discrete-event modeling and simulation: past, present … - gabriel... · discrete-event modeling...
TRANSCRIPT
Discrete-Event Modeling and
Simulation: Past, Present and Future
Gabriel Wainer
Department of Systems and Computer Engineering
Carleton University
Ottawa, ON, CANADA
http://cell-devs.sce.carleton.ca
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: DEVS simulators
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: DEVS simulators
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
Analytical Modeling
EquationsResults
ExperimentExperimental
FrameEntity
Results
QueryModel's Exp.
FrameModel
Analytical:
– Based on reasoning
– Symbolic
– General solutions to existing systems
– Impossible to define
– Impossible to solve
– Simplifications
x
Tkqx
Numerical Approximation
Approximation
Results
Experiment
Experimental
FrameEntity
Query
Model's Exp.
FrameModel
Approximate
Results
Computed
Query
Computation
Exp. FrameCompute
1
1
1
1
1
L
Kh
TL
KhT
T
o
x
Tkqx
Artificial Systems Modeling
Examples– Communication networks
– Distributed control
– Manufacturing
– Air Traffic controllers
– Defence applications
Human-made
Naturally concurrent
Non-linear
No accurate analytic solution
No transformation method
Complexity: analytical solutions cannot be provided.
Differential equations and approximations: inadequate tools
Modeling Artificial Systems
G Y R G:
45s
Y:
10s
R:
55s
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120
YELLOW
GREEN
RED
GREEN
Automata Simulation
Approximation
Results
Experiment
Experimental
FrameEntity
Query
Model's Exp.
FrameModel
Approximate
Results
Computed
Query
Computation
Exp. FrameCompute
G Y R
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120
YELLOW
GREEN
RED
GREEN
Computer Simulation
1950’s: simulation
– Particular solutions for a given problem
– Controlled experimentation
– Time compression
– Solving numerical methods more efficiently
– Conducting a large number of experiments in a controlled
fashion at a low cost
Discrete-Event Dynamic Systems
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120
YELLOW
GREEN
RED
GREEN
Pedestrian
button
pressed at
37.32722
Experiment
Building a Simulator
ProgramResults
ExperimentExperimental
FrameEntity
Results
Simulator
•Enhanced tools/languages•Statistical packages; libraries•Ease to use, flexibility•Parallel/Distributed systems•Enhanced visualization tools•Standards
Discrete-Event M&S formalisms
Discrete-Event M&S methodologies and theory(FSM, FSA, Markov Chains, Petri Nets, CCS, CSP,
Timed FSM, etc.)
Formalization of DE modeling concepts
Improvement of development task– Reuse
– Ease of modeling
– Development cost reductions
DEVS: Discrete-Event Systems Specifications (Zeigler 1976/84/90/2000).
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.
Atomic Models:
M = < X, S, Y, int, ext, , D >.
Coupled Models:
CM = < X, Y, D, {Mi}, {Ii}, {Zij}, select >
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: the CD++ tool
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
D(s) (1)s
DEVS = < X, S, Y, int , ext , D, >
(s) (2)
y (3)
s’ = int (s)
(4)
x (5)
s’ = ext (s,e,x)
(6)
DEVS atomic models
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: the CD++ tool
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
Components
couplings
Internal Couplings
External Input Couplings
External Output Couplings
repair
shop
out
sent
finished
repaired
faulty
generator
(genr)
transducer
(transd)
out report
stop
start
start
Coupled Models
CM = < X, Y, C, EIC, EOC, IC, SELECT >
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: the CD++ tool
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: DEVS Simulators
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
DEVS Toolkits
ADEVS (University of Arizona)
CD++ (Carleton University – Open source)
DEVS/HLA (ACIMS)
DEVSJAVA (ACIMS)
GALATEA (ULA – Venezuela)
GDEVS (Aix-Marseille III, France)
JDEVS (Université de Corse - France)
PyDEVS (McGill)
PowerDEVS (University of Rosario, Argentina)
SimBeams (University of Linz – Austria)
DEVSSim (KAIST, Korea; C++, Java, HLA)
New efforts in China, France, Portugal, Spain, Russia, Czech Republic.
Middleware/OS (Corba/HLA/P2P;
Windows/Linux/RTOS…)
Simulators (single/multi CPU/RT)
Models
Applications
Hardware (PCs/Clusters of PC/HW boards…)
A Layered view on M&S
Once we are confident with the results, we download the models into a special purpose processor.– Same Discrete Time Controller (specified in DEVS)
ECD++ simulator capable to run DEVS models embedded in Real-Time Linux.
Simulator as a Virtual Machine for models– interacting in a Hardware-In-The-Loop fashion
Target: Intel IXP2400 Network Processing Unit– Libraries to communicate the ECD++ Virtual Machine with
specialized RISC microengines
Embedded Target Platform
CD++ Builder Environment
Distributed Collaboration
Parallel Simulation
Stand-alone Simulation
`
Web service client
Rendering/Visualization
(CIMS
BPEL engine
(Webspher)Data capture
(Camera)
WSRF-Engine
(Globus)
CA*net 4/
Internet
UCLP Services
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: the CD++ tool
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
DEVS Applications
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. U.S. Air Force)
DEVS Applications
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.)
