Addicted to speed: Why broadband service providers

need a ‘healthier lifestyle’

CommunicAsia 2014 Singapore, 17th June 2014

PREDICTABLE

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SOLUTIONS

© 2014 All Rights Reserved

Modified version for Web upload. Same content, different format.

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company in the world.

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If we are wrong then please tell us, as it’s a bit lonely sometimes!

Dr Neil Davies Co-founder and Chief Scientist Computer Scientist, Mathematician and Engineer (but not a Futurologist)

Sustainability of ICT

The expertise I am sharing

here

15-25 YEARS AHEAD

We can foresee many likely

future demands on broadband

networks

TODAY

15-25 YEARS AHEAD

Meeting these requirements is influenced by what we do…

TODAY

15-25 YEARS AHEAD We are unknowingly storing up some big

problems!

This may be a difficult message to hear

You need to change your ‘lifestyle’…

…and adopt a ‘healthier’ alternative

But why…

…do I need to change my lifestyle?

Key messages

Problem The pursuit of ever

more speed has put the broadband

business in a vicious circle.

Why so? Speed (‘bandwidth’) is

no longer a helpful model for broadband.

So what? You need to

change your model to survive and

prosper.

Problem

More, more, more More

supply

Great! A “faster”

network!

More, more, more

More elastic demand

But demand automatically

expands to use resources

More, more, more

Faster saturation of infrastructure

This creates a “jackhammer”

effect

More, more, more

More variability

Applications need consistency of loss

and delay

More, more, more

Lower QoE

When they experience rapidly varying loss and

delay, you get…

More, more, more

More complaints and churn

In competitive markets that

drives…

In other markets the regulator gets the flack and comes under pressure to act

More, more, more

More cost

Churn is expensive, so you have to restore QoE.

How?

More, more, more More

supply

And round we go again!

More supply

More elastic demand

Faster saturation of infrastructure

More variability

Lower QoE

More complaints and churn

More cost

The technical vicious circle

The investment ‘cycle of doom’ Se

rvic

e

qu

alit

y U

nd

ep

reci

ate

d

asse

t va

lue

($$$)

($)

Let’s look at how QoE and operator debt change over time

TIME

Serv

ice

Qu

alit

y U

nd

epre

ciat

ed A

sset

Val

ue

The investment ‘cycle of doom’ Se

rvic

e

qu

alit

y U

nd

ep

reci

ate

d

asse

t va

lue

($)

($$$)

As you add users to an empty network, QoE

declines

Those users help you to pay down the debt used to

fund the network

Serv

ice

Qu

alit

y U

nd

epre

ciat

ed A

sset

Val

ue

The investment ‘cycle of doom’ Se

rvic

e

qu

alit

y U

nd

ep

reci

ate

d

asse

t va

lue

($)

($$$)

QoE falls faster than simplistic bandwidth models suggest and

churn rises

You need to upgrade earlier than your

capacity planning and financial models

predicted

Serv

ice

Qu

alit

y U

nd

epre

ciat

ed A

sset

Val

ue

($$$)

($)

The investment ‘cycle of doom’

Rising load makes service quality fall, forcing repeated upgrades

Serv

ice

Qu

alit

y U

nd

epre

ciat

ed A

sset

Val

ue

($$$)

($)

The investment ‘cycle of doom’

The period between upgrades falls due to decreasing effectiveness of capacity upgrades

to resolve the QoE issue

Serv

ice

Qu

alit

y U

nd

epre

ciat

ed A

sset

Val

ue

The investment ‘cycle of doom’

Failure to keep up with ever-rising demand forces ever-shorter upgrade cycles

Un

dep

reci

ated

Ass

et V

alu

e

The end result?

Un

dep

reci

ated

Ass

et V

alu

e

Death via unserviceable

debt load

Why so?

What drives the vicious circle?

Cosmic Ludic Ecological

Constraints on everything

We live in a finite universe where we can’t get everything we might want

Cosmic

Cosmic constraints

Physics limits us in many ways: not just the speed of light, but also energy

conservation, or how much information we

can encode on a channel (Shannon limits)

Ludic

Ludic constraints

“Ludic” constraints are “games”, with mathematical rules and limits. Chess can be mathematically modelled, for

example

Broadband is like a statistical ‘game of chance’

Ecological

Ecological constraints

There are constraints of human nature, law,

technology availability, standards, etc.

Cosmic Ludic Ecological

Speed of light

Statistical multiplexing

Pricing policy

Broadband drug: stat mux gain

Why trust in increasing speed is now misplaced

This may be a difficult message to hear. We did warn you!

