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Digital supply chain quality management

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We've figured out how to send physical goods around the world: aggregate them into containers. We're still struggling how to do digital good, which we disaggregate into packets. Here's the answer.

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Digital supply chain quality management

  1. 1. Digital Supply Chain Quality Management System How to deliver assured application performance and fit-for-purpose cloud access services? February 2018
  2. 2. NETWORK DEMAND • Weakly characterised performance envelope • Poorly defined network demand using averaged scalar metrics (throughput, loss, delay, jitter) – but there no quality in averages • Focus on demand for bandwidth/speed (quantity) but not on stationarity (quality) App performance QoE demand = network QoS requirement NETWORK SUPPLY • Emergent performance and highly variable quality (by geography, bearer, product, line) • Unstable quality (‘non-stationary’), so apps fail • Poor product performance metrics (mostly ‘speed’ – a weak proxy for QoE and quality) • Not assured; no quality floor vs UNWANTED RESULT: • Cannot compare & predict fitness-for-purpose; blame game with app/network suppliers • All performance risk transferred from vendor(s) to client • Performance failure only becomes visible in deployment; comes & goes without warning • Business benefit of move to cloud/SaaS/UC/virtual working lost • Self-insurance cost (plan Bs) and uncontained customer brand/employee goodwill impact REQUIREMENT: RUN DISTRIBUTED APPLICATION(S) WITH A BOUNDED FAILURE RATE 2
  3. 3. TYPICAL ROOT CAUSES OF DIGITAL EXPERIENCE QUALITY FAILURE UNMANAGED APPLICATION PERFORMANCE FAILURE Why so? PEOPLE Skills gap: wrong belief QoE engineering is not even possible, or can be done using weak QoE metrics PROCESS Lack a scientific management framework for digital experience quality; failure to apply proven knowledge TECHNOLOGY Immature science and engineering of quality in digital supply chains; wrong resource model Acquire language of performance hazards and skills to reason about supply and demand in digital supply chains Apply existing variation management science (theory of constraints, lean, six sigma) to digital experience quality Use quality attenuation analytics and high-fidelity network measures to see what is really happening & model/predict 3
  4. 4. THE ‘QUALITY ATTENUATION’ FRAMEWORK OFFERS A HOLISTIC SOLUTION FOUNDATIONS Mathematics ∆Q calculus provides rational unified resource model for distributed computing Science New ’quality attenuation’ metrics and models for demand and supply Engineering Performance hazard modelling adapted from safety-critical systems TECHNOLOGY Measurement Instantaneous performance captures by high-fidelity measurement (space & time) Models Robust predictive models based on “performance budgets” for supply chains Mechanisms High-frequency resource trading adapted from supercomputing CAPABILITIES Calibration QoE-centric network data with strong causality model Coordination Contract technical performance at boundaries in digital supply chains Control New approach to scheduling and quality assurance that delivers predictable performance and QoE For more technical detail, visit qualityattenuation.science. 4
  5. 5. THE DIFFERENCE THE QUALITY ATTENUATION FRAMEWORK APPROACH MAKES Predictable performance with a managed QoE “safety margin” It’s only ordinary science and engineering using proven management methods. There is no magic involved! Just new mathematics and metrics. Perform robust product feasibility analysis in advance of deployment Size individual deployments and quantify QoE risk Isolate performance problems using the scientific method The concept of a “performance hazard” allows us to relate the supply to demand and quantify the risk of QoE failure via a “performance contract”. This technical contract between supply and demand is called a Quantitative Timeliness Agreement (QTA). Meeting the QTA bound on loss and delay is both necessary and sufficient to deliver the application performance outcome. 5
