Boyd, Metcalfe and Amdahl - Modelling Networked Warfighting Systems - - PowerPoint PPT Presentation

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• Computer Science & Software Engineering

http://www.csse.monash.edu.au/

Boyd, Metcalfe and Amdahl - Modelling Networked Warfighting Systems

Carlo Kopp, BE(Hons), MSc, PhD MIEEE, MAIAA, PEng

Monash University, Clayton, Australia email: carlo@mail.csse.monash.edu.au

c 2004, Monash University, Australia

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• Defining the Problem

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• How to Quantify NCW Capability Gains?
• Networked system ‘capability gain’ remains a contentious issue.
• NCW advocates invoke Metcalfe’s Law and point to square law

gains.

• NCW critics argue that the number of engagements effected is the

measure of system capability.

• NCW trials and experiments do indicate measurable capability gains.
• How do these capability gains arise?
• How do we quantify these capability gains?
• How do we maximise these capability gains?
• How do we minimise an opponent’s capability gains?

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• NCW - Counter-Air Environment

Tanker Link 16 Relay Platform

Wedgetail AEW&C Combat Air Patrol Combat Air Patrol NCW Example RAAF Defensive Counter Air

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• NCW - Strike Environment

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• NCW - Strike Environment

KILLBOX INTERDICTION PERSISTENT BOMBARDMENT/

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• Quantifying Capability Gains

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• NCW vs Boyd’s OODA Loop
• Boyd’s Observation-Orientation-Decision-Action loop presents an ab-

straction to represent the event loop in an engagement.

• Vast empirical evidence to support Boyd model - also applicable to

biological ‘predator-prey’ interactions.

• Players in the event loop Observe environment, Orient themselves

to the situation by forming a model, Decide upon a course of action, and execute that Action.

• Intelligence Surveillance Reconnaissance (ISR) sensors and systems

collect information and a network distributes that information.

• Networking accelerates OODA loops by accelerating the Observation-

Orientation phases and improving situational awareness.

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• NCW - A Networked Fabric

A NETWORKED ‘FABRIC’

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• The Incompleteness Problem
• Representing capability gains using OO phases of OODA loop puts

focus on information domain gains.

• Real world systems combine information domain and kinetic domain

elements.

• Using only information domain elements neglects constraining sys-

tem behaviours imposed by kinetic domain elements.

• The result can be highly optimistic and unrealistic conclusions about

achievable capability gains.

• Representative modelling of complete system capability gains re-

quires a complete model which can encompass both the OO and DA phases of the Boyd OODA loop.

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• Metcalfe’s Law
• Metcalfe’s Law asserts that the usefulness or utility of a network

increases with the square of the number of nodes in the network.

• Empirically demonstrated on the WWW by correlating gains in sales

revenue against number of nodes connected to the network.

• Metcalfe’s Law is not a predictor of achieved utility, but rather an

indicator of achievable utility.

• ‘Utility’ is seen in terms of connectivity.
• Widely cited as a measure of capability gain in networked warfighting

systems.

• Metcalfe’s Law contains no implicit mechanism to quantify time

domain behaviour in the system.

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• Metcalfe’s Law

Number of Possible Connections [−] Number of Network Nodes [−] 500 600 400 300 200 100 700 800 900 1000 Metcalfe’s Law 100000 200000 300000 400000 500000 600000 700000 800000 900000 1e+06 Metcalfe’s Law

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• Metcalfe’s Law - Time Domain

NK DELAY SNS/PROC NK DELAY ISR ISR ISR SNS/PROC NK OBSERVATION PHASE FUNCTIONAL USER TEMPORAL INTERPRET/ MODEL ORIENTATION PHASE DB NETWORK COLLATE MODEL VALIDATE SNS/PROC NK DELAY SNS/PROC NK SNS/PROC

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• Metcalfe’s Law Limitations
• Implicit assumption that gains in connectivity produce gains in time

domain performance.

• Complex time domain dependencies in ISR system and network be-

haviour not accounted for.

• Network saturation and load effects not accounted for.
• Effects of hostile jamming not accounted for.
• Metcalfe’s Law at best a useful predictor of bounds on capability

gain.

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• Kinetic Domain - Decision-Action
• Completeness in modelling capability gains requires a kinetic do-

main model which can encompass the Decision-Action phases of the OODA loop.

