Models of Availability Case Studies
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This document discusses models of availability and complexity in large systems. It covers several services (SERVICEA-SERVICEG) and different priorities like performance, fault tolerance, and availability. It discusses how complex systems are difficult to comprehend and provides an example of a supply chain. It introduces the concepts of accidental and essential complexity. The document advocates for chaos engineering practices like experimenting in production by varying real-world events and automating experiments. Finally, it discusses intuition engineering and provides contact information.
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Models of Availability Case Studies
- 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS re:INVENT Models of Availability Casey Rosenthal November 30, 2017
- 4. Part One
- 13. FAULT TOLERANCE Performance Fault tolerance Availability Feature velocity PERFORMANCE AVAILABILITY
- 14. FAULT TOLERANCE Performance Fault Tolerance Availability Feature Velocity PERFORMANCE AVAILABILITY FEATURE VELOCITY
- 17. Part Two
- 18. Complex systems, by their nature, are unreasonable systems. They exceed the capacity of a human mind to comprehend the interaction of parts in any predictive capacity. UNREASONABLE SYSTEMS
- 19. Customers
- 22. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 Customers Retailer Brewer
- 24. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 4 2 2 -2 5 8 6 Customers Retailer Brewer
- 26. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 4 2 2 -2 5 8 6 64 5 5 -59 65 11 250 Customers Retailer Brewer
- 28. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 4 2 2 -2 5 8 6 64 5 5 -59 65 11 250 3 3 11 8 0 250 0 Customers Retailer Brewer
- 29. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 4 2 2 -2 5 8 6 64 5 5 -59 65 11 250 3 3 11 8 0 250 0 1 1 62 61 0 196 0 Customers Retailer Brewer
- 30. Demand Purchased In Stock Remaining Ordered Bottled Brewing 1 1 2 1 1 8 1 4 2 2 -2 5 8 6 64 5 5 -59 65 11 250 3 3 11 8 0 250 0 1 1 62 61 0 196 0 62 -61 196 distillery? Customers Retailer Brewer
- 37. Part Three
- 39. PERFORMANCE FAULT TOLERANCE AVAILABILITY FEATURE VELOCITY RELATIONSHIPS ENVIRONMENT IRREVERSIBILITY STATES
- 40. Performance Fault Tolerance Availability Feature Velocity ACCIDENTAL ESSENTIAL COMPLEXITY
- 43. Performance Fault Tolerance Availability Feature Velocity ACCIDENTAL ESSENTIAL COMPLEXITY
- 45. Part Four
- 46. CHAOS
- 48. CHAOS
- 49. CHAOS
- 53. E N G I N E E R I N G principlesofchaos.org
- 54. The facilitation of experiments to uncover systemic weaknesses E N G I N E E R I N G principlesofchaos.org
- 59. Tests: Define the valence of a known, asserted property Experiments: Create new knowledge by nullifying a hunch TESTS != EXPERIMENTS
- 60. ● Build a hypothesis about steady-state behavior principlesofchaos.org
- 61. ● Build a hypothesis about steady-state behavior. ● Vary real-world events principlesofchaos.org
- 62. ● Build a hypothesis about steady-state behavior. ● Vary real-world events. ● Experiment in production principlesofchaos.org
- 63. ● Build a hypothesis about steady-state behavior. ● Vary real-world events. ● Experiment in production. ● Automate experiments to run continuously principlesofchaos.org
- 64. ● Build a hypothesis about steady-state behavior. ● Vary real-world events. ● Experiment in production. ● Automate experiments to run continuously. ● Minimize the blast radius principlesofchaos.org
- 65. ● Build a hypothesis about steady-state behavior ● Vary real-world events ● Experiment in production ● Automate experiments to run continuously ● Minimize the blast radius principlesofchaos.org
- 76. Part Five
- 83. Ali Basiri abasiri@netflix.com Lorin Hochstein lhochstein@netflix.com Haley Tucker haley@netflix.com Nora Jones noraj@netflix.com Casey Rosenthal crosenthal@netflix.com @caseyrosenthal