and heterogeneous real-time
    execution as needed.
  • DEVS simulators exist that run over various
    middleware such as MPI,HLA, CORBA,P2P, and MOM.

18
Maintain Repositories of Reusable Models and
Frames
  • Models and Experimental Frames can be stored in
    organized repositories to support reuse under
    well specified conditions

Currently There are relatively few examples of
storing previously developed simulation
infrastructure commodities in such a way that
they can be easily adapted to developing
interoperability test requirements
  • In the MS-Framework-based approach..
  • Repositories of models and frames are created and
    maintained.
  • Such repositories foster reuse of existing models
    and frames to serve as components for
    constructing new ones.
  • When new models or frames are developed they are
    deposited in the repositories with appropriate
    information to enable their reuse with high
    confidence of success.

19
Managed Modeling in Lockheeds System of
Systems MS Environment
  • DEVS (Discrete Event Modeling Formalism)
  • Separates Model and Simulators
  • Defines Couple Models and Atomic Models
  • Modularized via Ports and Defined Events
  • SES (System Entity Structure)
  • Provides a well defined structure for model reuse
  • Maintains kind-of, part-of, multiplicity
    relationships
  • Supports constraints on model compatibility
  • Architecture based on SES/DEVS supports component
    model reuse evolved during last decade

20
Component Reusability in Lockheeds DEVS MS
Environment
Project Model Critical Mobile Target Global Positioning System III Arsenal Ship Coast Guard Deep Water Space Operations Vehicle Common Aero Vehicle Joint Composite Tracking Network Integrated System Center Space Based Laser Space Based Discrimination Missile Defense (Theater / National)
RAD x x x x x x x
IR x x x x x x x
MIS x x x x x
LAS x x x x
Comm x x x x x x
CC x x x
Earth x x x x x
WC x x
21
DEVS framework for knowledge based control of
steel production
  • Sachem large-scale real-time monitor/diagnose
    control system for blast furnace operation
  • Usinor -- worlds largest producer of steel
    products, Sachem saves it millions of euros
    annually
  • Problems for conventional control and AI
  • Experts perception knowledge is implicit,
    concerns dynamic physical processes
  • Difficult to model the reasoning of a control
    process expert.
  • Lack of mathematical models for blast furnace
    dynamics
  • Solution
  • time-based perception and discrete event
    processing for dealing with complex dynamical
    systems

22
DEVS framework for knowledge based control of
steel production (contd)
quanti zation
signal events
signal pheno mena
process pheno mena
  • Large Scale
  • Conceptual model contains 25,000 objects for 33
    goals, 27 tasks,etc.
  • Approximately 400,000 lines of code.
  • 14 man-years 6 knowledge engineers and 12
    experts
  • One advantage of DEVS is compactness 50,000
    reduction in data volume

Effective analysis and control of the behavior
of blast furnaces at high resolution
23
University of New Mexico Virtual Lab for
Autonomous Agents
V-Lab developed on top of DEVSJAVA includes a
simulation environment for robotic agents with
physics, terrain and dynamics. It extends DEVS
to provide a layer for specifying intelligent
automation and soft computing algorithms (IDEVS).
IDEVS
SimEnv
DEVS Simulator
Middleware (HLA,CORBA,JMS)
Computer Network
V-Lab-a virtual laboratory for autonomous
agents-SLA-based learning controllers El-Osery,
A.I. Burge, J. Jamshidi, M. Saba, A. Fathi,
M. Akbarzadeh-T, M.-R. Systems, Man and
Cybernetics, Part B, IEEE Transactions on ,
Volume 32 Issue 6 , Dec. 2002 Page(s) 791
-803
24
Mapping Differential Equation Models into DEVS
Integrator Models
DEVS Integrator
DEVS instantaneous function
25
Activity a characteristic of continuous models
Activity f(t1) f(t0)
Number of crossings Activity/quantum
26
DEVS Efficiency Advantage where Activity is
Heterogeneous in Time and Space
diffusion
activity
27
Activity as unifying continuous and discrete
paradigms
DEVS represents all decision making and
continuous dynamic components in the scene
Heterogeneous activity in time and space
Quantization allows DEVS to naturally focus
computing resources on high activity regions
28
Modeling and Simulation as a Bridging Discipline
(3)
  • Discrete Systems
  • Digital
  • Computer Science
  • Algorithms
  • Continuous Systems
  • Analog
  • Control theory
  • Linear/Non Linear
  • ODE/PDEs

29
Modeling and Simulation as a Bridging Discipline
(4)
  • PADS
  • Logical Process
  • Time Warp
  • Large Numbers
  • Network, Agent Apps
  • Computational Science
  • Numerical Methods
  • Supercomputing
  • MPI
  • PDEs

30
More Information
  • Zeigler, B.P., Praehofer, H., and Kim, T.G.,
    Theory of Modeling and Simulation, 2nd Edition.
    Academic Press, 2000.
  • ACIMS www.acims.arizona.edu DEVSJAVA
    downloadable software
  • Society for Modeling and Simulation, Intl.
    www.scs.org
  • Simulation Journal,
  • new Journal of Defense Modeling and Simulation
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