Addicted to speed: Why broadband service providers need a ‘healthier lifestyle’

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Broadband service providers are trapped in a vicious circle of network upgrades where they try to use capacity to fix scheduling problems. To escape this cycle, they need to construct their networks differently to schedule traffic appropriately. The benefits are enormous.

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Addicted to speed: Why broadband service providers need a ‘healthier lifestyle’

  1. 1. Addicted to speed: Why broadband service providers need a ‘healthier lifestyle’ CommunicAsia 2014 Singapore, 17th June 2014 PREDICTABLE NETWORK SOLUTIONS © 2014 All Rights Reserved Modified version for Web upload. Same content, different format.
  2. 2. The only network performance science company in the world. PREDICTABLE NETWORK SOLUTIONS If we are wrong then please tell us, as it’s a bit lonely sometimes!
  3. 3. Dr Neil Davies Co-founder and Chief Scientist Computer Scientist, Mathematician and Engineer (but not a Futurologist) Sustainability of ICT The expertise I am sharing here
  4. 4. 15-25 YEARS AHEAD We can foresee many likely future demands on broadband networks
  5. 5. TODAY 15-25 YEARS AHEAD Meeting these requirements is influenced by what we do…
  6. 6. TODAY 15-25 YEARS AHEAD We are unknowingly storing up some big problems!
  7. 7. This may be a difficult message to hear
  8. 8. You need to change your ‘lifestyle’…
  9. 9. …and adopt a ‘healthier’ alternative
  10. 10. But why… …do I need to change my lifestyle?
  11. 11. Key messages
  12. 12. Problem The pursuit of ever more speed has put the broadband business in a vicious circle. 
  13. 13. Why so? Speed (‘bandwidth’) is no longer a helpful model for broadband. 
  14. 14. So what? You need to change your model to survive and  prosper.
  15. 15. Problem 
  16. 16. More, more, more More supply Great! A “faster” network!
  17. 17. More, more, more More elastic demand But demand automatically expands to use resources
  18. 18. More, more, more Faster saturation of infrastructure This creates a “jackhammer” effect
  19. 19. More, more, more More variability Applications need consistency of loss and delay
  20. 20. More, more, more Lower QoE When they experience rapidly varying loss and delay, you get…
  21. 21. More, more, more More complaints and churn In competitive markets that drives… In other markets the regulator gets the flack and comes under pressure to act
  22. 22. More, more, more More cost Churn is expensive, so you have to restore QoE. How?
  23. 23. More, more, more More supply And round we go again!
  24. 24. More supply More elastic demand Faster saturation of infrastructure More variability Lower QoE More complaints and churn More cost The technical vicious circle
  25. 25. The investment ‘cycle of doom’Service quality Undepreciated assetvalue   ($$$) ($) Let’s look at how QoE and operator debt change over time TIME
  26. 26. ServiceQualityUndepreciatedAssetValue The investment ‘cycle of doom’Service quality Undepreciated assetvalue   ($) ($$$) As you add users to an empty network, QoE declines Those users help you to pay down the debt used to fund the network
  27. 27. ServiceQualityUndepreciatedAssetValue The investment ‘cycle of doom’Service quality Undepreciated assetvalue   ($) ($$$) QoE falls faster than simplistic bandwidth models suggest and churn rises You need to upgrade earlier than your capacity planning and financial models predicted
  28. 28. ServiceQualityUndepreciatedAssetValue   ($$$) ($) The investment ‘cycle of doom’ Rising load makes service quality fall, forcing repeated upgrades
  29. 29. ServiceQualityUndepreciatedAssetValue   ($$$) ($) The investment ‘cycle of doom’ The period between upgrades falls due to decreasing effectiveness of capacity upgrades to resolve the QoE issue
  30. 30. ServiceQualityUndepreciatedAssetValue The investment ‘cycle of doom’ Failure to keep up with ever-rising demand forces ever-shorter upgrade cycles
  31. 31. UndepreciatedAssetValue The end result?
  32. 32. UndepreciatedAssetValue Death via unserviceable debt load 
  33. 33. Why so?  What drives the vicious circle?
  34. 34. Cosmic Ludic Ecological Constraints on everything We live in a finite universe where we can’t get everything we might want
  35. 35. Cosmic Cosmic constraints Physics limits us in many ways: not just the speed of light, but also energy conservation, or how much information we can encode on a channel (Shannon limits)
  36. 36. Ludic Ludic constraints “Ludic” constraints are “games”, with mathematical rules and limits. Chess can be mathematically modelled, for example Broadband is like a statistical ‘game of chance’
  37. 37. Ecological Ecological constraints There are constraints of human nature, law, technology availability, standards, etc.
  38. 38. Cosmic Ludic Ecological Speed of light Statistical multiplexing Pricing policy Broadband drug: stat mux gain
