The Properties and Mathematics of Data Transport Quality

Delivering “Quality”
Quality Attenuation
Exploiting the Understanding
The Properties and Mathematics of
Data Transport Quality
A Brief Introduction to ’Quality’ in Data Networks; its Interaction
with End User Experience, its Conservation, Propagation, and
how it can be Traded, Costed and Managed.
Neil Davies
Predictable Network Solutions Limited
neil.davies@pnsol.com
Ofcom, Riverside House
5th February 2009
Neil Davies Properties of Data Transport Quality – an Introduction(c) 2009 Predictable Network Solutions Ltd

Quality Attenuation
Outline
1 Delivering “Quality”
Layered Viewpoint
“Would you Like Quality with that, Sir?”
Relationship with End User Experience
2 Quality Attenuation
Fundamental Properties
Representation and Measurement
Compositional Properties
3 Exploiting the Understanding
Applying it to the Application(s)
Applying it to the Network(s)
Applying it to the Economics

Quality Attenuation
Layered Viewpoint
Outline
Layered Viewpoint

Quality Attenuation
Layered Viewpoint
Delivering Quality
Layered View
For an end-user to achieve a certain quality of experience, an
application interacts (with a server or another application) across
the network.
For any particular application, the quality the user experiences will
depend on how quickly the application can interact (with the
remote peer) across the network.

Quality Attenuation
Layered Viewpoint
Delivering Quality
Not Just Quantity – Some Frequently Asked Questions:
1 Doesn’t it depend on the specific application? Yes and no.
Badly designed or written applications can make things worse,
however the delivered end-to-end quality is now typically
dominating the delivered quality of experience.
2 Isn’t more bandwidth more quality? No.
It doesn’t matter how much bandwidth you deliver, if the delay
is large (or rapidly varying) enough or the loss rate is high
enough then the application will fail.
3 So why do people keep on talking about adding bandwidth as
the answer? Adding more resources may resolve some issues
under limited circumstances. We’ll return to this point later.
Any given application’s effectiveness depends on end-to-end quality
being available in sufficient quantity – no more, no less.

Quality Attenuation
Layered Viewpoint
The ’Just Add Quality’ Myth
What has Silence, Cold, Dark and Quality all got in common?
You can no more ’add quality’ to a network than you can ’add
silence’ to a noisy room.
Just as silence is the absence of noise, what is colloquially
called ’quality’ in data networking is really the absence of
something.
Every network element attenuates the quality - introduces
delay and (the potential for) loss – every transmission line,
switch, router etc.
People may talk about quality and even desire it, but quality
attenuation is the physical property we have to work with.
This is a key concept – having introduced quality attenuation we
can start re-framing the issues in a coherent framework.

Quality Attenuation
Layered Viewpoint
Delivering Quality ≡ Bounding Quality Attenuation
Introduction to Properties of Quality Attenuation (∆Q)
In data networks, ’quality of service’ is achieved when the delivered
quality attenuation, over the end-to-end path, is suitably bounded.
We use the concept of quality attenuation so frequently that we
refer to it as ’∆Q’ - think of it as the change in quality.
This inevitable ∆Q comes in two forms: immutable – ﬁxed by
physics, and mutable which can be managed and traded.

Quality Attenuation
Layered Viewpoint
Examples – 1
What Bounded Quality Attenuation Delivers
You want to assure some
average performance for
typical (10kb) HTTP
web page access – What
∆Q should you aspire
to deliver?
What is the
dependency on the
one-way delay?
What is the
dependency on the
loss rate?
The dependency is on
both delay and loss,
not delay or loss

Quality Attenuation
Layered Viewpoint
Examples – 2
Applies to Real-time Services As Well
You want to assure some
perceived quality for a
G.711 VoIP call – what
∆Q should you aspire
to deliver?
What is the
dependency on the
one-way delay?
What is the
dependency on the
loss rate?
The dependency is on
both delay and loss,
not delay or loss

Quality Attenuation
Outline
Layered Viewpoint

Quality Attenuation
Quality Attenuation
Properties of ∆Q
It is Conserved
it only every increases, and can’t be ’destroyed’;
you can’t ’un-delay’ packets or ’un-loose’ them.
hence is monotonically increasing – ’adds’, but not by simple
arithmetic.
Manifests itself in two different ways:
1 ∆Q associated with the data transport for a single user or
application instance — an application’s viewpoint.
2 ∆Q associated with a network element (for example a
switch/router where multiplexing occurs) applying to all the
streams of data that are flowing through that point — a
network operations viewpoint.
The total ∆Q at that network element is still conserved - but
can be ’traded’ through differential allocation amongst the
individual data streams.

