Addicted to speed: Why broadband service providers
need a ‘healthier lifestyle’
CommunicAsia 2014 Singapore, 17th June 2014
PREDICTABLE
NETWORK
SOLUTIONS
© 2014 All Rights Reserved
Modified version for Web upload. Same content, different format.
The only network performance science
company in the world.
PREDICTABLE
NETWORK
SOLUTIONS
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.
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