Addicted to speed: Why broadband service providers need a ‘healthier lifestyle’
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Transcript of Addicted to speed: Why broadband service providers need a ‘healthier lifestyle’
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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.
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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!
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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
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15-25 YEARS AHEAD
We can foresee many likely
future demands on broadband
networks
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TODAY
15-25 YEARS AHEAD
Meeting these requirements is influenced by what we do…
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TODAY
15-25 YEARS AHEAD We are unknowingly storing up some big
problems!
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This may be a difficult message to hear
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You need to change your ‘lifestyle’…
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…and adopt a ‘healthier’ alternative
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But why…
…do I need to change my lifestyle?
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Key messages
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Problem The pursuit of ever
more speed has put the broadband
business in a vicious circle.
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Why so? Speed (‘bandwidth’) is
no longer a helpful model for broadband.
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So what? You need to
change your model to survive and
prosper.
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Problem
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More, more, more More
supply
Great! A “faster”
network!
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More, more, more
More elastic demand
But demand automatically
expands to use resources
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More, more, more
Faster saturation of infrastructure
This creates a “jackhammer”
effect
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More, more, more
More variability
Applications need consistency of loss
and delay
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More, more, more
Lower QoE
When they experience rapidly varying loss and
delay, you get…
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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
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More, more, more
More cost
Churn is expensive, so you have to restore QoE.
How?
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More, more, more More
supply
And round we go again!
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More supply
More elastic demand
Faster saturation of infrastructure
More variability
Lower QoE
More complaints and churn
More cost
The technical vicious circle
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The investment ‘cycle of doom’ Se
rvic
e
qu
alit
y U
nd
ep
reci
ate
d
asse
t va
lue
($$$)
($)
Let’s look at how QoE and operator debt change over time
TIME
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Serv
ice
Qu
alit
y U
nd
epre
ciat
ed A
sset
Val
ue
The investment ‘cycle of doom’ Se
rvic
e
qu
alit
y U
nd
ep
reci
ate
d
asse
t va
lue
($)
($$$)
As you add users to an empty network, QoE
declines
Those users help you to pay down the debt used to
fund the network
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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
($)
($$$)
QoE falls faster than simplistic bandwidth models suggest and
churn rises
You need to upgrade earlier than your
capacity planning and financial models
predicted
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Serv
ice
Qu
alit
y U
nd
epre
ciat
ed A
sset
Val
ue
($$$)
($)
The investment ‘cycle of doom’
Rising load makes service quality fall, forcing repeated upgrades
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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
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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
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Un
dep
reci
ated
Ass
et V
alu
e
The end result?
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Un
dep
reci
ated
Ass
et V
alu
e
Death via unserviceable
debt load
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Why so?
What drives the vicious circle?
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Cosmic Ludic Ecological
Constraints on everything
We live in a finite universe where we can’t get everything we might want
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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)
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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’
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Ecological
Ecological constraints
There are constraints of human nature, law,
technology availability, standards, etc.
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Cosmic Ludic Ecological
Speed of light
Statistical multiplexing
Pricing policy
Broadband drug: stat mux gain
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Why trust in increasing speed is now misplaced
This may be a difficult message to hear. We did warn you!
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Why trust in increasing speed is now misplaced
Pac
ket
de
lay
Let’s consider the delay a packet experiences…
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Why trust in increasing speed is now misplaced
Pre-IP Early IP Now
…and see how that changes over time
Pack
et d
elay
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Cosmic constraint
Pre-IP Early IP Now
Pack
et d
elay
How did this constraint change?
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Pre-IP Early IP Now
Geography
Pack
et d
elay
Cosmic constraint
Fixed overhead: Speed of light, packet routing
lookups
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Pre-IP Early IP Now
The speed of light is not changing
Pack
et d
elay
Geography
Cosmic constraint
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Pre-IP Early IP Now
Pack
et d
elay
Ecological constraint
How did this constraint change?
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Pre-IP Early IP Now
Serialisation speed
Pack
et d
elay
Ecological constraint
How quickly can we squirt the packet over a link?
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Pre-IP Early IP Now
Pack
et d
elay
Ecological constraint Historically speed did
correlate with more value
Serialisation speed
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Pre-IP Early IP Now
Pack
et d
elay
Ludic constraint
How did this constraint change?
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Variability
Pre-IP Early IP Now
Pack
et d
elay
Ludic constraint Delay due to other
packets in the system
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Pre-IP Early IP Now
Pack
et d
elay
Ludic constraint
Variability
Now dominates application performance
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G, S and V
G
S
V Variability
Serialisation speed
Geography
The outliers are what kill application performance, and they are growing
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Shifting constraints
G
S
V
Ecological
Cosmic
Once we had digital networks, the key constraint was ecological
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Shifting constraints
G
S
V
Ludic Ecological
Cosmic
It is now ludic, but mainstream network engineering & regulatory
policy has yet to reflect this
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Networks are…
trading spaces
…principally for V,
in a statistical ‘game of chance’
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How ‘V’ is distributed among competing streams
is how demand is matched to the supply
Fact
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“Magical” thinking
Problem
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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
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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
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So what?
Is there a better approach?
Can the cycle be broken?
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What has to change?
NOW FUTURE
MORE BANDWIDTH
Selling peak speed
and commodity inputs
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What has to change?
NOW FUTURE
MORE BANDWIDTH
Selling peak speed
and commodity inputs
BETTER SCHEDULING
Selling QoE & differentiated
application outcomes
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(Simplified) structure of broadband demand
Bulk
Interactive
Real-time
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(Simplified) structure of broadband supply
Bulk
Interactive Single
class of service
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Today’s economic model
Real-time
Bulk
Interactive
Real-time
Too quality-sensitive
Too cost-sensitive
COST REVENUE
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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
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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
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Example of a possible alternative supply approach
Superior traffic costs more to deliver… so should attract a premium
Economy
Standard
Superior
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Example of a possible alternative supply approach
Standard traffic is today’s off-peak Internet… but is consistently the same
Economy
Standard
Superior
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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
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Future rational economic model
COST REVENUE
Economy
Standard
Superior
Economy
Standard
Superior
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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
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How to reach health and prosperity?
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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.
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YES! 1. Measure QoE
– on customer-centric basis.
2. Increase utilisation via scheduling
– to make a profit.
3. Plan capacity
– based on QoE effects.
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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.