Quality Attenuation
Representing Quality Attenuation – 1
Observing and Predicting Outcomes
From a performance / ∆Q point of view; the interest is in outcomes.
If event A should lead to B
occurring, the measure is:
how frequently B
actually occurs
the time interval
between B and A.
We can express both the
aspiration (here 50% out-
comes occurring within 3s,
95% within 10s - the blue
stepped line) and what was
delivered (the black line).
As the delivered curve is always to the left and above the aspiration curve —
the aspiration was met and ’quality’ delivered.

Quality Attenuation
Focusing on the ’Tail’
It is the tail of distributions that is of most interest1
These graphs represent the same outcome: 50% delivered with
54ms; 90% within 82ms; 99% within 91ms; with 0.5% packet loss.
1
Maths Note: This a Cumulative Distribution Function (CDF), technically
they are improper CDFs as P → 1 as t → ∞.

Quality Attenuation
Comparing What is Actually Delivered
Compare the previous
slide’s delivered ∆Q:
50% within 54ms;
90% within 82ms;
99% within 91ms;
105ms - 0.5%
loss.
with this graph.
50% within 43ms;
90% within 60ms;
95% within 70ms;
2000ms - 2% loss.
Same ISP, same applica-
tion, same two end points,
just diﬀerent time of day.

Quality Attenuation
Quality Attenuation as the Representation Measure
Focusing on the quality attenuation (∆Q) – especially when the
upper bound ( ∆Q ) an application can tolerate is known – is the
key.
Importantly, All application requirements can be reduced to
this form – it says:
“Within this quality attenuation from A to B
deliver to me this (minimum) rate”
We have a ’budget’ ( ∆Q ) to work within!
How can this budget be divided? How is it allocated across
the network elements on the end to end path? What is a
reasonable expectation on, for example, the access network?

Quality Attenuation
Quality Attenuation Budgets
How Aspects of the End to End Path Contribute
The contribution of any network element can be broken down into
three components:
G - dependent on geographical and other fixed factors.
S - dependent on the packet size and the transmission media.
V - the variability; dependent on many factors, see below.
1 G is a constant for a given path, incorporates factors such propagation
delay and residual error rates for transmission media. It is immutable.
2 S is fixed for a given packet size over a particular path (given that path is
fixed) - it captures the delay of processing packets. It is immutable.
3 V is the effects of the rest of the network on this traffic - this is mutable
and often highly variable - it is this component that requires management.
These ’sum’ (convolve) component-wise for each network element
traversed.

Quality Attenuation
Composing ∆Q
If it helps you can think of:
G as being the time for a packet of zero length to get from A
to B (a packet that pays no serialisation/de-serialisation
overhead but has to gain access to the transmission medium).
ADSL that would be [0–1.5ms]2
(256k) + propagation time;
UMTS that would be [0–10ms] + propagation time.
S being the time to transmit a packet of a given size, this is
dependent on packet size and the level-2 networking
technology overheads (e.g. quantisation for ATM, frame
transmission time in wireless) and incorporates any time that it
takes the transmission medium to become available for the
next packet/frame (inter-frame gap)
This gap is 0 for ADSL and UMTS but a ﬁxed 9.6µs for
10mbps Ethernet.
2
Uniform distribution between the bounds

Quality Attenuation
Measuring G, S and V
Beginning to See the Art of the Possible
Here we have taken a sam-
ple run and grouped the
times by packet size (in
this case the number of
ATM cells). From this we
can deduce:
G ≈ 8.2ms
S ≈ 2.3ms cell−1
V ∈ [0 · · · 20ms]
It is the magnitude of V
that determines the cus-
tomer experience
Both ends are 256/512k ADSL tails using IP Stream, one in BA6 the other in
CT2 going via an ISP based Telehouse North – the Central link was not being
used for anything else. There was ≈ 0.5% packet loss.

Quality Attenuation
Compositional Properties – Taking Stock
1 Got the tools to measure and analyse where ∆Q is accruing.
or alternately divide up a ∆Q budget and allocate to the
elements of the network
2 Know that high quality services are feasible (there can be a
reasonable bound on ∆Q in access networks)
3 Key to delivering quality is managing and controlling V
can’t eliminate V ; it comes with using statistical multiplexing
How do we ’tame’ V ?
Need to look a little more deeply into some other properties of
∆Q.

