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GKE Native Support for Custom Metrics: Smarter Autoscaling Beyond CPU and Memory
Modern cloud-native applications rarely scale perfectly using CPU or memory metrics alone. Many workloads are driven by signals like…
How to Optimize Kubernetes Costs
Learn proven Kubernetes cost optimization strategies and tools that finance leaders can use to reduce waste, improve efficiency, and control cloud spending.
Kubernetes Fine-Grained Horizontal Pod Autoscaling with Container Resource Metrics
Kubernetes Horizontal Pod Autoscaler (HPA) has revolutionized how we manage workloads by automatically scaling deployments/statefulset pods…
Kubernetes custom metric autoscaling: almost great
Custom metrics tend to be more accurate and useful than CPU- and RAM-based autoscaling, but could use improvement. Here's how to make it better.
Event-Driven Autoscaling in Kubernetes: Harnessing the Power of KEDA
How to use KEDA event driven approach to autoscale production kubernetes clusters.
The BigQuery Autoscaling Public Preview Rundown (DoiT Edition)
Author’s note: Google announced on March 29, 2023 that they are rolling out a completely new billing model for BigQuery that includes, and…
Autoscaling K8s HPA with Google HTTP/S Load-Balancer RPS EXTERNAL Stackdriver Metrics
Most of the time, we scale our Kubernetes deployments based on metrics such as CPU or memory consumption, but sometimes we need to scale based on external metrics. In this post, I’ll guide you through the process of setting up Horizontal Pod Autoscaler (HPA) autoscaling using any Stackdriver metric; specifically we’ll use the Request Per Second from a Google Cloud HTTP/S Load Balancer.
Autoscaling Google Dataproc Clusters
Cloud Dataproc is an amazingly fast to provision, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simple and very cost-efficient way.