Announcing the general availability of worker node auto-scaling in IBM Cloud Kubernetes Service
We’re extremely excited to announce the general availability of worker node auto-scaling in IBM Cloud Kubernetes Service. Natively, Kubernetes is self-healing container orchestration platform that will detect failures from your pods and redeploy that workload. IBM Cloud Kubernetes Service is a self-healing cluster management platform that will automatically recover a failed worker node within your cluster.
The other side of scalability is on the cluster capacity itself. As we’ve mentioned, Kubernetes allows you to define policies to scale up and down the various containerized apps in your architecture, but what happens when the worker nodes run out of capacity? By default, new pods would fail to deploy, potentially causing issues. Now you can configure auto-scaling policies for your worker nodes at the worker node pool level, ensuring you always have the right capacity to meet your apps and customer’s requirements.
Learn more about implementing auto-scaling within your IBM Cloud Kubernetes Service.
What is the Kubernetes Service?
IBM Cloud Kubernetes Service is a managed Kubernetes offering that delivers powerful management tools, an intuitive user experience, and built-in security and isolation to enable rapid delivery of applications, all while leveraging Cloud Services and cognitive capabilities from Watson. IBM Cloud Kubernetes Service provides native Kubernetes capabilities like intelligent scheduling, self-healing, horizontal scaling, service discovery and load balancing, automated rollouts and rollbacks, and secret and configuration management. IBM is also adding capabilities to the Kubernetes Service, including the following:
Container security and isolation choices
The ability to design your own cluster
The option to leverage other IBM Cloud services, such as Watson for your cognitive applications
Completely native Kubernetes CLI and API
Integrated operational tools or support to bring your own tools to ensure operational consistency with other deployments