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.
Peter Thompson CTO
Ex: GoS Networks, U4EA, SGS-Thomson, INMOS & Universities of Bristol, Oxford, Cambridge and
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.
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
– in addition to well-understood old ones.
• Using the right conceptual tools transforms
performance engineering from a skilled craft into
a mathematical science.
∆Q is the foundational concept
for this network performance science.
What is ΔQ?
• ΔQ is a fundamental breakthrough in the mathematics of
– 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.
• 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
– both a strong proxy to
application outcomes (hence
the customer experience)
– and a network performance
ΔQ is a morphism
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
– 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.
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
• Techniques using the ΔQ approach can transform
network capability and cost
– to enable sustainable broadband network economics.