The Role of Machine Learning In Cloud Native Performance Optimization
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The document discusses the challenges of Kubernetes resource management, highlighting issues like over-provisioning and its impacts on cost, performance, and reliability. It advocates for machine learning-driven optimization that utilizes in-depth application analysis and automation to improve efficiency. The author, Brian Likosar, emphasizes the need for a more intelligent approach to resource management in cloud-native environments.
The Role of Machine Learning In Cloud Native Performance Optimization
- 1. Brought to you by The Role of Machine Learning For Cloud Native Performance Optimization Brian Likosar Liko Global Director of Solutions Architecture at StormForge
- 2. Liko (lick-oh) Global Director of Solution Architecture ■ Worked with open source for ~25 years ■ Failed statistics at University, but still appreciate P99s ■ Theme Park nerd who loves live music ■ Father, husband, friend, and mentor
- 3. K8s Resource Optimization? Sure, But How?
- 4. Some Are Already Doing It… But
- 5. Kubernetes Resource Management Complexity PERFORMANCE COST RELIABILITY Container 1 CPU Memory Requests Limits Requests Limits Replicas Container 2 CPU Memory Requests Limits Requests Limits Replicas Application Settings JVM Heap size Garbage collection Container 3 CPU Memory Requests Limits Requests Limits Replicas Shuffle file buffer Reducer max size
- 6. This Complexity Forces You to Choose… ■ Over-provisioning, driving cost of revenues up ■ Risking application performance & availability issues ■ Slowing down time-to-market so your DevOps team can focus on achieving efficiency OVER-PROVISIONING/ COST PERFORMANCE & RELIABILITY ISSUES CONFIGURATION TIME & EFFORT
- 7. “... but this is kind of stupid, right? Humans adapting things in YAML files… you want to have something automatic, AI and what not…” (Henning Jacobs, Head of Dev Productivity, Zalando)
- 8. Key Attributes of ML-driven Optimization In-depth app analysis & insights Maximize value from existing data Purpose-built ML-Algorithms Automation (to be not stupid)
- 9. Until patterns emerge, it’s impossible to make informed decisions 9
- 10. Until patterns emerge, it’s impossible to make informed decisions 10
- 11. Come chat with us in person! - KubeCon, Detroit, 24-28 Oct 2022 - AWS re:Invent, Las Vegas, 28 Nov - 2 Dec 2022
- 12. Brought to you by Brian Likosar liko@stormforge.io @liko9 https://www.linkedin.com/brianlikosar