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
Outline
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Framework for M&S
Discrete Event Processing
DEVS Formalism
Implications for Current Practice
Application Examples
M&S as a Bridge Discipline
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Framework for M&S:
Entities and Relations
Experimental Frame
Device for
executing model
Simulator
Real World
Data: Input/output
relation pairs
modeling
relation
Experimental frame specifies
conditions under which the system
is experimented with and observed
Each entity is formalized
as a Mathematical Dynamic
System
Each relation is represented
by a homomorphism or other
equivalence
simulation
relation
Model
Structure for generating behavior
claimed to represent real world
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Discrete Event Time Segments
X
x0
t0
x1
t1
t2
S
e
y0
Y
4
DEVS Background
• DEVS = Discrete Event System Specification
• Based on formal M&S 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
DEVS Hierarchical Modular
Composition
Atomic: lowest level model,
contains structural
dynamics -- model level
modularity
Coupled: composed of
Atomic
one or more atomic
and/or coupled
models
Hierarchical
construction
Ato mic
Atomic
Atomic
A to mic
Atomic
Atomic
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
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DEVS Expressability
Coupled Models
Atomic Models
Ordinary
Differential
Equation
Models
Processing/
Queuing/
Coordinating
Spiking
Neuron
Models
Petri Net
Models
Partial
Differential
Equations
Networks,
Collaborations
Processing
Networks
Physical
Space
Spiking
Neuron
Networks
n-Dim
Cell Space
Discrete
Time/
StateChart
Models
Stochasti
c
Models
Fuzzy
Logic
Models
can be
components
in a coupled
model
Cellular
Automata
Quantized
Integrator
Models
Reactive
Agent
Models
Multi
Agent
Systems
Self Organized
Criticality
Models
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Coupled model structure
Cell Space
Ignite
Wind
Potential neighbor cells to
ignite by fire from center cell.
NW
W
N
NE
S
SE
E
SW
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Atomic model structure
Forest Cell State Transitions
Fire
suppressant
unburned_wet
unburned
Fireline
Intensity
FI
Ignition and
(fireline intensity
> Threshold)
Burning
delay = 0
burned
burning
Fire suppressant
and fire fighting rule
satisfied
burning_wet
input
Fire
suppressant
delay = 0
Make a transition
burned_wet
elapsed
time
Phase “unburned”
If (FI > Threshold)
holdIn
else(“burning”,
passiva
teIn( “Unburned)”
Time advance
Phase “burning”
Compute new spread ( using
Rothermel’s eq)
Compute remaining distance to
reach center of neighbor cell
Compute time delays
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Experimentation
experimental frame
Cell Space
Display
Display
Average Rate
of Spread &
Direction
Transducer
Display
Active Cells
Vs.
Total Cells
Display
Other Stats
Forest Cell
Igniter
Wind
Flow
Model
Fire
Fighting
Model
Cell Space
Ignite
Wind
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wind across valley floor experiments
Wind
N
Wind
Wind
15°
NFFL-Fuel-Model 11:
Light logging slash
10°
NFFL-Fuel-Model
NFFL-Fuel-Model 7:
5: Brush (2 ft)
Southern rough
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water meets fire experiment
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M&S 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
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Separate Models From Simulators
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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 M&S-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)
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Separate Models From
Experimental Frames
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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 M&S-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.
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Use the DEVS Formalism for Developing
Models, Experimental Frames, and Simulators
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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 M&S-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.
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Maintain Repositories of
Reusable Models and Frames
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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 M&S-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.
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Managed Modeling in Lockheed’s
“System of Systems” M&S 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
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Component Reusability in Lockheed’s
DEVS M&S Environment
Project
Model
RAD
IR
Critical
Mobile
Target
Global
Positionin
g System
III
x
Arsen
al Ship
Coast
Guard
Deep
Water
Space
Operatio
ns
Vehicle
Commo
n Aero
Vehicle
Joint
Compos
ite
Tracking
Network
x
x
x
x
x
x
x
x
MIS
x
CC
x
Earth
x
WC
x
x
x
x
x
x
Space
Based
Laser
x
x
x
x
x
x
x
x
Space
Based
Discrimi
nation
Missile
Defense
(Theater /
National)
x
x
LAS
Comm
Integrat
ed
System
Center
x
x
x
x
x
x
x
x
x
x
x
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DEVS framework for knowledge based
control of steel production
Sachem = large-scale real-time monitor/diagnose control system for
blast furnace operation
Usinor -- world’s 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
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DEVS framework for knowledge based
control of steel production (cont’d)
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
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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
SimEnv
Terrain
Physics Dynamic
Control Agents SimMan
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
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Mapping Differential Equation Models into DEVS
Integrator Models
(n+1)D
X>0
D
ta(q) = ((n+1)D-q)/x
nD
x
s
x
f1
d s1/dt

ta(nD) = |D/x|
s1
X>0
D
X<0
X<0
nD
(n-1)D
s
x
f2
d s2/dt

s2
d sn/dt

sn
q
e
ta(q) = |q-nD/x|
...
s
x
fn
DEVS Integrator
x
DEVS
instantaneous
function
s
x
s
x

DEVS
S
d s 2/dt DEVS
 s2
f2
f
F
d s 1/dt
s1
d s n/dt
 sn
DEVS
1
F
...
s
x
F
fn
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Activity – a characteristic of continuous models
f (t )
quantum
t
 
q
d f (t )
dt
Activity = |f(t1) – f(t0)|
Number of crossings = Activity/quantum
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DEVS Efficiency Advantage where Activity is
Heterogeneous in Time and Space
time step
size
t
# time
steps
=T/
t
Potential Speed Up
=
#time steps /
# crossings
Time
Period
T
activity
A
# crossings
=A/q
X
diffusion
quantum
q
number
of
cells
activity
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Activity as unifying continuous and discrete paradigms
Heterogeneous
activity in time
and space
DEVS represents all
decision making and
continuous dynamic
components in the
scene
Quantization allows
DEVS to naturally
focus computing
resources on high
activity regions
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Modeling and Simulation
as a Bridging Discipline (3)
DEVS
Continuous Systems
• Analog
• Control theory
• Linear/Non Linear
• ODE/PDEs
• Representation
• Quantized Integration
• Discrete Pulse Wave Approx
Discrete Systems
• Digital
• Computer Science
• Algorithms
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Modeling and Simulation
as a Bridging Discipline (4)
DEVS
Computational Science
• Numerical Methods
• Supercomputing
• MPI
• PDEs
• Discrete Event Universality
• DEVS Simulation Protocol
• Representation of Cont Sys
PADS
• Logical Process
• Time Warp
• Large Numbers
• Network, Agent Apps
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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
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Society for Modeling and Simulation, Intl. :
www.scs.org
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Simulation Journal,
new: Journal of Defense Modeling and Simulation
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