Why trust in increasing speed is now misplaced

Pac

ket

de

lay

Let’s consider the delay a packet experiences…

Why trust in increasing speed is now misplaced

Pre-IP Early IP Now

…and see how that changes over time

Pack

et d

elay

Cosmic constraint

Pre-IP Early IP Now

Pack

et d

elay

How did this constraint change?

Pre-IP Early IP Now

Geography

Pack

et d

elay

Cosmic constraint

Fixed overhead: Speed of light, packet routing

lookups

Pre-IP Early IP Now

The speed of light is not changing

Pack

et d

elay

Geography

Cosmic constraint

Pre-IP Early IP Now

Pack

et d

elay

Ecological constraint

How did this constraint change?

Pre-IP Early IP Now

Serialisation speed

Pack

et d

elay

Ecological constraint

How quickly can we squirt the packet over a link?

Pre-IP Early IP Now

Pack

et d

elay

Ecological constraint Historically speed did

correlate with more value

Serialisation speed

Pre-IP Early IP Now

Pack

et d

elay

Ludic constraint

How did this constraint change?

Variability

Pre-IP Early IP Now

Pack

et d

elay

Ludic constraint Delay due to other

packets in the system

Pre-IP Early IP Now

Pack

et d

elay

Ludic constraint

Variability

Now dominates application performance

G, S and V

G

S

V Variability

Serialisation speed

Geography

The outliers are what kill application performance, and they are growing

Shifting constraints

G

S

V

Ecological

Cosmic

Once we had digital networks, the key constraint was ecological

Shifting constraints

G

S

V

Ludic Ecological

Cosmic

It is now ludic, but mainstream network engineering & regulatory

policy has yet to reflect this

Networks are…

trading spaces

…principally for V,

in a statistical ‘game of chance’

How ‘V’ is distributed among competing streams

is how demand is matched to the supply

Fact

“Magical” thinking

Problem

When there is excessive delay, people are trying to make V disappear by building more capacity rather than distributing it

through scheduling

Problem

Attempting to solve scheduling problems using capacity is inefficient and ineffective

Result: telecoms is a capital killer

Source: PwC http://www.pwc.com/en_GX/gx/communications/publications/assets/pwc_capex_final_21may12.pdf

It’s not getting any better since

then

So what?

Is there a better approach?

Can the cycle be broken?

What has to change?

NOW FUTURE

MORE BANDWIDTH

Selling peak speed

and commodity inputs

What has to change?

NOW FUTURE

MORE BANDWIDTH

Selling peak speed

and commodity inputs

BETTER SCHEDULING

Selling QoE & differentiated

application outcomes

(Simplified) structure of broadband demand

Bulk

Interactive

Real-time

(Simplified) structure of broadband supply

Bulk

Interactive Single

class of service

Today’s economic model

Real-time

Bulk

Interactive

Real-time

Too quality-sensitive

Too cost-sensitive

COST REVENUE

Today’s economic model

Real-time

Bulk

Interactive

Real-time

COST REVENUE

Everything carries the high costs of real-time, but the revenues

don’t match that cost structure

Example of a possible alternative supply approach

Economy

Standard

Superior This three-class “polyservice” model is specially constructed. It

should not be confused with existing “priority QoS”

mechanisms

Example of a possible alternative supply approach

Superior traffic costs more to deliver… so should attract a premium

Economy

Standard

Superior

Example of a possible alternative supply approach

Standard traffic is today’s off-peak Internet… but is consistently the same

Economy

Standard

Superior

Example of a possible alternative supply approach

Economy traffic does not drive capacity upgrades

Economy

Standard

Superior

It is also unsuitable for real-time applications

Future rational economic model

COST REVENUE

Economy

Standard

Superior

Economy

Standard

Superior

Five class model incorporates resilience

Sup

erio

r St

and

ard

Su

perio

r Stan

dard

Economy

Sup

erio

r St

and

ard

Sup

erior

Stand

ard

Economy

Sup

erio

r St

and

ard

Sup

erior

Stand

ard

Economy

Drives capacity planning

(primary service)

COST

Drives resilience & redundancy

capacity planning

COST

Drives

REVENUE

How to reach health and prosperity?

NO! 1. Firefighting

– due to rapid QoE declines.

2. Panic buying

– of capacity to deal with QoE crises.

3. Complaining!

– There's loads of slack.

YES! 1. Measure QoE

– on customer-centric basis.

2. Increase utilisation via scheduling

– to make a profit.

3. Plan capacity

– based on QoE effects.

The Prize

10%?

30%?

>100%?

Improvement in QoE from assets

Spectacular cost and QoE improvements are possible

when the best available mathematics is applied to

scheduling problems.

Dr Neil Davies Neil.Davies@pnsol.com Tel: +44 (0)3333 407715

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NETWORK

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Thank you