Explore how you can leverage Dynatrace’s Davis® AI to forecast resource needs and automate pod scaling with workflows.
Key Highlights:
– Understand the importance of setting the correct quotas for your applications.
– Learn the differences between horizontal and vertical scaling and why HPA/VPA might not be enough.
– Discover how Davis® AI forecasts work and how they can predict resource usage.
– See how to integrate predictive scaling into a modern GitOps environment using GitHub for Workflows.
🔗 Useful links:
Explore our sample dashboards on the Dynatrace Playground:
Get hands-on and deploy our predictive auto-scaling demo app using GitHub Codespace:
Implement predictive autoscaling in your environments with our detailed Dynatrace Documentation: Coming Soon!
📖 Chapters 📖
00:00 Introduction
00:42 Why is (auto-)scaling important?
01:10 How can you scale in Kubernetes?
02:00 What about Horizontal and Vertical Pod Autoscalers?
03:37 Predicting resource consumption with Davis® AI
10:18 Using Davis® AI to scale Kubernetes Workloads
14:34 Expecting the unexpected
15:42 Conclusion and key takeaways
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