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Automated Performance Tuning with Bayesian Optimization
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- 1 Intro
- 2 TWITTER RUNS ON MICROSERVICES
- 3 A PERFORMANCE STACK AT TWITTER
- 4 TUNING AT THE JVM LAYER
- 5 PERFORMANCE OPTIMIZATION
- 6 CONSTRAINTS
- 7 PERFORMANCE TUNING
- 8 OPTIMIZATION OF A BLACK BOX FUNCTION
- 9 BAYESIAN OPTIMIZATION EXAMPLE
- 10 ALTERNATIVE APPROACHES
- 11 BAYESIAN OPTIMIZATION EXPERIENCES AT TWITTER
- 12 MICROSERVICE STACK
- 13 OPTIMIZING A MICROSERVICE BY TUNING THE JVM
- 14 A SAMPLING OF JVM PARAMETERS
- 15 SET-UP
- 16 EVALUATION
- 17 PERFORMANCE OF THE OPTIMUM RESULT
- 18 GC COST
- 19 OPTIMIZED SETTINGS
- 20 KEY TAKEAWAYS
- 21 AUTOTUNE AS A SERVICE
- 22 WHAT DOES AURORA BRING TO THE TABLE
- 23 AURORA BASICS
- 24 LAUNCHING AN EXPERIMENT
- 25 A BRIEF DIVERSION
- 26 RUNNING AN EXPERIMENT
- 27 FINISHING AN EXPERIMENT
- 28 CLOSING THE LOOP
- 29 THE VIRTUOUS CIRCLE
- 30 BEYOND THE JVM
- 31 CONCLUSION
- 32 WHAT'S NEXT
