# questions to answers via models: answers to questions?

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Questions to Answers via Models: Answers to Questions?. Professor Rob Pooley Heriot-Watt University. We all agree…. Systems are becoming far too complex to understand easily in their entirety. Abstraction is the key to managing complexity. - PowerPoint PPT PresentationTRANSCRIPT

Questions to Answers via Models:Answers to Questions?Professor Rob PooleyHeriot-Watt University

SummerSim 2008

We all agree.Systems are becoming far too complex to understand easily in their entirety.Abstraction is the key to managing complexity.Modelling provides abstractions which answer questions.

So what are the questions that are left?

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More questions than answers?Modelling has been an important tool in many areas for four decades or more.Today sees renewed interest in the application of modelling to the understanding of enormously complex systems.Yet, there remain many challenges in making modelling accessible to non-specialists.Here I pose the questions which seem meaningful to me and attempt to answer some of them.

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Key questionsHow can modelling be more accessible?How can modelling be more efficient?How can we model more complex systems?

Clearly these are inter-related to some extent.

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How can modelling be more accessible?From its earliest uses, modelling has sought to make itself understandable.From its earliest uses modelling has sought to make itself precise.This has created a major tension in the definition of modelling techniques and in their automation.

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How can modelling be more accessible? Broadly speaking there are three aspects to this problem:How can we make it easier to create models?How can we make the results from models more understandable?How can we make it possible to ask our questions?

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How can we make it easier to create models?Here the key questions are:

How can we describe models more effectively?How can we make it easier to implement the models we have described?How can we aid validation of our models?

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Describing modelsTocher began the move to intuitive ways of expressing models with his activity cycle diagrams.

Tocher K.D. 1963. The Art of Simulation, The English Universities Press, London Awaiting arrivalsIdle arrivalsUnloadingDockingIdle tugIdle jettyLeavingJetty cycleArrival cycleTug cycle 1Tug cycle 2

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Progress since TocherWe can see many developments in making diagrams a key for describing modelsHills P.R. 1968. "Hocus a simple approach to simulation", Data Processing, May 1968 Pegden C.D. 1985 Introduction to SIMAN, Systems Modeling Corp., State College, Penn. Kurose J.F., J.G. Kurtiss, R.F. Gordon, E.A. McNair and P.D. Welch March 1986. A graphics-oriented modeller's workstation environment for the RESearch Queueing Package (RESQ), IBM Research Report RC11803, IBM Hawthorne Laboratories, Yorktown Heights, NY10596 Pritsker A.A.B. 1984. Introduction to simulation and SLAM II, John Wiley and Sons, New York etc.

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Implementing modelsWith graphical workstations came graphical input.Kurose J.F., J.G. Kurtiss, R.F. Gordon, E.A. McNair and P.D. Welch March 1986. A graphics-oriented modeller's workstation environment for the RESearch Queueing Package (RESQ), IBM Research Report RC11803, IBM Hawthorne Laboratories, Yorktown Heights, NY10596 Pritsker A.A.B. 1979. Modelling and analysis using Q-GERT Networks, Systems Publishing Corporation, Lafayette, Indiana Barber E.O. and P.H. Hughes August 1990, Evolution of the Process Interaction Tool, A Graphical Editor for DEMOS, in Proceedings of the Seventeenth SIMULA Users Conference, Pilsen, pp 171-180, Association of SIMULA Users

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Solution method focusedMany attempts focused on paradigms which could be readily solved, like Petri nets and queueing networksMelamed B. and R.J.T. Morris 1985. Visual Simulation: the Performance Analysis Workstation, IEEE Computer, Vol 18 No 8, pp 87-94, August 1985 Molloy M.K. and P. Riddle April 1986. The stochastic Petri net analyser system design tool for bit-mapped workstations, Technical Report, Department of Computer Science, University of Texas at Austin Lindemann C. September 1992. DSPNExpress: a Software Package for the Efficient Solution of Deterministic and Stochastic Petri Nets, in R. Pooley and J. Hillston Eds. Computer Performance Evaluation - Modelling Techniques and Tools, 6th International Conference on Modelling Techniques and Tools for Computer Performance Evaluation, Edinburgh, Edits 10, Edinburgh University Press

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Queueing networks intuitive?