• Establish what bounds exist on the number of engagements the sys-

tem can produce within a defined time, with some bounded number

• f elements.
• Decision processes involve delays since decision-makers often depen-

dent on inputs from superiors and subordinates, introducing queue- ing behaviours into the system.

• Executing Actions involves sequences of events such as positioning

a platform for an engagement, also introducing queueing behaviours into the system.

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• Kinetic Domain Constraint Example

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• Kinetic Domain - Decision-Action
• In practical terms the system at the Decision-Action level involves

complex mixes of sequential / serial / queueing behaviours, and some parallel behaviours.

• How do we best model a complex mix of serial and parallel func-

tions?

• Answer: exploit Amdahl’s Law used in supercomput-

ing.

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• Amdahl’s Law
• Amdahl’s Law asserts that a large system of ‘processors’ working

in parallel to solve a single problem can never achieve aggregate performance equal to the sum of the achievable performance of each and every individual processor in that system. The idealised ‘linear speedup’ in problem solving cannot be achieved for any real world problem, or: Speedup = (s + p ) / (s + p / N ) = 1 / (s + p / N )

• where s and p are the serial and parallel time fractions.
• In Amdahl’s Law, the nature of the workload imposes constraints
• n behaviour, regardless of the number of elements in the system

performing work. In networked warfighting systems, we thus treat entities performing work as processors in a complex serial / parallel system, executing tasks.

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• Amdahl’s Law

1.31% Serial Fraction 1.7% Serial Fraction 15 25 30 35 40 45 50 Linear (Ideal) Speedup 45 40 35 30 25 Number of CPUs [−] 20 15 10 5 Speedup [−] Amdahl’s Law 5 10 20

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• Boyd vs Metcalf vs Amdahl

D A O O

OBSERVATION ORIENTATION DECISION ACTION Amdahl’s Law Metcalfe’s Law

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• Decision Action Capability Gains
• Capability of the system in the Decision-Action phases reduces with

the increasing number of ‘serial chains’ within the system.

• Capability of the system in the Decision-Action phases increases with

the increasing level of parallelism in the system.

• Complex sequential decision processes thus impair capability regard-

less of networking capability.

• Maximising the number of platforms, maximising concurrent engage-

ments per platform and maximising platform persistence in proximity to targets maximises parallelism and thus capability.

• Empirical experience supports conclusions derived from Amdahl’s

Law.

• Metcalfe and Amdahl models are complementary, not exclusive.

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• Maximising Capability Gains

O A D O

ACTION OBSERVATION ‘MAXIMISE CONNECTIVITY’ ‘MINIMISE SERIAL CHAINS’ ‘MAXIMISE SENSOR CAPABILITY’ DECISION ORIENTATION ‘MAXIMISE PARALLELISM’

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• Minimising an Opponent’s Gains
• Given two mutually opposed networked systems, maximising own

capability requires:

• 1. Maximising number of ISR elements
• 2. Maximising connectivity (and link capacity)
• 3. Maximising parallelism
• 4. Minimising serialism
• Minimising the opponent’s capability requires:
• 1. Reducing the opponent’s number of ISR elements
• 2. Minimising the opponent’s connectivity (and link capacity)

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• Minimising Opposing ISR Capability

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• Minimising Opposing Connectivity

Distance to Datalink Transmitter Distance to Jammer

Su−30/32 Electronic Attack Role (2−5 x High Power Jammer Pods)

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• Minimising Opposing Connectivity

• K
• p

THE DATALINK AND ISR JAMMING PROBLEM

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• Conclusions
• Amdahl’s Law provides a valuable abstraction for modelling the im-

pact of the Decision-Action phases of the OODA loop on system capability gains.

• Amdahl’s Law complements Metcalfe’s Law by providing for a com-

plete abstraction to model OODA loop behaviour.

• Amdahl’s Law presents a model which relates achievable numbers
• f engagements to time.
• Metcalfe’s Law, conversely, presents capability gains indirectly, as it

measures utility in terms of connectivity.

• Fusion of Boyd, Metcalfe and Amdahl provides an intellectual frame-

work for understanding capability gains in networked warfighting sys- tems.

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• End Presentation

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• Revision Information

This document is currently at revision level: $Id: CSE-3141-2004.tex,v 1.1 2004/02/06 12:34:52 carlo Exp carlo $