Kubernetes utilization is typically poor with only 20-45% of requested resources used. Kubernetes optimizations must meet application demands and minimizing idle resources. This video covers the potential to optimize at the pods and node/cluster level. The capabilities and limitations of Kubernetes HPA, VPA and Cluster Autoscaler are covered here.
Key moments:
00:24: Resource wastage common
01:22: Kubernetes utilization is typically poor and 35% of cloud spend is wasted
02:23: How to balance application performance demand and minimizing idle resources
02:41: Autoscaling is a major pillar to manage performance and cost
02:54: Kubernetes offers scaling at pod or
03:32: Horizontal Pod Autoscaling (HPA)
05:00: How Cluster Autoscaler Works
06:06: Vertical Pod AutoScaler (VPA)
07:46: When to use HPA vs VPA
08:50: Kubernetes Autoscalers have serious limitations
11:50: Cluster Autoscaler has limitations also
13:31: Final Thoughts: scaling is key but admin overhead is high
14:24: Autonomous can solve the issues
15:04: Q&A - Why can't we use HPA and VPA
16:32: Q&A - How configure HPA and HPA for stateful workloads?
17:28: Q&A - Are there other tools that could help in HPA & VPA configuration? e.g,., Keda
18:45: Q&A - How does compliance/change management work with autonomous systems?
20:37: Q&A: Watchouts for performance management for K8s deployments
22:16: Q&A: Differences between k8s providers
To learn more about Sedai visit https://bit.ly/3e3Cqlv
#autonomouscloud #kubernetes #hpa #vpa #autoscaling #sre #devops #aws #a8s4k8s #sedai #autocon22