  7. 7. OUR SPECIAL NETWORK “X-RAY VISION SPECTACLES” LET US SEE SUPPLY VS DEMAND See performance from the user perspective DEMAND See performance from the network perspective SUPPLY Relate demand to supply using quality attenuation analytics, high-fidelity network measurement, and the ∆Q calculus to predict performance Rather than 3D movie glasses, it is really a well-proven measurement system developed over 10 years. It injects low bitrate ‘golden packet’ flows with special statistical properties. The timing of those ‘golden’ packets are observed as they pass in each direction, and that data is analysed to construct an high- definition ‘image’ of the network in space and time. This system has been deployed many times at tier 1 fixed and mobile operators, and is now available for end user use in a compact virtual machine form. 7
  8. 8. EXAMPLES OF HIGH-FIDELITY MEASURES (MANY MORE ARE AVAILABLE) Round Trip (run e4e53aec-4045-4d2f-96e7-67ebf1307ce2) 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0 50 100 150 200 250 300 delay(s) run time (s) Observed Delay against Experiment Run Time boris-s001->london->NHC->london->boris-s001 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0 200 400 600 800 1000 1200 1400 1600 delay(s) packet size (octet) Observed Delay against Packet Size boris-s001->london->NHC->london->boris-s001 G=8.69e-3s, S=2.52e-7s/octet, MSE=2.08e-7 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0 50 100 150 200 250 300 delay(s) run time (s) Observed Delay Variability (V) against Experiment Run Time boris-s001->london->NHC->london->boris-s001 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0 200 400 600 800 1000 1200 1400 1600 delay(s) packet size (octet) Observed Delay Variability (V) against Packet Size sample variability (V): mean=1.20e-2s, std dev=2.75e-2 boris-s001->london->NHC->london->boris-s001 0.8 1 vedservice Cumulative Distribution of V fraction lost=5.40e-2 boris-s001->london->NHC->london->boris-s001 1 veservice scale Inverse Cumulative Distribution of V boris-s001->london->NHC->london->boris-s001 4G home gateway + WiFi in Lithuania ATLAS experiment at CERN (video at 40 million frames/sec) Satellite in southern Spain Peak hour DOCSIS cable + WiFi in Ireland 8
  9. 9. ∆Q METRICS HAVE AN ALGEBRA FOR TRUE ENGINEERING (AND NOTHING ELSE DOES!) ∆Q(A) ∆Q(B) ∆Q(C) VA SA GA VB SB GB VC SC GC + + + + + + = = = ∑V ∑S ∑G SUPPLIER A SUPPLIER B SUPPLIER C ∆Q(∑ A+B+C) 9 Variable delay due to load Size of packet delay Geographic delay ∆Q|G ∆Q|S ∆Q|V Packet size One-waydelay G/S/V are independent probability functions using improper random variables or improper cumulative distributions. These can be (de)convolved and “budgeted” along the supply chain using (de)composable “quality contracts”.
  10. 10. Single class of service Unpredictable ‘best effort’ quality Poor resource utilisation Complex to manage Multiple classes of service Predictable ‘just right’ quality Excellent resource utilisation Simple to manage “BEST EFFORT” Quality with a quantity Low value, high cost QUALITY ASSURED Quantity of quality High value, low cost NEW SCHEDULING MECHANISMS DELIVER ASSURED QUALITY TO A ∆Q-BASED SPEC By applying the concepts of ‘lean’ and ‘just in time’ to packet networks we can achieve a transformation in economics and deliver ‘assured cloud access’. 10
  11. 11. INTENTIONAL SEMANTICS DENOTATIONAL SEMANTICS OPERATIONAL SEMANTICS What QoE you wanted What QoS you asked for What you got What it maybe useful for What QoE it might give What QoS has happened F4P: P4F: Quality Assured: Fitness-for-purpose (F4P) Best Effort: Purpose-for-fitness (P4F) – Engineered performance with predictable quality floor Emergent performance with unpredictable quality floor – THIS IS A PARADIGM CHANGE FROM EMERGENT TO ENGINEERED PERFORMANCE 11 This change is the basis for an telco/cloud industry transformation comparable to how physical transport switched from break bulk shipping to intermodal container logistics.
  12. 12. To learn more about the science visit qualityattenuation.science. To discuss how we can work together contact mail@martingeddes.com.