Google Cloud Platform
Google Container Engine - Kubernetes 1.8 takes advantage of the cloud built for containers
Next week, we will roll out Kubernetes 1.8 to Google Container Engine for early access customers. In addition, we are advancing significant new functionality in Google Cloud to give Container Engine customers a great experience across Kubernetes releases. As a result, Container Engine customers get new features that are only available on Google Cloud Platform, for example highly available clusters, cluster auto-scaling and auto-repair, GPU hardware support, container-native networking and more.
Since we founded Kubernetes back in 2014, Google Cloud has been the leading contributor to the Kubernetes open source project in every release including 1.8. We test, stage and roll out Kubernetes on Google Cloud, and the same team that writes it, supports it, ensuring you receive the latest innovations faster without risk of compatibility breaks or support hassles.
Let’s take a look at the new Google Cloud enhancements that make Kubernetes run so well.
Speed and automationEarlier this week we announced that Google Compute Engine, Container Engine and many other GCP services have moved from per-minute to per-second billing. We also lowered the minimum run charge to one minute from 10 minutes, giving you even finer granularity so you only pay for what you use.
Many of you appreciate how quickly you can spin up a cluster on Container Engine. We’ve made it even faster - improving cluster startup time by 45%, so you’re up and running faster, and better able to take advantage of the pricing minimum-time charge. These improvements also apply to scaling your existing node pools.
A long-standing ask has been high availability masters for Container Engine. We are pleased to announce early access support for high availability, multi-master Container Engine clusters, which increase our SLO to 99.99% uptime. You can elect to run your Kubernetes masters and nodes in up to three zones within a region for additional protection from zonal failures. Container Engine seamlessly shifts load away from failed masters and nodes when needed. Sign up here to try out high availability clusters.
In addition to speed and simplicity, Container Engine automates Kubernetes in production, giving developers choice, and giving operators peace of mind. We offer several powerful Container Engine automation features:
- Node Auto-Repair is in beta and opt-in. Container Engine can automatically repair your nodes using the Kubernetes Node Problem Detector to find common problems and proactively repair nodes and clusters.
- Node Auto-Upgrade is generally available and opt-in. Cluster upgrades are a critical Day 2 task and to give you automation with full control, we now offer Maintenance Windows (beta) to specify when you want Container Engine to auto-upgrade your masters and nodes.
- Custom metrics on the Horizontal Pod Autoscaler will soon be in beta so you can scale your pods on metrics other than CPU utilization. To setup HPA custom metrics on Kubernetes Engine, follow this tutorial.
- Cluster Autoscaling is generally available with performance improvements enabling up to 1,000 nodes, with up to 30 pods in each node as well as letting you specify a minimum and maximum number of nodes for your cluster. This will automatically grows or shrinks your cluster depending on workload demands.
Container-native networking - Container Engine exclusive! - only on GCPContainer Engine now takes better advantage of GCP’s unique, software-defined network with first-class Pod IPs and multi-cluster load balancing.
- Aliased IP support is in beta. With aliased IP support, you can take advantage of several network enhancements and features, including support for connecting Container Engine clusters over a Peered VPC. Aliased IPs are available for new clusters only; support for migrating existing clusters will be added in an upcoming release.
- Multi-cluster ingress will soon be in alpha. You will be able to construct highly available, globally distributed services by easily setting up Google Cloud Load Balancing to serve your end users from the closest Container Engine cluster. To apply for access, please fill out this form.
- Shared VPC support will soon be in alpha. You will be able to create Container Engine clusters on a VPC shared by multiple projects in your cloud organization. To apply for access, please fill out this form.
Machine learning and hardware accelerationMachine learning, data analytics and Kubernetes work especially well together on Google Cloud. Container Engine with GPUs turbocharges compute-intensive applications like machine learning, image processing, artificial intelligence and financial modeling. This release brings you managed CUDA-as-a-Service in containers. Big data is also better on Container Engine with new features that make GCP storage accessible from Spark on Kubernetes.
- NVIDIA Tesla P100 GPUs are available in alpha clusters. In addition to the NVIDIA Tesla K80, you can now create a node with up to 4 NVIDIA P100 GPUs. P100 GPUs can accelerate your workloads by up to 10x compared to the K80! If you are interested in alpha testing your CUDA models in Container Engine, please sign up for the GPU alpha.