Quality Attenuation
Why ∆Q, not ’Delay and Loss’
Two Degrees of Freedom
Every queue has two degrees of freedom.
Fix two parameters and you’ve
fixed the third
Fix one parameter and you
establish a relationship between the
other two
Can’t choose any arbitrary three
values at will
For a fixed load, if you want to reduce
loss you have to increase delay; For a fixed
loss, as load increases delay must increase;
and so on

Quality Attenuation
That ’Question’
Why do People See Bandwidth as the Answer?
“So why do people keep on talking about adding bandwidth as the answer?”
1 Providers are not managing V , they are taking what ’emerges’ from the
day-to-day operation of their network.
We’ll come to what that means for the consumer and the operator shortly
2 The V they deliver to their customers is an arbitrary and un-manged
relationship between delay and loss
2◦ of freedom along with the offered load creeping up day by day
3 Their customers complain because their applications are not ’delivering sufficient
Quality of Experience’
The delay and loss, ∆Q, they are delivering to their customers is too high
(over the application’s implicit budget)
4 They increase the capacity of links/network to reduce the offered load
while the physics allows and they can afford it
5 Because of (1) they return to step (2) and iterate
Hence ISP — the Internet (in-)Solvency Problem

Quality Attenuation
2◦
of Freedom ⇒ Trading Space with Two Dimensions
Trading in Loss and Delay – ∆Q as partial order
This is how trading within a given ∆Q (or at least the V component) can be
visualised. Individual data streams can be given different loss and delay
characteristics, so that where contention for resources occur (which is where
queues are in network) the resulting ∆Q can be differentially distributed.
For example: traffic in B2 gets lower loss than traffic in C3, but equal delay,
lower delay than B3, but equal loss and both lower delay and loss than C3.

Quality Attenuation
Quality Trading in Data Links
More Properly: Quality Attenuation Trading
The properties described above have many interesting consequences on what is
possible, or more valuable, what is not possible with data networks.
One of the more interesting consequences is that any ’pipe’ (a path over
which data can be delivered within a bounded ∆Q) can carry multiple,
differentiated, data transport services even though the ’pipe’ itself doesn’t
support differentiation.
Alternately, given a multiple data streams the characteristics of the ’pipe’
needed can be calculated, so that the collected set of traffic can be
carried with that all their individual ∆Q constraints met.
This means that a ’differentiated service’ network can be built on top of an
existing ’single service’ network — if you understand the characteristics and
constraints properly.
This offers an incremental (hopefully lower cost) route to delivering
differentiated services. Which is useful as differentiated services are essential for
the long term economic viability of data networks

Quality Attenuation
Outline
Layered Viewpoint

Quality Attenuation
Delivering Quality
There is No Quality in Averages
Averages are dangerous.
People do not remember
’averages’ they remember
extremes.
So delivering quality
to users is about
making bad
experiences rare.
This is the graph of the
95% centile of time to
complete the same 10Kb
HTTP transfer presented
earlier.
This imposes more
stringent limitations
on ∆Q

Quality Attenuation
Establishing the Relationship Between QoE and ∆Q
So what is the quantity of quality that is needed to achieve some
task? There are three basic ways of establishing this:3
1 Emulate ∆Q: Connected the parts of the application together through a
suitable ’Network Degrader’
Expensive, tedious, can be diﬃcult to reproduce faults – however
should be part of any validation process
2 Simulate both the application and the network (simulating everything)
Expensive, often restricted by computation, supplied libraries (for
network protocols) often don’t behave the same way as real
implementations
3 Analytically. Mathematically model behaviour and ∆Q — solve
analytically or numerically
Cheaper, used to show feasibility and trends. Can be used to
formulate hypotheses to be tested by method (1) or (2).
3
Note for the unwary: most of the tools out there do not work properly -
they will introduce loss and delay, but not in the same way a real network will

Quality Attenuation
An Example
Loading Google’s Front Page
This is a DNS look up followed by small HTTP transfer, with
allowance for server response times.
Would have a median/75% centile/95% centile time to
complete of 0.73s, 0.76s and 0.81s; given the round trip time
was in the range 125ms to 200ms.
This rises to 2.33s, 2.69s, 3.17s if the round trip range was
125ms to 1000ms
The downstream rates need to support this ’quality’ vary from
34.3kbps to 10.8kpbs; the same amount of data over a longer time.
This shows there can be an advantage (to the provider) in
giving ’bad’ quality — it reduces the instantaneous oﬀered
load — conversely ’good’ quality can increase both the peak
oﬀered load and its variability.

