april 22, 2002vmasc board meeting issues in enhancing simulation model reuse c. michael overstreet...

April 22, 2002 VMASC Board Meeting

Issues in Enhancing Simulation Model Reuse

C. Michael [email protected]

Richard E. [email protected]

Osman [email protected]

April 22, 2002 VMASC Board Meeting 2

Motivations for Simulation Model Reuse: To reduce life-cycle costs

model specification code specification & implementation V&V plans & execution 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 efficient components


Perspective/terminology A simulation typically consists of

A collection of interacting sim. models An infrastructure enabling interaction

of those sim. models Mechanisms for observing or

characterizing selected behaviors Mechanisms for user interaction with

simulation


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, improved quality

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

laws of physics might be intentionally ignored Generate believable behaviors

movies, background for training simulations

Different objectives can imply different behaviors, correctness, accuracy, and performance requirements for the same object or situation.


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 among speed, accuracy, included features, costs.


Thus: Simulation 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


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

Ease of understanding Quicker implementation Reduced debugging Often most run-time efficient Improved reuse potential (perhaps)

easier modification, if needed Bias towards elegant, simple

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


Desirability of minimalist view? Does this enhance or impede

reuse? Does this reflect an inappropriate

1950’s view of computing: It’s a sin to waste a cycle.


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, cheaper hardware results in

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

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


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


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.

Infrastructure reused (e.g., HLA)


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.


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.

Still, a slight change in objectives can mean that reuse of these models is undesirable.


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

potentially reusable models Detecting incompatible objectives

and assumptions among selected models

Building models in such a way that reuse potential is enhanced

Determining the level of granularity that best enhances reuse potential.


Key reuse issues: research needed - 2 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?


Comments on issues Some 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


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


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

economics. The changing costs of computing

changes the models we choose to build.

The changing costs of computing changes the economics of reuse: Faster hardware makes execution

inefficiencies due to reuse irrelevant


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 objectives are well-understood and stable.

Completely automated reuse appears scientifically infeasible.

Automated support is more likely economical.

april 22, 2002vmasc board meeting issues in enhancing simulation model reuse c. michael overstreet...

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