- Cloud Storage is now accessible from Spark. Spark on Kubernetes can now communicate with Google BigQuery and Google Cloud Storage as data sources and sinks from Spark using bigdata-interop connectors.
- CronJobs are now in beta so you can now schedule cron jobs such as data processing pipelines to run on a given schedule in your production clusters!
ExtensibilityAs more enterprises use Container Engine, we are actively improving extensibility so you can match Container Engine to your environment and standards.
- Ubuntu node image is now generally available. To offer more flexibility and choice, you can now select Ubuntu as your node image when creating a node. Container Optimized OS (COS) remains our default node image.
- Custom Resource Definition (CRD) is a lightweight way to create API resource types in Kubernetes and make it easy to interact with custom controllers via kubectl. Please note that CRD replaces the now deprecated Third Party Resource (TPR) object.
Security and reliabilityWe designed Container Engine with enterprise security and reliability in mind. This release adds several new enhancements.
- Role Based Access Control (RBAC) is now generally available. This feature allows a cluster administrator to specify fine-grained policies describing which users, groups, and service accounts are allowed to perform which operations on which API resources.
- Network Policy Enforcement using Calico is in beta. Starting from Kubernetes 1.7.6, you can help secure your Container Engine cluster with network policy pod-to-pod ingress rules. Kubernetes 1.8 adds additional support for CIDR-based rules, allowing you to whitelist access to resources outside of your Kubernetes cluster (e.g., VMs, hosted services, and even public services), so that you can integrate your Kubernetes application with other IT services and investments. Additionally, you can also now specify pod-to-pod egress rules, providing tighter controls needed to ensure service integrity. Learn more about here.
- Node Allocatable is generally available. Container Engine includes the Kubernetes Node Allocatable feature for more accurate resource management, providing higher node stability and reliability by protecting node components from out-of-resource issues.
- Priority / Preemption is in alpha clusters. Container Engine implements Kubernetes Priority and Preemption so you can associate priority pods to priority levels such that you can preempt lower-priority pods to make room for higher-priority ones when you have more workloads ready to run on the cluster than there are resources available.
Enterprise-ready container operations - monitoring and management designed for KubernetesIn Kubernetes 1.7, we added view-only workload, networking, and storage views to the Container Engine user interface. In 1.8, we display even more information, enable more operational and development tasks without having to leave the UI, and improve integration with Stackdriver and Cloud Shell.
The following features are all generally available:
- Easier configurations: You can now view and edit your YAML files directly in the UI. We also added easy to use shortcuts for the most common user actions like rolling updates or scaling a deployment.
- Node information details: Cluster view now shows details such as node health status, relevant logs, and pod information so you can easily troubleshoot your clusters and nodes.
- Stackdriver Monitoring integration: Your workload views now include charts showing CPU, memory, and disk usage. We also link to corresponding Stackdriver pages for a more in-depth look.
- Cloud Shell integration: You can now generate and execute exact kubectl commands directly in the browser. No need to manually switch context between multiple clusters and namespaces or copy and paste. Just hit enter!
- Cluster recommendations: Recommendations in cluster views suggest ways that you can improve your cluster, for example, turning on autoscaling for underutilized clusters or upgrading nodes for version alignment.
Container Engine everywhereContainer Engine customers are global. To keep up with demand, we’ve expanded our global capacity to include our latest GCP regions: Frankfurt (europe-west3), Northern Virginia (us-east4) and São Paulo (southamerica-east1). With these new regions Container Engine is now available in a dozen locations around the world, from Oregon to Belgium to Sydney.
Customers of all sizes have been benefiting from containerizing their applications and running them on Container Engine. Here are a couple of recent examples:
Mixpanel, a popular product analytics company, processes 5 trillion data points every year. To keep performance high, Mixpanel uses Container Engine to automatically scale resources.
All of our applications and our primary database now run on Google Container Engine. Container Engine gives us elasticity and scalable performance for our Kubernetes clusters. It’s fully supported and managed by Google, which makes it more attractive to us than elastic container services from other cloud providers.
What it comes down to for us is speed-to-market and cost. With Google Cloud Platform, we can confidently release services multiple times a day and launch new markets in a day. We’ve also reduced our cloud hosting costs by 50% by moving to microservices on Google Container Engine.
Bitnami, an application package and deployment platform, shows you how to use Container Engine networking features to create a private Kubernetes cluster that enforces service privacy so that your services are available internally but not to the outside world.