By Palak Dalal, Product Manager
We’re excited to announce that Kubernetes version 1.9 will be available on Google Kubernetes Engine next week in our early access program. This release includes greater support for stateful and stateless applications, hardware accelerator support for machine learning workloads and storage enhancements. Overall, this release achieves a big milestone in making it easy to run a wide variety of production-ready applications on Kubernetes without having to worry about the underlying infrastructure.
Google is the leading contributor to open-source Kubernetes releases and now you can access the latest Kubernetes release on our fully-managed Kubernetes Engine, and let us take care of managing, scaling, upgrading, backing up and helping to secure your clusters. Further, we recently simplified our pricing by removing the fee for cluster management, resulting in real dollar savings for your environment.
We’re committed to providing the latest technological innovation to Kubernetes users with one new release every quarter. Let’s a take a closer look at the key enhancements in Kubernetes 1.9.
The core Workloads APIs (DaemonSet, Deployment, ReplicaSet and StatefulSet), which let you run stateful and stateless workloads in Kubernetes 1.9, move to general availability (GA) in this release, delivering production-grade quality, support and long-term backwards compatibility.
Google Cloud Platform (GCP) provides a great environment for running machine learning and data analytics workloads in containers. With this release, we’ve improved support for hardware accelerators such as NVIDIA Tesla P100 and K80 GPUs. Compute-intensive workloads will benefit greatly from cost-effective and high performance GPUs for many use cases ranging from genomics and computational finance to recommendation systems and simulations.
Improvements to the Kubernetes scheduler in this release make it easier to use local storage in Kubernetes. The local persistent storage feature (alpha) enables easy access to local SSD on GCP through Kubernetes’ standard PVC (Persistent Volume Claim) interface in a simple and portable way. This allows you to take an existing Helm Chart, or StatefulSet spec using remote PVCs, and easily switch to local storage by just changing the StorageClass name. Local SSD offers superior performance including high input/output operations per second (IOPS), low latency, and is ideal for high performance workloads, distributed databases, distributed file systems and other stateful workloads.
This Kubernetes release introduces an alpha implementation of Container Storage Interface (CSI). We’ve been working with the Kubernetes community to provide a single and consistent interface for different storage providers. CSI makes it easy to add different storage volume plugins in Kubernetes without requiring changes to the core codebase. CSI underscores our commitment to being open, flexible and collaborative while providing maximum value—and options—to our users.
In a few days, you can access the latest Kubernetes Engine release in your alpha clusters by joining our early access program.