Optimizing Kubernetes Applications for Performance and Cost - Pooja Malik
Description
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
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