Representation of hardware models developed with heterogeneous
languages (Kim, J-K.; Kim, Y.G.; Kim, T.G.)
DEVS/HLA (Distributed Simulation Lockheed-Martin, UA, NJ)
USINOR (Sachem)
…
Component Model Reuse Matrix
xxxCommand
Control Model
xxxxLaser Model
x
x
x
Space Based
Discrimination
x
x
x
Space Based
Laser
x
x
x
x
x
x
x
x
x
Missile Defense
(Theater /
National)
xxxxxxWaypoint &
Heading Nav
Model
xxxxOrbital
Propagate
Model
x
x
x
Integrated
System
Center
x
x
x
Common
Aero Vehicle
x
x
x
x
Joint
Composite
Tracking
Network
xxBallistic
Trajectory
Model
xWeather Model
xxxxEarth &
Terrain Model
xxComm.
Model
xMissile Model
xxIR Sensor
Model
xxxxRadar Model
Space
Operations
Vehicle
Coast Guard
Deep Water
Arsenal
Ship
Global
Positioning
System III
Critical Mobile
Target
Project
Model
U. of New Mexico Virtual Lab for
Autonomous Agents
Computer Network
Middleware
(HLA,CORBA,JMS)
DEVS Simulator
IDEVS SimEnv
V-Lab: DEVS M&S environment for robotic agents with physics,
terrain and dynamics (Mars Pathfinders for NASA).
Reported gains in development times thanks to the use of DEVS
Physics and Chemistry
DLA Surface Tension
Binary solidification
0 0.5 1 1.5 2 2.5
x 104
-100
-80
-60
-40
-20
0
20
40
données expémentales et approximation polynomiale
Heart tissue
Cancer Models
Krebs Cycle in the Cell
Nerve Terminal modeling
Biomedical
Modelica/CD++
model circuitModelica.Electrical.Analog.Sources.PulseVoltage
V(V=10, width=50, period=2.5);Modelica.Electrical.Analog.Basic.Resistor R1(R=0.001);Modelica.Electrical.Analog.Basic.Inductor I1(L=500);Modelica.Electrical.Analog.Basic.Inductor I2(L=2000);Modelica.Electrical.Analog.Basic.Capacitor C(C=10);Modelica.Electrical.Analog.Basic.Resistor R2(R=1000);Modelica.Electrical.Analog.Basic.Ground Gnd;equationconnect(V.p, R1.p);connect(R1.n, I1.p);connect(R1.n, I2.p);connect(I2.n, C.p);connect(I2.n, R2.p);connect(C.n, I1.n); connect(R2.n, C.n);connect(I1.n, V.n);connect(V.n, Gnd.p);end circuit;
Environmental Systems
GRASS “Geographic Resources Analysis Support System”
http://grass.fbk.eu/– Multi-Layered information
– E.g.: Terrain elevation, landuse, etc.
GRASS GIS sample dataset:
“North Carolina, USA”
Coverage:
1.2 Km. x 0.85 Km.
Cell Resolution:
10 m. x 10 m.
Landuses
Landuse
map
Elevation
map
Outline
Motivation
Modeling: DEVS formalism
– DEVS Atomic Models
– DEVS Coupled Models
– Extensions
Simulation: the CD++ tool
– Real-Time
– Parallel
– Distributed
Applications: some applications of the methodology
Visualization: advanced techniques for viewing the
simulation results
Advanced Visualization
Boulevard St. Laurent (MTL)
http://www.cims.carleton.ca/pose
Evacuation in St. Laurent Blvd.
The FUTURE
Advantages of DEVS
Reduced development times
Improved testing => higher quality models
Improved maintainability
Easy experimentation
Automated parallel/real-time execution
Verification/Validation facilities
Difficulties of DEVS
Legacy (current experience of modelers)
Building DEVS models is not trivial– Petri Nets, FSA, etc.
– Do not “scale-up”: inadequate for large scale systems
– Do not address the issues previously discussed
Training– Differential Equations
– State machines
– Programming
Prejudice/Misconceptions– Too complex? (2nd year students)
– Too formal? (not need to know the formalities)
– Overhead? (minimum: <5% for very large models)
Where to go from now
Bridging the gap between Academic world
and actual Application users– Companies being formed
Standardization of models
Building libraries/user-friendly environments
Further research required; open areas.
Concluding remarks
• DEVS formalism: enhanced execution speed, improved model definition, model reuse.
• Hierarchical specifications: multiple levels of abstraction.
• Separation of models/simulators/EF: eases verification.
• Experimental frameworks: building validation tools
• Modeling using CD++: fast learning curve
• Parallel execution of models: enhanced speed
• The variety of models introduced show the possibilities in defining complex systems using Cell-DEVS.
• User-oriented approach. Development time improvement: test and maintenance.
• Incremental development
Further Information
http://cell-devs.sce.carleton.ca
http://youtube.com/arslab
TMS/DEVS 2014
SummerSim 2014
Thanks!