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Key takeaways
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Classroom Contents
Automating Performance Tuning with Machine Learning
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- 1 Intro
- 2 SREs care about efficiency and performan
- 3 Tuning system configuration matters...
- 4 but it is getting harder and harder
- 5 Key requirements for a new approach
- 6 ML techniques for smart exploration
- 7 ML enables automated performance tuning
- 8 and a new performance tuning process
- 9 The target system: Online Boutique
- 10 Use Case: optimizing cost of K8s microservices while ensuring reliability
- 11 The reference architecture
- 12 The optimization goals & constraints
- 13 Best configuration found by ML in 24H improves cost efficiency by 77%
- 14 Best config: optimal resources assigned to microservices
- 15 Best config: higher performance & efficiency for the overall service Baseline vs Best Service throughout Baseline vs Best Service po response time
- 16 Use Case: maximizing service performance & efficiency with JVM tuning
- 17 Best config: +28% throughput, and meeting SLOS
- 18 Best config: optimal JVM options 8
- 19 Key takeaways
