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Network performance optimisation using high-fidelity measures

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Communications service providers are seeking to increase their profitability and return on assets Predictable Network Solutions Ltd has the capability to support optimisation beyond traditional …

Communications service providers are seeking to increase their profitability and return on assets Predictable Network Solutions Ltd has the capability to support optimisation beyond traditional approaches to network data analytics. This capability is built around a robust scientific method. CSPs can benefit greatly from enhancing the fidelity of their measurements of critical aspects of network performance. Standard techniques fail to capture enough resolution. We have the missing leading-edge measurement capabilities that all CSPs need.

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  • 1. Network Performance Optimisation How Communications Service Providers (CSPs) can create new value from quality attenuation analytics © Predictable Network Solutions 2013
  • 2. PREDICTABLE NETWORK SOLUTIONS The only network performance science company in the world. • New mathematical performance measurement and analysis techniques. • Performance assessment methodology. • World’s first network contention management solution. Dr Neil Davies Co-founder and Chief Scientist Ex: University of Bristol (23 years). Former technical head of joint university/research institute (SRF/PACT). Peter Thompson CTO Ex: GoS Networks, U4EA, SGS-Thomson, INMOS & Universities of Bristol, Warwick and Cambridge. 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. Thought leader on the future of the telecommunications industry.
  • 3. Presentation Outline • CSPs are seeking to increase their profitability and return on assets. • Predictable Network Solutions Ltd has the capability to support optimisation beyond traditional approaches to network data analytics. – This capability is built around a robust scientific method. • CSPs can benefit greatly from enhancing the fidelity of their measurements of critical aspects of network performance. – Standard techniques fail to capture enough resolution. • We have the missing leading-edge measurement capabilities that all CSPs need. © Predictable Network Solutions 2013 3
  • 4. The need to manage to the right metrics CSPS’ QOE AND COST DILEMMA © Predictable Network Solutions 2013 4
  • 5. What are the network optimisation goals of every CSP? Commercial Technical The CSP’s revenue is ultimately bounded by the value perceived by the final end user. • User value is derived from applications delivering fit-forpurpose outcomes (FFPOs). • Users value consistency CSPs need to make bad user experiences sufficiently rare, at affordable cost. • This creates a balancing act: running the network too hot vs too cold. – The absence of failures of service – Bad experiences must be rare • Every CSP’s goal is to maximise the value of FFPOs (i.e. QoE) at the minimum input cost. – For this they need to have good proxies for QoE. • A good proxy is one that directly relates to the delivered QoE… – …that can also be measured, managed and predicted… – …and must also have low operational cost to gather. © Predictable Network Solutions 2013 5
  • 6. Network performance measures Average Single Point Offered Load and Utilisation (mean values only) Today’s key CSP QoE proxy. Is it a good one? Might there be some important details about traffic conditions that are lost? (Yes!) No! Reporting the number of packets on a 1Gb/s Ethernet link every five minutes is like counting cars on a six-lane highway for two years! © Predictable Network Solutions 2013 6
  • 7. Need distributions, not averages: Same bandwidth, different QoE Comparison between two LLU broadband providers to same location in the UK. ‘Bandwidth’ is an average. It fails to capture this non-stationarity. SAME ‘BANDWIDTH’ 1/3 THE VALUE The difference between these ISPs is the distribution of loss and delay. The one on right has 1/3 the capability of the left for carrying POTS-quality VoIP. © Predictable Network Solutions 2013 7
  • 8. Utilisation is a poor proxy for QoE The data CSPs use: bandwidth The data CSPs need: strong QoE proxy © Predictable Network Solutions 2013 This is (the first publishable) evidence comparing utilisation with a direct QoE measurement. This is a well-run and well-managed network. Our engagements with CSPs have shown this to be a common phenomenon. 8
  • 9. High load, but no QoE breach Overprovisioning just wastes money Low load (<0.01%), but QoE breach © Predictable Network Solutions 2013 Overprovisioning doesn’t solve your QoE problem 9
  • 10. The CSP QoE and cost problem Commercial Technical The failure to appropriately measure QoE means there are unmanaged hazards in the current supply chains. • These hazards can and do mature into application and network failures. • FFPOs are dropping, and cost per FFPO is rising. In-life management costs increase due to the inability to manage the QoE hazards, which appear as ‘faults’. So: • CSPs turn to arbitrary traffic management to shed load which, in turn, increases tension between customers, legislators and CSPs; • Or, CSPs regress to previous planning and design ratios by capping access speeds due to continuing failure; • Or, stationarity continues to decrease, reducing FFPOs and QoE, which leads to less value-in-use and tarnishes every CSP’s reputation. – This leads to premature upgrades, compared to the original capacity plan. • Return on assets continues to drop… – …so CSP share prices fall. © Predictable Network Solutions 2013 10
