INNOVATIVE METHODS AND TECHNIQUES FOR HIGH-PERFORMANCE AND - RELIABILITY MODELING AND SIMULATION (M&S)

(1)

INNOVATIVE METHODS AND TECHNIQUES

FOR HIGH-PERFORMANCE AND - RELIABILITY

MODELING AND SIMULATION (M&S)

Prof. Dr. Axel Lehmann

Institut für Technik Intelligenter Systeme (ITIS)

Institut für Technische Informatik

at the

Universität der Bundeswehr München

Germany

e-mail: [email protected]

URL: http://www.informatik.unibw-muenchen.de/inst4/

(2)

OUTLINE

1. Modeling & Simulation (M&S):

“State of the Art” and Demands

2. M&S: A Multiple-Phase Development & Application Process

3. Component-Based M&S

4. M&S - Verification, Validation & Accreditation (VV&A)

5. Parallel and Distributed M&S

(3)

1. M&S - “State of the Art” and Demands

Trends / Requirements of (Technical) Systems Developments

•

Rapid technological innovations

Æ

new information and communication technologies

Æ

efficient,

powerful,

computer-assisted tools (e.g. CAD, CAM, . . . )

•

Increasing systems complexity & lifetime

Æ

embedded

systems

Æ

distributed

systems

Æ

networks of components / systems

•

Increasing productivity & cost-benefit

(4)

Major

Challenges

•

“Mastering” of system(s) complexity

over lifetime w.r.t. multiple aspects /goals?

(safety, reliability, performance, ...)

(5)

•

“Mastering” of model(s) complexity

!?

⇒

STRATEGIC approach: “Devide and Conquer” !!

(6)

Current Importance of M&S

(as a discipline / methodological approach / tool set):

Æ

Receives increasing acceptance by decision makers

Æ

Becomes more and more a “standard” method / tool set

Æ

Seen as a major enabling technology for innovations

(7)

• 1995, US-DARPA:

“... M&S is one of the top-10-key enabling technologies ...

• 1998, DoD (Dr. Gansler): “... by the year 2000 ... Systems development in 25 % less time...”

• 1999, US-government:

IT

2

Research Initiative

• 1999, PITAC report:

“Fund research in ... global-scale networks and its associated

information infrastructure .... including .... Modeling and

simulating network behaviour (Recommendation 3.3.2)

(8)

Major Challenges for M&S applications:

• Increasing systems / M&S complexity

• Decreasing cycle times for systems / M&S innovations

• Increasing lifetimes of systems, models, and simulations

• Increasing variety of M&S-aspects / purposes

• Safety, reliability, ... cost-benefit constraints (for systems / M&S)

• “Hardware-/Software-/User-in-the loop” simulation

• User acceptance; ease of use & credibility

(9)

General Approaches, Methods to cope these

M&S-Challenges:

• Hierarchical modeling

• Hybrid models (OR-, analytic, simulation solutions)

• Interoperability of models

• Reuseability of models (model components)

• Collaborative M&S

• Parallel & distributed M&S

• Improved model credibility by application of VV&A

• Model engineering

(10)

2. M&S - Design, Implementation and Application

Process

Example: Effectiveness and efficiency of a “Booking System”

(Customers who access their bank accounts to transfer or to deposit

money; client-server architecture)

Goal parameters to be analysed might be, e.g.:

Æ

processing time per transaction

Æ

client/server utilization

Æ

queueing time, queue length

How to approach this (complex) problem?

⇒

“Divide and Conquer”:

(11)

Phases & Products in the M&S

-Development Process

Model Input Data

Solution Techniques

Model Documen-tation Objekt Objekt Objekt Objekt Intera ktion Systemgrenze “Umwelt-objekt“ “Hauptobjekt” mit Modellattributen

Communicative

Conceptual Model

, Project Objectives

Structured

Problem Description

Formal

Model

Executable

Model

Results

Problem

Definition

System

Analysis

Model

Formalization

Implementation

Experimentation

Modeling Method

System Observations

Examination Aim

Experimental req.&constr.

Technical req.&constr.

Formal req.&constr.

Conceptual req.&constr.

