Forecasting the Demand for Military Network Services 26 ISMOR September 2009 David Frankis.
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Transcript of Forecasting the Demand for Military Network Services 26 ISMOR September 2009 David Frankis.
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