  • 11. Service Quality The CSP investment ‘cycle of doom’ QoE declines faster than the capacity plan predicts Time Undepreciated Asset Value Rising load makes service quality fall, forcing upgrades Failure of technology to keep up with ever rising demand forces shorter upgrade cycles Upgrade before previous investment amortised  Death via unserviceable debt load 11 Time
  • 12. All analytic approaches are limited by the fidelity of their inputs HOW TO OBTAIN PERFORMANCE DATA WITH REAL VALUE? © Predictable Network Solutions 2013 12
  • 13. FFPOs require bounded ‘quality attenuation’ (∆Q) Median time to complete HTTP transfer in seconds Different QoE implies different bounds on ∆Q One-way loss rate (%) Need to manage network to a QoE goal We care about both loss and delay One-way delay (ms) © Predictable Network Solutions 2013
  • 14. ΔQ accumulates along a path Example: 3G round-trip cross-sectional analysis We want visibility of how each network element contributes to ΔQ (No service) © Predictable Network Solutions 2013
  • 15. Network performance measures Average Single Point Offered Load and Utilisation (mean values only) PLUS To get loss and delay plus path decomposition we need multi-point measurements (and not just multiple single-point measurements) Multiple Delay and Loss Point (mean and variance) © Predictable Network Solutions 2013 15
  • 16. There is no ‘quality’ in averaged measurements CSPs need highfidelity data to see fast-varying QoE effects AVERAGE DELAY ∆Q for 16kbit offered load at a busy international 3G location © Predictable Network Solutions 2013 16
  • 17. FFPOs require strict bounds on loss and delay HTTP time to complete in seconds (95th percentile) Just a few users falling over the ‘cliff’ generates churn, even if the average user is OK One-way delay (ms) One-way loss rate (%) CSPs need to manage their delivery to avoid these QoE ‘cliffs’ © Predictable Network Solutions 2013
  • 18. Network performance measures Average Single Point Offered Load and Utilisation PLUS (mean values only) Multiple Delay and Loss Point (mean and variance) © Predictable Network Solutions 2013 Distribution Arrival Patterns Capturing the ‘outliers’ of QoE means we need the distribution of packet arrival patterns. 18
  • 19. Network performance measures Average Single Point Multiple Point Distribution The data CSPs use When you capture distributions via multi-point measurements you get the strong QoE proxy data you need. © Predictable Network Solutions 2013 The data CSPs need 19
  • 20. How to measure the right things with a robust scientific method EXPLOITING HIGH-FIDELITY MEASUREMENTS © Predictable Network Solutions 2013 20
  • 21. High-fidelity data capture is the key enabler Commercial Technical CSPs want to set a price floor for their services, and differentiate via network quality. • This increases the focus on getting the trade-off between cost and QoE right. • Current network management approaches focus on making the average experience better. QoE depends on ∆Q…and nothing else. • QoE certainly does not depend on averages or peak bandwidths. – The key is making bad experiences rare. Performance data needs to enable CSPs to directly manage the cost/QoE tradeoff. – Average or peak measures like ‘bandwidth’ at best allow CSPs to manage cost vs performance. • The current capture processes lose critical information that impacts QoE. – CSPs don’t measure ∆Q directly. – Current approaches try to compensate by gathering more and more data, the volume of which itself degrades the network quality! © Predictable Network Solutions 2013 21
  • 22. Network performance measures Average Distribution Single Point Limited predictive power Temporal predictive power (and localised assurance) Multiple Point Spatial predictive power ΔQ Temporal and spatial predictive power © Predictable Network Solutions 2013 22
  • 23. Network performance measures Average Single Point Multiple Point Distribution Limited predictive power Temporal predictive power LOW FIDELITY LOW VALUE Spatial predictive power Represents all that can be known about a system (by observation) © Predictable Network Solutions 2013 HIGH FIDELITY HIGH VALUE 23
  • 24. NetHealthCheck™ Process Our service that embodies these ideas Inject low-rate test streams Measure test streams at multiple points Analyse measurements to obtain distributions Understand QoE/cost tradeoff © Predictable Network Solutions 2013 24
  • 25. Example client outcomes 1. Major UK mobile network operator • Was in 2nd/3rd place in its market (depending on location) for HTTP download key performance indicator (KPI). • NetHealthCheck™ enabled a 100% improvement in this KPI without any additional capital expenditure. • Placed MNO as definitive 1st in the market. 2. BT Operate • Applied to delivery of wholesale broadband services… – …on a mature, highly-optimised, well-managed network. • Revealed flexibility to optimise planning rules. • Potential for 30% increase in utilisation of key resources. • Estimated savings value of £2.3M. © Predictable Network Solutions 2013 25
  • 26. NetHealthCheck™ Benefits Structural capacity optimisation: 10% - 30% + Scheduling optimisation: 25% - 75% = QoE improvement 50% - 100% These all generate ‘slack’ to… …sweat assets to optimise CAPEX: get ‘free’ growth. …improve QoE at no cost: for all customers, or specific groups. © Predictable Network Solutions 2013 26
  • 27. For more information Visit our website for detailed case studies, presentations and white papers www.pnsol.com Contact us info@pnsol.com © Predictable Network Solutions 2013 27