(12)

M&S-Sources of Knowledge and

Expertise

Problem

Definition

System

Analysis

Model Formalization

Implementation

Experimentation

Modeling

Expertise

(HW-)SW-Expertise

Experimental

Design and

Domain

Knowledge

User

Knowledge

(13)

Problem

Definition

System

Analysis

Model

Formalization

Implementation

Experimentation

Project Manager

(Contractor)

Modeller

Domain Expert

User

Modeling

Expertise

Experimental

Design and

Analysis

Domain

Knowledge

(HW-)SW-Expertise

User

Knowledge

Customer

(14)

Æ

“Divide and Conquer”-approach:

decomposition of (complex) models/problems

→

“components”

Æ

“Component” : not a well-defined term !!

Example: “Booking System”

(15)

Example: „Booking System“

Conceptual Model :

I/O device 1

CPU

T 1

T n

I/O device n

• • • • • •

Server

(16)

Example: Booking System

(17)

Example: Booking System

Formal Model

(Version 2):

(18)

Little‘s law:

w

Q

t

k

=

λ

or

=

λ

Performance measures:

With: response time t, queueing time w, service rate µ

t = w + ; k =

µ

1

Σ

k • p (k)

6

State probability

p(k)

6

Utilization

ρ

( m service stations)

1

<

=

µ

λ

ρ

m

service rate

arrival rate

(19)

Conclusions

↔

“model component”

(e.g. regarding the example “Booking System”)

•

Problem Description:

Æ

pragmatism / goal specification

•

Conceptual Model:

Æ

structural & functional description of

“components”

Æ

different levels of abstraction

•

Formal Model:

Æ

formal specification of

“components”

(

↔

selected modeling paradigm(s))

Æ

different levels of abstraction

Æ

hierarchical modeling approach

(

•

decomposition into submodels /

“components”

•

Executable Model(s):

e.g.

Æ

analytic solution reusable SW-

“components”

(20)

Phases & Products in the M&S

-Development Process

Model Input Data

Solution Techniques

Model Documen-tation Objekt Objekt Objekt Objekt Interaktion Systemgrenze “Umwelt-objekt“ “Hauptobjekt” mit Modellattributen

Communicative

Conceptual Model

, Project Objectives

Structured

Problem Description

Formal

Model

Executable

Model

Results

Problem

Definition

System

Analysis

Model

Formalization

Implementation

Experimentation

Modeling Method

System Observations

Examination Aim

Experimental req.&constr.

Technical req.&constr.

Formal req.&constr.

Conceptual req.&constr.

(21)

X

(22)

6

Model Federation Level

„Black Boxes“

6

Model Level

Autonomous, interoperable

models

6

Submodel / Object Level

Submodels /

Object structures of

different modeling

paradigms

6

Function Level

Coded basic functions /

algorithms

( ) 6 0 und 0 mit : 0 0 ) ( wenn : ) , ( ) ( ) ( . 1 1 1 1 ≤ < ≤ <    − ⋅ ⋅ − + > = ∀ + + + + s n i sonst t l t t z t t r f t l t l s s i si s i i si i si i s

Model („Component“) Specification

Levels

Library of

submodels &

communication

infrastructure

Library of

objects/methods (for

interaction)

Program Library

Model

repository

(23)

Component-Based M&S : Current Approaches

•

Hierarchical modeling via

- decomposition

- aggregation

- hybrid solution / implementation techniques

•

Generic modeling object templates

(depending on the modeling paradigm), e.g.

- class / object libraries

•

Function / Program libraries, e.g.

- statistical analyses

- random number generators

•

Coupling of monolithic models, e.g.

- federation of models (DIS, HLA, . . . )

- agent-based simulation

Conclusion: Missing

comprehensive

formal &

(24)

4. M&S-Verification, Validation &

Accreditation (VV&A)

Terminology

6

Model

validation:

process of demonstrating that a model and its behavior a

suitable representations

of the real system

and its behavior

w.r.t.

intended purpose

of model application.

6

Model

verification:

process of demonstrating that

a model is

correctly represented

and was

transformed correctly

from one representation

form into another,

w.r.t.

transformation and representation rules

,

requirements, and constraints.

(25)