ECE 449549

1
ECE 449/549

• Class Notes 4
• DEVS Models for Processing and Coordinating
  Workflow
• /
• System Entity Structure
• Sept. 2008

2
DEVS Models for Processing and Coordinating
Workflow

• Sending/Receiving/Interpreting Messages
• Processor Models
• Workflow experimental frame components
• Work Flow Coupled Models
• Experimental Frame for Workflow Performance
  Measurement
• Hierarchical Model Construction
• Workflow Coordinator Models
• Workflow Architecture Models
• Performance of Simple Workflow Architectures

3
Sending/Receiving/Interpreting Messages how to
use casting to receive instances of arbitrary
entity subclasses
coupled model
entity
in
out
A
B
doubleEnt
doubletEnt(double)
double
double
getv() ? double
coupling (A,out,A,in)
public message out( ) message m new
message() m.add( makeContent("out", new
doubleEnt(1.2)) return m
in
out
cast
message
doubleEnt
add
p
getv()
val
doubleEnt
deltext(double e,message) x)if
(somethingOnPort(x,"in") entity val
getEntityOnPort(x,"in") doubleEnt f
(doubleEnt)val double v f.getv()
v
content
double
casting the received entity down to the doubleEnt
subclass
4
Sending/Receiving/Interpreting Messages
(contd) multiple copies of an object are needed
to avoid hard-to-find dependencies.
coupled model
entity
in
out
A
B
job

• job(procTime)
• update(e)
• procTime e
• copy() return new
• job(procTime)

job
job
coupling (A,out,A,in)
This instance of job is now shared in common by
both A and B if either makes a change to its
state, then the other will also be subject to
it. For example, if B does
job.update(10) then the instance at A will be
similarly altered. This can lead to mysterious
effects (similar to quantum entanglement) where
components of a model can influence each other
outside of their interface couplings. It is
difficult to trace this kind of non-modularity.
Suppose A sends its instance of job directly to
B public message out( ) message m new
message() m.add( makeContent("out", job) return
m
The right way B stores a copy of its
input deltext(double e,message) x) if
(somethingOnPort(x,"in)) job temp
getEntityOnPort(x,"in") myJob
temp.copy() where copy() is a method you define
to create a new instance of job and give it
values of an existing one.
out
and B stores it as its instance deltext(double
e,message) x) if (somethingOnPort(x,"in")) myJob
getEntityOnPort(x,"in")
The cure is simple create a new instance as a
local copy of an entity if it is to be altered
(this happens automatically when using toString()
and toObject(), see chap. 12)
5
Processor Models
6
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 i0 ilt 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 i0 ilt
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
7
Workflow experimental frame components

• To compute the performance measures, the
  transducer, places job-ids that arrive at its
  'ariv input port on its arrived-list together
  paired with their arrival times.When, and if, the
  job-id also appears at the 'solved input port,
  the transducer places it on the solved-list and
  also computes its turnaround time. it maintains
  its own local clock to measure arrival and
  turnaround times.
• In transd, an internal transition is used only to
  cause an output at the
• end of the observation interval. In a more
  general experimental frame, the role of
  terminating the simulation run would be handled
  by a component
• called an acceptor.

• The transducer measures two performance indexes
  of interest for work flow the throughput and
  average turnaround time of jobs in a simulation
  run.
• throughput is the average rate of job
  departures from the architecture,
• estimated by the number of jobs processed during
  the observation interval,
• divided by the length of the interval.
• A job's turnaround time is the length of time
  between its arrival to the processor and its
  departure from it as a completed job.
• For the simple processor, the turnaround time is
  the same as the processing time. (For more
  complex architectures, this relationship is not
  necessarily true as we shall see.).

generator is an autonomous model, (its behavior
is self induced by recurring internal events)
hence, it does not need an external transition
function To dictate its response to external
input events. The added an input ports start
and stop when stimulated, start and stop the
generation of outputs.
8
Work Flow Coupled Models
9
Basic Workflow Coupled Model generator,
processor, transducer
10
Experimental Frame for Workflow Performance
Measurement

• 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.

