1. Continue the car washer example Experimental frame
Outline
Continue the car washer example
Experimental frame
Chapter 7 – simple architecture

2. Expectations After This Class
Decide your project topic by Wednesday
Know how DEVS works and what it can do
Know how to build DEVS models and run simulation using DEVSJAVA
Know how your project is going to be accomplished
Your should be clear about the problem statement of your project
You should be clear about the aim of your project
You should have a picture of how you are going to implement your project

3. It is all about modeling
Why DEVS ?
It is all about modeling
Software Engineering is not only about programming
Software Engineering is not only about design
Software Engineering is also about specification, analysis, and verification
A different level specification from UML
MDA (Model Driven Architecture)

4. An carwash example Chapter 4: exercise 1 part a -- car wash
To simplify the problem, let’s first assume the car wash center only takes cars and will reject any incoming cars if it is busy
Add random number to the car generator
Let’s consider the truck
Let’s consider the capacity of the car wash center
Let’s analyze this system
Add a test file – run simulation without simView

5. Processor with queue
illustrating how to process a bag of inputs (multiple concurrent events)
accumulate the inputs on port “in” into the queue
public class procQ extends proc{
protected Queue q;
public procQ(String name,
double procTime ){
super(name,procTime);
q = new Queue(); }
public void deltext(double e, message x){
Continue(e);
if (phaseIs("passive")){
for (int i=0; i< x.size(); i++)
if (messageOnPort(x, "in", i)){
q.add(x.getValOnPort("in", i)); }
holdIn("busy", procTime);
job = (entity)q.first();
else if (phaseIs("busy")){
for (int i=0; i< x.size();i++)
if (messageOnPort(x, "in", i)) {
entity jb = x.getValOnPort("in", i);
q.add(jb);
class Queue is a container with FIFO discipline
this makes sure the processed job is the one at
the front
public void deltint( ){
q.remove();
if(!q.isEmpty()){
job = (entity)q.first();
holdIn("busy", procTime); }
else passivate();
remove the job at the front that was just finished
SimpArc
.procQ
GenCol
.Queue

6. Experimental Frame
The main model of interest – carWashCenter
Although a model, such as the carWashCenter, can be tested in a standalone fashion, it really does not “come to life” until it is coupled with a module capable of providing it input and observing its output.
An experimental frame module is a coupled model, which when coupled to a model, generates input external events, monitor its running, and processes its outputs.

7. Experimental Frame (cont.)
The design of an experimental frame reflects the objectives one has in experimenting a model.
Thus the same model may be coupled to different experimental frame modules, which observe it under different conditions.
Conversely, the same experimental frame module may be employed to experiment with different models under the same conditions.
Two major components of a experimental frame are generator and transducer.

8. Experimental Frame for Workflow Performance Measurement
SimpArc ef
Output lines may diverge to indicate the occurrence of simultaneous events. When genr sends out a job identifier on port “out”, it goes at the same clock time, both to the “ariv” port of transd and port “out” of ef, hence eventually to some processor model.
Input lines may converge, i.e., two or more source ports connected to the same destination port, can occur. Bags represent the collection of inputs that arrive simultaneously at a component.
Instances of the classes genr and transd are coupled together to form the experimental frame, ef.
The input port “in” of ef is for receiving solved jobs which are sent to the “solved” input port of transd via the external input coupling.
There are two output ports: “out”, which transmits job identifiers sent to it by genr, and 'result which transmits the performance measures computed by transd. External output couplings bring about both these transmissions.
There are two internal couplings:
the output port “out” of genr sends job identifiers to the 'ariv port of transd
the output port “out” of transd which couples to the 'stop input port of genr.

9. Experimental Frame in a Flat Coupled Model
SimpArc
gpt
gpt
start
start
generator
(genr)
out
processor
(proc)
stop in
out
out
arrived
transducer
(transd)
out
solved

10. Experimental Frame/Model Modularity
SimpArc
efp
efP ef
start
start
start
out
out
generator
(genr)
processor
(proc)
stop in
out
out
arrived
transducer
(transd)
out
out
solved
solved

11. ef Hierarchical Coupled Model efP start Processor With Queue (procQ)
SimpArc
efp
efP ef
start
start
out
Processor
With
Queue
(procQ) in
out
out
out
solved

12. Multiprocessor Architecture (chapter 7)
Single Processor
Multi-Server
Pipeline
Divide and Conquer
Turn around time and throughput

13. Architecture Model
proc
coord
Job_in
Job_finished

14. Multiprocessor Architecture (chapter 7)
SimpArc in DEVSJAVA – efp.java
Single Processor – proc.java
Multi-Server – multiServer.java
Pipeline – pipeSimple.java
Divide and Conquer – DandC3.java

15. Workflow Coordinator Models
public class Coord extends proc{
public Coord(String name){
super(name,1);
inports.add("setup");
inports.add("x");
outports.add("y");
public void initialize(){
passivate();
super.initialize();; }
protected void add_procs(devs p){ //use devs for signature
System.out.println("Default in Coord is being used"); }}
Coord
Atomic model
I/O Behavior Description
divide and conquer coordinator
breaks incoming jobs into parts for processing and compiles the results into a final output
pipeline coordinator
routes incoming jobs through a series of processing states and outputs the results
Multiserver coordinator
routes incoming jobs for processing and collects the results for final output
SimpArc
divideCoord
SimpArc
pipeCoord
divide
Coord
pipe
Coord
multi
Server
Coord
SimpArc
multiServerCoord

16. Workflow Architecture Modelsin
out x y co p0 p1 p2
Architecture
SimpArc
DandC3
Coupled model
Divide and conquer
divide and conquer coordinator with processors
Pipeline
pipeline coordinator with processors
Multiserver
multiserver coordinator with processors
SimpArc
pipeLine
SimpArc
multiServer

17. Performance of Simple Workflow Architectures
Processing Time
Average
Turnaround
Time
Maximum
Throughput
Simple Processor p
1/p
Multiserver
homogeneous
p1,p2,p3
p1=p2 = p3 =p
3/thruput
1/p+1/p2+
1/p3
3/p
Pipeline
p1 + p2 + p3 = p
p1=p2 = p3=p/3
1/max{p1,p2,p3}
Divide and Conquer
p1=p2 = p3 =p/3
max{p1,p2,p3}
p/3
SimpArc
.proc
SimpArc
multiServer
SimpArc
pipeline
SimpArc
DandC3
Average turnaround time and maximum throughput as a function of processing time

18. Read chapter 7 Project Homework
Send me your project statement by Wednesday
Start to exercise DEVSJAVA and build your own models