Introduction and summary sections from long slide deck (165 slides) on network performance science as the associated mathematical breakthrough that makes it possible.

1. Introduction to ΔQ and
Network Performance
Science
© Predictable Network Solutions 2014
INTRODUCTION +
SUMMARY EXTRACTS

2. Dr Neil Davies Co-founder and Chief Scientist
Ex: University of Bristol (23 years).
Former technical head of joint university/research institute (SRF/PACT).
The only network performance science company in the world.
• High-fidelity network performance measurement and analysis.
• Performance modelling and prediction for packet networks.
• World’s first network contention management solution.
PREDICTABLE
NETWORK
SOLUTIONS
Peter Thompson CTO
Ex: GoS Networks, U4EA, SGS-Thomson, INMOS & Universities of Bristol, Oxford, Cambridge and
Warwick.
Authority on technical and commercial issues of converged networking.
Martin Geddes Associate Director of Business Development
Ex: BT, Telco 2.0, Sprint, Oracle, Oxford University.
Consultant on the emerging telecommunications technology and business models.

3. ΔQ AND
NETWORK PERFORMANCE SCIENCE
Overview of
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4. Packet-based networking is a new discipline
• It is fundamentally different from earlier forms
of circuit-based telecommunications technology.
• To reason about its performance requires new
concepts
– in addition to well-understood old ones.
• Using the right conceptual tools transforms
performance engineering from a skilled craft into
a mathematical science.
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∆Q is the foundational concept
for this network performance science.

5. What is ΔQ?
• ΔQ is a fundamental breakthrough in the mathematics of
stochastic systems.
– A conceptual innovation like ‘zero’ in arithmetic, or ‘imaginary
numbers’ in complex analysis.
– A new branch of mathematics that underpins probability theory.
– It has comparable utility to the development of computability in
the 1930s or information theory in the 1940s.
• ΔQ is a unified stochastic model of variability (typically of
time delay) and loss.
– Standard queuing theory, when applied to networks, fails to
model reality, since it inadequately models loss.
– The mathematics of ΔQ is based on improper random variables
and their composition, in order to model loss.
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6. Network
performance
Customer
experience
CONCRETE
CONCRETE
• A morphism is a model that
can be viewed from multiple
levels of abstraction.
• This morphism formally
relates network performance
to customer experience
• ΔQ is the only possible
general network measure
that is:
– both a strong proxy to
application outcomes (hence
the customer experience)
– and a network performance
metric
ΔQ is a morphism
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ABSTRACT
ΔQ
ΔQ
ΔQ
ΔQ
ΔQ
ΔQ
ΔQ
Model that
joins these

7. We use ΔQ to measure, model,
and manipulate performance
1. ΔQ-based measurement is a universal quality of
experience (QoE) proxy
– ΔQ can also be composed & decomposed in several ways,
depending on the level of abstraction used.
2. ΔQ can be used to predictively model network
performance.
– Robust prediction with strong philosophical foundations is the
essence of the scientific method.
3. The ΔQ approach enables powerful new ways to
manipulate packet network performance.
– Technologies based on ΔQ enable networks to be driven to
their theoretical mathematical limits.
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8. The value of ΔQ and performance science
• ΔQ helps us to reason about the performance of
complete distributed computing systems.
– It offers a new perspective that is focused on the
computational outcome, not the packets per se.
– It offers both prediction and assurance. This a
(missing) critical precondition to successful network
virtualisation.
• Techniques using the ΔQ approach can transform
network capability and cost
– to enable sustainable broadband network economics.
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9. WHY IS ∆Q IMPORTANT?
Summary
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10. Why is ∆Q important?
• Fundamental technical breakthrough
– Similar to Maxwell’s wave equation (continuous);
Boltzman’s kinetic theory of gases (discrete)
– Key features: power laws; error of measurement;
system of 2 degrees of freedom
– Conservation law (“=”, “>”) – like E – mc2 = 0
• Aligns technical with commercial
– Captures dynamic vs static properties
– QoE is about making bad experiences rare and ∆Q
models failure modes
– Allows optimisation to a cost/QoE goal
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11. Why is ∆Q commercially valuable?
• ∆Q is a rational unified resource model
– Commercial resources: money, QoE
– Technical resources: resource opportunity costs, ∆Q
• It is a fundamental conceptual advance in our
ability to financially model networks
– Foundational idea like ‘double-entry book keeping’ in
economics, or ‘just-in-time’ in manufacturing
• Allows performance budgeting
– Aligns technical and financial management
– Enables prospective cost models, and not merely
retrospective ones
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