11
Hierarchical Model Construction
12
Example Flat Coupled Model
gpt
start
start
out
generator (genr)
processor (proc)
out
in
stop
out
arrived
transducer (transd)
out
solved
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Experimental Frame/Model Modularity
efP
ef
start
start
start
out
out
generator (genr)
processor (proc)
out
in
stop
out
arrived
transducer (transd)
out
out
solved
solved
14
Hierarchical Coupled Model
efP
ef
start
start
Processor With Queue (procQ)
out
out
in
out
out
solved
15
Hierarchical Models and Experimental Frame
Modularity in DEVSJAVA
Create EF
ef.setBlackBox(true)
m.setBlackBox (true)
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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
divide Coord
pipe Coord
multi Server Coord
17
Workflow Architecture Models
18
Performance of Simple Workflow Architectures
Average turnaround time and maximum throughput as
a function of processing time
19
The System Entity Structure Using Simulation
to Search and Optimize

• System Entity Structure
• Examples
• Model Base Organization and Synthesis
• System Entity Structure for Optimal crew size

20
System Entity Structure

• System Entity Structure (SES) represents a family
  of hierarchical DEVS models
• Particular members of the family are generated by
  process called pruning to generate a pruned
  entity structure (PES)
• A hierarchical DEVS model ready to execute is
  obtained by transforming a PES, i.e., accessing
  components in a repository and coupling them
  together according to the PES specification.

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System Entity Structure Axioms

• SES is represented as a labeled tree with
  attached attributes that satisfies the following
  axioms
• alternating entity/aspect or entity/specialization
  
• Each node has a mode that is either
  entity/aspect or entity/specialization
• such that a node and its successors are always
  opposite modes the mode of the root is entity.
• Coupling is associated with aspects
• uniformity
• Any two nodes with the same names have
  identical attached variable types and isomorphic
  sub-trees.
• strict hierarchy
• No label appears more than once down any path
  of the tree.
• valid brothers
• No two brothers have the same label.
• attached variables
• No two variable types attached to the same
  item have the same name.

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aspect
entity
specialization
Key Items Entity An independently identified
real world object Aspect Represents one
decomposition out of many possible of an
entity Specialization represents the way in which
the entity can be categorized into specialized
entities ? Selection rule
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SES Example
elevator
physical decomposition
motion specialization
floors
contents
escalator
pas- senger
lift
Rule if select freight from carriage spec
then select lift from motion
spec and
select cargo from contents spec
people
Rule if select passenger from carriage spec
then select people from contents spec
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SES Multiplicities
city
multiple aspect
roadNet
multiple entity
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System Entity Structure Inheritancee.g., Mapping
into XML
specialized entities inherit their parents
subtriee
vehicle
aluminum.airplane.vehicle
- density low - strength medium
speed high
material composition
decomposition
transportation class
- density - strength
- speed
decomposition
motor
propulsion
transmission
aluminum
car
ship
steel
airplane
- buoy ancy
- density low - strength medium
- speed high
motor
wing.propulsion
transmission
wood
wings
wheels
XML ltobjectgt aluminum.airplane.vehicle ltdensitygt
low ltstrengthgt medium ltspeedgt high ltmadeofgt motor
transmission wing.propulsion
Rule if select airplane from transportation
then select
aluminum from material composition
and wings from propulsion
Note buoyancy field is not present
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SES/Model Base Synthesize new models from
reusable DEVS components
SES
Develop a System Entity Structure to organize
the Repository
PES
transforming
pruning
hierarchical DEVS model
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SES and UML as Ontologies
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Worker Interference

• A wall has a 1000 bricks. A brick layer can build
  such a wall in 1000 minutes. However, if N
  people work on the wall together each can only
  work at this standard rate until 1000-aN bricks
  have been laid. At that point, the remaining (aN)
  bricks have to be done by one person at the
  standard rate. Note agt0 is the interference
  factor.
• Develop a DEVSJAVA model with parameters, N and
  a.
• Develop an experimental frame (efb) the measures
  the time it takes to complete the task
• Run your model in a) coupled to efb and note the
  time.
• Derive a formula for the completion time as a
  function of N and find the optimal value of N
  (the number of workers that minimizes the time to
  completion).

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System Entity Structure for Worker Interference
Finding the optimal crew size by simulation
workerOpt interference param, a
maxCrewSize
(min(35,1000/a)
distribute these on different computers for
parallel execution
fireOnceNeuron
workCrews
workCrew numWorkers
rate Estimator (EF)
workerCellSpace (arch)
var Dsiplay
workerCells
thresholdTester threshold 1000
anumWorkers
sum
workerCell (varGen)
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SES in Natural Language and Model Base
Aspect
Coupling
Specialization
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XML Output
Coupling Info
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SESBuilderwww.sesbuilder.com

• Developed by RTSync Corp and ACIMS(Arizona Center
  for Integrated Modeling and Simulation) at the
  University of Arizona
• Natural Language Expression for the Component
  Composition and Data Modeling
• Automatic XML and Schema output

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Case Study Application to the US Climate Data
Overall System Structure
DEVS Component Repository
Source Data to DEVS model
XML Metadata
DEVS Components
Source Data (US Climate Data)
Query by SES (Component name, Coupling Info)