ECE 449/549

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. p val content v double Sending/Receiving/Interpreting Messages how to use casting to receive instances of arbitrary entity subclasses coupled model entity out in 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 (cont’d) multiple copies of an object are needed to avoid hard-to-find dependencies. coupled model entity out in 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 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

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. EF EFA GEN TRANSD EF ARCH ARCH PROC COORD PROC Hierarchical Model Construction

12. Example: Flat Coupled Model gpt start start out generator (genr) processor (proc) in out stop out arrived transducer (transd) out solved

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

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

15. Hierarchical Models and Experimental Frame Modularity in DEVSJAVA Create EF ef.setBlackBox(true) m.setBlackBox (true)

16. 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. Architecture in in out out co x y p2 p0 p1 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.

21. 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.

22. 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

23. carriage specialization contents specialization freight cargo 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

24. city roadNet roadSeg line traffic Genr (G) traffic Light (L) road Sched (S) seg S S S G G G L L L step event Seg Seg Seg Seg Seg Seg Seg Seg Seg ... ... ... roadNet roadSeg SES Multiplicities city multiple aspect roadNet multiple entity

25. 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 - density - strength transportation class - speed decomposition motor transmission propulsion aluminum car ship steel airplane - buoy ancy - density = low - strength = medium - speed = high motor transmission wing.propulsion wood wings wheels XML: <object> aluminum.airplane.vehicle <density> low <strength> medium <speed> high <madeof> 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

26. Repository of DEVS components 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

27. SES and UML as Ontologies

28. 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 a>0 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).

29. 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 – a*numWorkers sum workerCell (varGen)

30. SES in Natural Language and Model Base Aspect Coupling Specialization

31. XML Output Coupling Info

32. 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

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