  39. 39. Why trust in increasing speed is now misplaced This may be a difficult message to hear. We did warn you!
  40. 40. Why trust in increasing speed is now misplaced Packetdelay Let’s consider the delay a packet experiences…
  41. 41. Why trust in increasing speed is now misplaced Pre-IP EarlyIP Now …and see how that changes over time Packetdelay
  42. 42. Cosmic constraint Pre-IP EarlyIP Now Packetdelay How did this constraint change?
  43. 43. Pre-IP EarlyIP Now Geography Packetdelay Cosmic constraint Fixed overhead: Speed of light, packet routing lookups
  44. 44. Pre-IP EarlyIP Now The speed of light is not changing Packetdelay Geography Cosmic constraint
  45. 45. Pre-IP EarlyIP Now Packetdelay Ecological constraint How did this constraint change?
  46. 46. Pre-IP EarlyIP Now Serialisation speed Packetdelay Ecological constraint How quickly can we squirt the packet over a link?
  47. 47. Pre-IP EarlyIP Now Packetdelay Ecological constraint Historically speed did correlate with more value Serialisation speed
  48. 48. Pre-IP EarlyIP Now Packetdelay Ludic constraint How did this constraint change?
  49. 49. Variability Pre-IP EarlyIP Now Packetdelay Ludic constraint Delay due to other packets in the system
  50. 50. Pre-IP EarlyIP Now Packetdelay Ludic constraint Variability Now dominates application performance
  51. 51. G, S and V G S V Variability Serialisation speed Geography The outliers are what kill application performance, and they are growing
  52. 52. Shifting constraints G S V Ecological Cosmic Once we had digital networks, the key constraint was ecological
  53. 53. Shifting constraints G S V Ludic Ecological Cosmic It is now ludic, but mainstream network engineering & regulatory policy has yet to reflect this
  54. 54. Networks are… trading spaces   …principally for V, in a statistical ‘game of chance’
  55. 55. How ‘V’ is distributed among competing streams is how demand is matched to the supply Fact
  56. 56. “Magical” thinking Problem
  57. 57. When there is excessive delay, people are trying to make V disappear by building more capacity rather than distributing it through scheduling Problem Attempting to solve scheduling problems using capacity is inefficient and ineffective
  58. 58. Result: telecoms is a capital killer Source: PwC http://www.pwc.com/en_GX/gx/communications/publications/assets/pwc_capex_final_21may12.pdf It’s not getting any better since then
  59. 59. So what?  Is there a better approach? Can the cycle be broken?
  60. 60. What has to change? NOW FUTURE MORE BANDWIDTH Selling peak speed and commodity inputs
  61. 61. What has to change? NOW FUTURE MORE BANDWIDTH Selling peak speed and commodity inputs BETTER SCHEDULING Selling QoE & differentiated application outcomes
  62. 62. (Simplified) structure of broadband demand Bulk Interactive Real-time
  63. 63. (Simplified) structure of broadband supply Bulk Interactive Single class of service
  64. 64. Today’s economic model Real-time Bulk Interactive Real-time Too quality- sensitive Too cost- sensitive COST REVENUE
  65. 65. Today’s economic model Real-time Bulk Interactive Real-time COST REVENUE Everything carries the high costs of real-time, but the revenues don’t match that cost structure
  66. 66. Example of a possible alternative supply approach Economy Standard Superior This three-class “polyservice” model is specially constructed. It should not be confused with existing “priority QoS” mechanisms
  67. 67. Example of a possible alternative supply approach Superior traffic costs more to deliver… so should attract a premium Economy Standard Superior
  68. 68. Example of a possible alternative supply approach Standard traffic is today’s off-peak Internet… but is consistently the same Economy Standard Superior
  69. 69. Example of a possible alternative supply approach Economy traffic does not drive capacity upgrades Economy Standard Superior It is also unsuitable for real-time applications
  70. 70. Future rational economic model COST REVENUE Economy Standard Superior Economy Standard Superior
  71. 71. Five class model incorporates resilience SuperiorStandard SuperiorStandard Economy SuperiorStandard SuperiorStandard Economy SuperiorStandard SuperiorStandard Economy Drives capacity planning (primary service) COST Drives resilience & redundancy capacity planning COST Drives REVENUE
  72. 72. How to reach health and prosperity?
  73. 73. NO! 1. Firefighting – due to rapid QoE declines. 2. Panic buying – of capacity to deal with QoE crises. 3. Complaining! – There's loads of slack.
  74. 74. YES! 1. Measure QoE – on customer-centric basis. 2. Increase utilisation via scheduling – to make a profit. 3. Plan capacity – based on QoE effects.
  75. 75. The Prize 10%? 30%? >100%? Improvement in QoE from assets Spectacular cost and QoE improvements are possible when the best available mathematics is applied to scheduling problems.
  76. 76. Dr Neil Davies Neil.Davies@pnsol.com Tel: +44 (0)3333 407715 PREDICTABLE NETWORK SOLUTIONS Thank you

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