Introduction to ΔQ and Network Performance Science (extracts)


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

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Introduction to ΔQ and Network Performance Science (extracts)

  1. 1. Introduction to ΔQ and Network Performance Science © Predictable Network Solutions 2014 INTRODUCTION + SUMMARY EXTRACTS
  2. 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. 3. ΔQ AND NETWORK PERFORMANCE SCIENCE Overview of © Predictable Network Solutions 2014 3
  4. 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. 4 ∆Q is the foundational concept for this network performance science.
  5. 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. 5
  6. 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 6 ABSTRACT ΔQ ΔQ ΔQ ΔQ ΔQ ΔQ ΔQ Model that joins these
  7. 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. 7
  8. 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. 8
  9. 9. WHY IS ∆Q IMPORTANT? Summary © Predictable Network Solutions 2014 9
  10. 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 © Predictable Network Solutions 2014 10
  11. 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 © Predictable Network Solutions 2014 11