The promise of Google Kubernetes Engine (GKE) is the power of Kubernetes with ease of management, including planning and creating clusters, deploying and managing applications, configuring networking, ensuring security, and scaling workloads. However, when it comes to autoscaling workloads, customers tell us the fully managed mode of operation, GKE Autopilot, hasn’t always delivered the speed and efficiency they need. That’s because autoscaling a Kubernetes cluster involves creating and adding new nodes, which can sometimes take several minutes. That’s just not good enough for high-volume, fast-scale applications.
Enter the container-optimized compute platform for GKE Autopilot, a completely reimagined autoscaling stack for GKE that we introduced earlier this year. In this blog, we take a deeper look at autoscaling in GKE Autopilot, and how to start using the new container-optimized compute platform for your workloads today.
Understanding GKE Autopilot and its scaling challenges
With the fully managed version of Kubernetes, GKE Autopilot users are primarily responsible for their applications, while GKE takes on the heavy lifting of managing nodes and nodepools, creating new nodes, and scaling applications. With traditional Autopilot, if an application needed to scale quickly, GKE first needed to provision new nodes onto which the application could scale, which sometimes took several minutes.
To circumvent this, users often employed techniques like “balloon pods” — creating dummy pods with low priority to hold onto nodes; this helped ensure immediate capacity for demanding scaling use cases. However, this approach is costly, as it involves holding onto actively unused resources, and is also difficult to maintain.
Source Credit: https://cloud.google.com/blog/products/containers-kubernetes/container-optimized-compute-delivers-autoscaling-for-autopilot/
