jan. 29, 2002grand challenges in simulation issues in enhancing model reuse c. michael overstreet...

Jan. 29, 2002 Grand Challenges in

Simulation

Issues in Enhancing Model Reuse

C. Michael [email protected]

Richard E. [email protected]

Osman [email protected]


Simulation 2

Motivations for Model Reuse: To reduce life-cycle costs

model specification code specification & implementation V&V plans & implementation accreditation

To reduce time until new simulation is available near instantaneous construction of new

simulations To improve quality of new simulations

based on trusted or time-optimized components


Simulation 3

Perspective/terminology A simulation typically consists of

A collection of interacting models An infrastructure enabling interaction

of those models Mechanisms for displaying or

summarizing some model behaviors Mechanisms for user interaction with

simulation


Simulation 4

Fundamental assertions - 1: Each simulation is constructed to meet a

concrete set of objectives, such as: Improve system performance

planning, design Improve understanding

scientific modeling; manager’s intuition Reduce training time

“correctness” of some aspects may not be important Build a fun game

laws of physics might be intentionally ignored Different objectives can imply different

behaviors, correctness, accuracy, and performance requirements for the same object.


Simulation 5

Fundamental assertions - 2:

Objectives determine desired behaviors of models.

Desired behaviors determine model content. Models are based on abstractions and

assumptions. Appropriateness of abstractions depends on

desired behaviors. The models used in simulations reflect

sometimes subtle tradeoffs of speed, accuracy, included features, costs.


Simulation 6

Thus: Model reuse must take both

original and new objectives into consideration; valid reuse requires consistency between the two sets of objectives.

Similarly for model assumptions and constraints


Simulation 7

Occam’s view of simulation: The simplest, minimal model is best:

Ease of understanding Quicker implementation Reduced debugging effort Likely most run-time efficient Improve reuse potential

easier modification, if needed Bias towards elegance

Thus models should be just barely good enough to meet objectives.


Simulation 8

Economic facts of simulation: Costs are in development & CPU cycles

are free. Tyranny of better software and cheaper

hardware: User “needs” are often quite elastic; if it’s

not too expensive, it’s a requirement. Faster, cheap hardware results in

unanticipated new uses of simulations (e.g., real-time decision support)

Many of today’s simulations will be perceived as inadequate tomorrow.


Simulation 9

Conflicting user needs Create “total immersion” interactive

environment Create believable environment Create new simulations on demand Create simulations cheaply Incorrect behavior unacceptable Some incorrectness required

Games Tutorials

Execution efficiency vital


Simulation 10

Example levels of reuse Plug ‘n play: no changes necessary

ModSAF a successful example Existing model “easily” altered to

provide new or modified behaviors Can result in significant cost benefit

Modeling approach useful in new domain Reuse concepts, architecture, designs,

etc.


Simulation 11

Impossible goal: automated reuse of arbitrary models? Page & Opper showed that

deciding if a collection of models meets a set of objectives is NP-complete.

Overstreet & Nance showed that deciding if two models are equivalent is unsolvable.


Simulation 12

Feasible goal: automated reuse of specially constructed models ModSAF (OneSAF): can build “new”

simulation by combining existing library of models as needed.

Each model is built from consistent set of objectives so that it will interact with other models correctly.

Adding a new model to library requires that it be built in conformance to these objectives.

A slight change in objectives could mean that reuse of these models is undesirable.


Simulation 13

Key reuse issues: research needed - 1 Determining how to locate

potentially reusable models. Detecting incompatible objectives

among selected models. Detecting incompatible assumptions

among selected models. Building models in such a way that

reuse potential is enhanced.


Simulation 14

Key reuse issues: research needed - 2 Determining the level of granularity that

best enhances reuse potential. Capturing and representing the

objectives, constraints and assumptions of each model.

Determining if constraints (such as speed, memory) will be met with selected collection of models.

If individual models are valid, what does this imply about a new combination?


Simulation 15

Comments on issues Many of these issues are well know to

designers of Simulation Programming Languages, for example, granularity: GPSS (and many current simulation

programming languages) consists of a collection of reusable models, each easily parameterized.

But building a new simulation is like writing a new program from scratch.

Use of high level components results in faster development but loss of flexibility


Simulation 16

No single solution Execution overhead:

Some models are run once and thrown away Some model executions must meet real-time deadlines Some are execution intensive but not real-time

Some models need only be suggestive (wake of a ship at sea); others must be highly precise (fluid flow about a supersonic wing).

A solution should be less expensive than the problem it solves we need both quick & dirty simulations and well-

documented, highly reusable simulations


Simulation 17

Summary - 1 Reuse is, in large part, motivated by

economics. The changing economics of

computing changes the models we choose to build.

The changing economics of computing changes the economics of reuse.


Simulation 18

Summary - 2 Key to reuse is the capturing of

objectives, assumptions and constraints. Models can be designed for reuse, but it

appears feasible only when original objectives are compatible.

Completely automated reuse appears economically infeasible

Automated support is more likely economical.

jan. 29, 2002grand challenges in simulation issues in enhancing model reuse c. michael overstreet...

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