Quality Attenuation
Nature of ’The Service’ in Current Networks
Specifically Access Networks
Current access networks only offer a single service – the service is not one that
they ’specified’, it is what ’emerges’ during operation. In this service an
application’s data traffic has:
1 No isolation from the effects of other traffic flowing to/from that end user
2 No isolation from the effects of other traffic flowing to/from other end
users (or even ISPs)
Which leads to:
1 People shutting down all their applications and disconnecting other
computers so that they can play an interactive game, make a VoIP call or
stream some video.
2 Really annoys people as there is nothing they can do about it.
Access network providers do try to do something about (2) — BT uniformly
shares bandwidth at their BRAS’s, ComCast buckets ’heavy’ users into a
constrained service class.
What consumers need is assured bounds on quality attenuation for some
portions of their traffic — then the application that is wish to use will deliver
what they require.

Quality Attenuation
UK is Well Positioned
Though More by Accident than Design (or Good Engineering Principles Win Through)
These comments are specifically about IP Stream. It is the only access network
with sufficient data about its design and operation in the public domain to be
able to draw reliable conclusions.
BT’s planning rule for capacity between a BRAS and a DSLAM is
that an end user should be able to achieve 2Mbps during the busy
period, 90% of the time4.
This is the specification of an outcome and, as you will now
know, there must be an associated delivered ∆Q .
The equivalent ∆Q corresponds to delivering 97%+ of
packets with a low delay variation (15ms to 20ms)
Thus, in the UK, over the national data infrastructure, we already
have ’pipes’ with sufficiently known, and good, properties into
which multiple differentiated services can be multiplexed.
4
the same planning rules have been proposed for 21CN

Quality Attenuation
Why Single Service Networks Are Bad News
Why to Remain Tenable Broadband Needs to Move to Multiple Services
1 Citizens, Consumers, Commerce and the Government want to
get more out of Broadband.
2 Many of the services people want will require stronger upper
bounds on quality attenuation.
for example video conferencing or highly interactive
applications.
3 But a single service network can, at best, only have a quality
attenuation bound — therein lies the problem.
No industry can afford to structure its business to deliver all its
services at the cost point only a few would be willing to pay.
Having data traffic with differing quality requirements is needed to
make optimal use of the infrastructure, with the savings that
implies.

Quality Attenuation
Differentiated Service Access Network
So, Who Gets to Decide Which Traffic Gets Treated Which Way?
Simple, the End User.
Only they know the importance of the applications quality of
experience to their requirements.
The same application can take on different roles, requiring
different bounds on the quality attenuation at different times.
There should be a differential price (though that may not mean a
differential charge) for different qualities
This creates the appropriate economic feedback to make a
rational market.
Most important is need for a ’scavenger’ style class — one where
there are no published bounds on ∆Q, a ’below normal’ service
where the delivered rate can be reduced to a trickle for peak periods
— would make a substantial difference to the economics of
Broadband delivery
The price/charge differential would need to be reasonably high
to persuade end users to engage.

Quality Attenuation
Costing Differential Services
Exploiting the Two Dimensional Nature of the Trading Space
The two dimensional nature of the ∆Q trading space has one other
interesting property. It can be used to calculate a the cost of
delivering of quality, using an opportunity cost argument.
1 At any point (network element) in the network there is some
∆Q, that ∆Q is conserved. Giving ’less’ ∆Q to some traffic
means that the remaining traffic must experience
proportionately more.
E.g. more traffic in the A1 box means that B2 traffic must
experience a greater ∆Q in proportion.
2 So the B2 traffic is not just reduced by the volume in A1, but
as some of the ∆Q budget has already been ’consumed’, even
less volume of traffic can be carried in B2 and meet the
budget.

Quality Attenuation
Conclusions
We’ve come at the ’quality’ issue in several different ways — all those
’quality’ issues can represented in terms of ∆Q — Quality Attenuation.
∆Q, its conservation and 2◦
of freedom is the underlying physical
property of statistically multiplexed data networks.
Any policy, regulation, service specification, network design,
application design, etc, etc has to work within its constraints.
This is good news, it helps define the ’Art of the Possible’, partially by
showing what is not possible, and partially by
∆Q bringing a quantitative basis to many of the contentious issues that
surround data networking today, like
How to specify requirements, predict performance, manage large
scale networks through creating ∆Q budgets, describe service
agreements, and cost proposed services.
It has been (and being) used to design new network elements, create
novel services over existing infrastructure and make distributed computer
system safer and more reliable.

The Properties and Mathematics of Data Transport Quality

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