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Database

SCSI

Disc

CPU

Petri nets easy to query?

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_1006517341.unknown

ValidationMostly focuses on animation (was tracing) of simulationsSome work on proving behaviourStochastic Petri nets (GSPNs) have behavioural checking of untimed netsStochastic process algebras such as PEPA also have some behavioural checkingLimited work on proving simulations correctYcesan E. and L. Schruben 1992 Structural an Behavioural Equivalence of Simulation Models, ACM Transactions on Modelling and Computer Simulation, Vol 2 No 1, January 1992, pp 82-103 Birtwistle G.M., C. Tofts and R.J. Pooley October 1993, Characterising the Structure of Simulation Models in CCS, Transactions of the SCS Vol 10 No 3, pp 205-237 Zeigler B.P. 1976 Theory of Modeling and Simulation, Wiley, New York

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How can modelling be more efficient?Here we can perhaps identify two aspects:

Are there more efficient techniques for model solution?Can models be simplified prior to solution?

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Are there more efficient techniques for model solution?Model level techniquesDecomposition and aggregationNortons Theorem applied to queuesBeilner H. June 1989. Structured Modelling - Hierarchical Modelling, in Proceedings of the European Simulation Multiconference, RomeSolution techniquesSee elsewhere in this conference

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Queueing networks efficient and intuitive?Decompose and short circuitComposeDatabaseSCSIDiscCPU

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FESC

CPU

Database

SCSI

Disc

HIT a basis for decomposition

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task

ACTIVITIES

read

write

execute

io_comp

compute

ACTIVITIES

disk_1

disk_2

access1

access2

access1

access2

read_io

write_io

read_io

write_io

compute

read

write

read

write

Can models be simplified prior to solution?This depends on proving equivalence of a complex and a simpler modelSchruben tackled simple cases of equivalence in simulationPEPA uses it extensively for stochastic process algebraHillston J.E. April 1994. A Compositional Approach to Performance Modelling, PhD Dissertation CST-107-94, University of Edinburgh, Department of Computer Science Some progress has been made for wider classes of simulation modelRob Pooley, "Behavioural Equivalence in Simulation Modelling", Simulation Modelling, Practice and Theory, to appear 2007

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How can we model more complex systems?Crucially this combines much from before, resulting in three key questions:Can we find meaningful, but solvable abstractions of complex systems?Can we relate detailed results to high level structure?Have we ignored significant details?

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Meaningful, but solvable abstractions?There have been several domain specific modelling systemsSoftware HITNetworking OPNETCan we find a general abstraction?Petri nets too technical?Queues too abstract?Unified Modelling Language???

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UMLThe Unified Modelling Language comes from the software domainIt is used for other kinds of modelsBusiness processesBiological systemsPerformance models can be derived using annotationIs this the way of the future?

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Collaborating objects

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xPlayer:Player

yPlayer:Player

b:Board

u:Umpire

States describe internal behaviour of objects

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XhasMoved

XtoMove

XtoMove

YtoMove

XtoMove

YhasMoved

move(r,c)/^umpire.validate(X,r,c)

move(r,c)/^umpire.validate(Y,r,c)

invalid()/^xPlayer.makeAMove()

invalid()/^yPlayer.makeAMove()

valid()/^xPlayer.makeAMove()

valid()/^yPlayer.makeAMove()

Combine the two

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States reachable in model

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Relate detailed results to high level structure?

If our input is high levelIf our solvers are low level(Except simulation?)How do we return the results in a meaningful way?

Still an issue!

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Have we ignored significant details?How do we know about rare events?

Have we designed for the average?

Have we answered the real question?

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Issues and ideasLook to other paradigmsBiologically inspired approachesMembrane computingSystem heart rate diagnostic analoguesDialogues and wizardsHigh Level Architecture (HLA) style heterogeneity

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To borrow a phrase

No silver bullet

Yet!

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Collaborative Research Center 559 SymposiumIn this talk, I hope to provide a survey of the key issues which have surrounded modelling since the earliest days of digital computers.

What is clear is that many of the original issues remain only partially resolved at best.

At the same time, the scale and scope of the systems we wish to analyse has grown considerably.

New techniques and approaches are essential for progress.Questions to Answers via ModelsCollaborative Research Center 559 SymposiumThe problem of complexity does not occur in