Forecasting the Demand for Military Network Services

26 ISMOR September 2009

David Frankis

Acknowledgement

Atkins wishes to acknowledge the contributions of Chris Whittaker of D IIS

Structure

• The Problem

• Approach

• Findings

The Problem

Future Core Network

• Network infrastructure from 2012 for MoD and Armed Forces

• Usually defined in terms of equivalent current capabilities (contracts):– Fixed telephone network (DFTS)– Satellite network (Skynet 5)– High Frequency network (DHFCS)– IT services (DII)– Electronic commerce services (DECS)

• Boundary with battlespace not always defined in detail

• Roughly £1bn p.a. over a thirty year life

• Service provision, not equipment

• The above contracts don’t terminate simultaneously, so a tranche-based acquisition is planned

Aim of Study

To inform the requirement and business case for FCN

with forecasts of demand for FCN services, through life.

Planning

Why understand demand?

• Cost

• Scaling

• Planning for acquisition programme

Scaling

Cost

Approach

Summary of Approach

• Long term look across the types of demand

• Not a detailed (and inevitably short term) IER study

• Examine possible sources of demand and how they may change, not equipment plans

• Broad framework developed using Atkins’s ACCLAIM conceptual framework

• Aim to express demand in terms of ‘large’, ‘medium’ and ‘small’ for each type of user– Can be refined where firm information is available

• Spreadsheet model developed to embody framework

• Model used to develop quantitative demand estimates

ACCLAIM

a

bc

a

bc

Level 1 parameters

Level 2 parameters

Level 3 parameters

Level 4 parametersDetailed Models

Abstract Models

Implemented in VB

Overview of Demand

Demand Drivers

E.g. Manpower will be increasingly constrained

Business functions

E.g. Logistic services:Stock monitoring and supply

User services

E.g. Voice

Network and

application services

E.g. Packet addressing

Requires

Supports

Time

Identification of Demand Types

User Services

Office Services

Specialist Military

Welfare

ISTAR

MedicalLogs

UAV control

Doc edit

Videocon

Etc.

Videocon Monitoring

Contact home Etc.

Metrics defined and estimated at this level

CBM

Cross-checked with J1 – J9 requirements

Interactions

Generic Demand Taxonomy

Office services

Human-machineHuman-human Machine-machine

Synchronous Asynchronous

Aim for user, not technology, focusUse to check completeness

Quantification of Demand

Service taxonomy

User taxonomy

M

Specific service type,

e.g. text

Service metric, e.g. message size.

Service may have more than one

metric.

UK-based civilians are estimated to send Medium size text messages

Specific user type, e.g. UK-based civilian

Identification of Demand Drivers

• PESTLE analysis

• Political, e.g. willingness to go to war;

• Economic, e.g. increasing unit cost of weapon systems;

• Social, e.g. rise of the ‘X-box generation’;

• Technological, e.g. miniaturisation;

• Legal, e.g. emphasis of freedom of information;

• Environmental, e.g. impact of carbon accounting.

Representation of Demand Drivers

Drivers

Service metricValues

User numbers

User Behaviour

Time Demand

Growth Functions

• Step– New service, e.g. streaming video in theatre for welfare

• Linear between two points– Change from one state to another, e.g. move to home-working

• Exponential– Growth, e.g. sensor performance– Decline, e.g. overall numbers of personnel

Findings

Typical Results

0

0

0

1

10

100

1,000

10,000

100,000

1,000,000

10,000,000

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

Year

ISTAR

Office

Welfare

CBM

Logs

Medical

UAV Control

Note logarithmic y-axis

Calibration and Validation

• Baseline: consistency with today’s traffic– Today’s traffic not necessarily equal to today’s demand– Today’s measurements don’t necessarily align with scope of FCN

• Future growth– Historical comparisons for future growth

• Growth per user (Moore’s Law, Nielsen’s Law, etc.) • Change in the user base

– Some open source historic data, e.g., US CENTCOM comparison – approx 33% pa since 1991

• Imprecise user base so hard to apply

Sensitivity Analysis: Impact on Growth Rates

• Intelligence/ISTAR– Impact of centralisation of staffs

• Logistics– Predictive maintenance (e.g. HUMS)

• Welfare– Breadth of services offered to personnel

Key Demand Drivers

• Technology– Higher performing IT used because it can be– Drives expectations of tools to be used in the office and battlespace

• Miniaturisation – Proliferation of ISTAR information sources and sinks in the battlespace

• Availability of highly trained personnel– Shortage forces MoD to centralise, supported by IT– Applies to intelligence analysts, medical staff

• ISTAR grows fastest because of ‘double whammy’– Increasing sensor performance– Increasing numbers of (small) platforms

Limits to Growth: Technology

• Key assumption: in the battlespace, demand will expand to fill what the technology can provide

– Extrapolation from the past implies large and rapid growth

• Moore’s Law won’t go on forever– Estimates of the date when fundamental limits are reached range from ten to

600 years hence

• Limits to bearer capacity– For fixed systems, unlikely to be a bearer issue in FCN timescale– For EM-based systems, there are spectrum limits

Limits to Growth: Users

• Human ability to receive input – eyes, ears…– In the medium term (i.e. within FCN timescale), assumed to limit welfare

demands, most office applications, and medical– Assumed to apply to personnel in battlespace (i.e. no direct computer-brain

communication in the life of FCN)– Not assumed to apply to ISTAR, because automated analysis tools may keep

pace• Workplace de facto standardisation

– Employment market means one office employer much like another– Cost, productivity per worker about the same across employers– MoD will be able to cope with advances in office technology (or nobody will)

• Related networks– The high volume tactical information comes from non-FCN networks– Historically these are lower capacity– Need for balanced investment

MoD Ability to Influence Growth• Full MoD control

• What is allowed in theatre – affects welfare• Partial MoD control

• Operations undertaken – affects scale, ISTAR, Logs, Medical• Own technology – affects all services• How logistics is done – not necessary to report all HUMS data• Working practices

– Policy on centralisation of intelligence staffs– Home versus office-based working,

• Manpower – affects overall scale• Outside MoD Control

• Cultural expectations – affects welfare, office IT• Threat technology – affects CBM, ISTAR

Summary of Findings

• Areas of high demand and growth– ISTAR– To lesser extent, welfare, office

• Key external drivers– Technology in the battlespace– Technology in the home culture

• MoD choices that affect demand– Centralisation of intelligence– Level of provision for welfare

Observations on the Approach

• ACCLAIM was very helpful for getting a framework in place and developing strawman estimates

– And provides a natural route to investigate if proposed solutions meet demand

• Hard to get good calibration and validation data

– There’s always a reason why it isn’t quite right for the job

– But study momentum is more important than being right

• The numbers are probably wrong

– But they help focus the argument

• Important to distinguish between unconstrained demand and constrained demand

– Historically, demand has been defined by expectations of supply (not just for network services): this feedback loop colours the whole study

• Prediction is hard

– Especially about the future, but even about the present

Questions