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Autopilot mode for Google Kubernetes Engine (GKE) is our take on a fully managed, Pod-based Kubernetes platform. It provides category-defining features with a fully functional Kubernetes API with support for StatefulSet with block storage, GPU and other critical functionality that you don’t often found in nodeless/serverless-style Kubernetes offerings, while still offering a Pod-level SLA and a super-simple developer API. But until now, Autopilot, like other products in this category, did not offer the ability to temporarily burst CPU and memory resources beyond what was requested by the workload. I’m happy to announce that now, powered by the unique design of GKE Autopilot on Google Cloud, we are able to bring burstable workload support to GKE Autopilot. Bursting allows your Pod to temporarily utilize resources outside of those resources that it requests and is billed for. How does this work, and how can Autopilot offer burstable support, given the Pod-based model? The key is that in Autopilot mode we still group Pods together on Nodes. This is what powers several unique features of Autopilot such as our flexible Pod sizes. With this change, the capacity of your Pods is pooled, and Pods that set a limit higher than their requests can temporarily burst into this capacity (if it’s available). With the introduction of burstable support, we’re also introducing another groundbreaking change: 50m CPU Pods — that is, Pods as small as 1/20th of a vCPU. Until now, the smallest Pod we offered was ¼ of a vCPU (250m CPU) — five times bigger. Combined with burst, the door is now open to run high-density-yet-small workloads on Autopilot, without constraining each Pod to its resource requests. We’re also dropping the 250m CPU resource increment, so you can create any size of Pod you like between the minimum to the maximum size, for example, 59m CPU, 302m, 808m, 7682m etc (the memory to CPU ratio is automatically kept within a supported range, sizing up if needed). With these finer-grained increments, Vertical Pod Autoscaling now works even better, helping you tune your workload sizes automatically. If you want to add a little more automation to figuring out the right resource requirements, give Vertical Pod Autoscaling a try! Here’s what our customer Ubie had to say: “GKE Autopilot frees us from managing infrastructure so we can focus on developing and deploying our applications. It eliminates the challenges of node optimization, a critical benefit in our fast-paced startup environment. Autopilot's new bursting feature and lowered CPU minimums offer cost optimization and support multi-container architectures such as sidecars. We were already running many workloads in Autopilot mode, and with these new features, we're excited to migrate our remaining clusters to Autopilot mode.” - Jun Sakata, Head of Platform Engineering, Ubie A new home for high-density workloadsAutopilot is now a great place to run high-density workloads. Let’s say for example you have a multi-tenant architecture with many smaller replicas of a Pod, each running a website that receives relatively little traffic, but has the occasional spike. Combining the new burstable feature and the lower 50m CPU minimum, you can now deploy thousands of these Pods in a cost-effective manner (up to 20 per vCPU), while enabling burst so that when that traffic spike comes in, that Pod can temporarily burst into the pooled capacity of these replicas. What’s even better is that you don’t need to concern yourself with bin-packing these workloads, or the problem where you may have a large node that’s underutilized (for example, a 32 core node running a single 50m CPU Pod). Autopilot takes care of all of that for you, so you can focus on what’s key to your business: building great services. Calling all startups, students, and solopreneursEveryone needs to start somewhere, and if Autopilot wasn’t the perfect home for your workload before, we think it is now. With the 50m CPU minimum size, you can run individual containers in us-central1 for under $2/month each (50m CPU, 50MiB). And thanks to burst, these containers can use a little extra CPU when traffic spikes, so they’re not completely constrained to that low size. And if this workload isn’t mission-critical, you can even run it in Spot mode, where the price is even lower. In fact, thanks to GKE’s free tier, your costs in us-central1 can be as low as $30/month for small workloads (including production-grade load balancing) while tapping into the same world-class infrastructure that powers some of the biggest sites on the internet today. Importantly, if your startup grows, you can scale in-place without needing to migrate — since you’re already running on a production-grade Kubernetes cluster. So you can start small, while being confident that there is nothing limiting about your operating environment. We’re counting on your success as well, so good luck! And if you’re learning GKE or Kubernetes, this is a great platform to learn on. Nothing beats learning on real production systems — after all, that’s what you’ll be using on the job. With one free cluster per account, and all these new features, Autopilot is a fantastic learning environment. Plus, if you delete your Kubernetes workload and networking resources when you’re done for the day, any associated costs cease as well. When you’re ready to resume, just create those resources again, and you’re back at it. You can even persist state between learning sessions by deleting just the compute resources (and keeping the persistent disks). Don’t forget there’s a $300 free trial to cover your initial usage as well! Next steps: Learn about Pod bursting in GKE.Read more about the minimum and maximum resource requests in Autopilot mode.Learn how to set resource requirements automatically with VPA.Try GKE’s Autopilot mode for a workload-based API and simpler Day 2 ops with lower TCO.Going to Next ‘24? Check out session DEV224 to hear Ubie talk about how it uses burstable workloads in GKE. View the full article

As more customers use multiple cloud services or microservices, they face the difficulty of consistently managing and connecting their services across various environments, including on-premises, different clouds, and existing legacy systems. HashiCorp Consul's service mesh addresses this challenge by securely and consistently connecting applications on any runtime, network, cloud platform, or on-premises setup. In the Google Cloud ecosystem, Consul can be deployed across Google Kubernetes Engine (GKE) and Anthos GKE. Now, Consul 1.16 is also supported on GKE Autopilot, Google Cloud’s fully managed Kubernetes platform for containerized workloads. Consul 1.17 is currently on track to be supported on GKE Autopilot later this year. Benefits of GKE Autopilot In 2021, Google Cloud introduced GKE Autopilot, a streamlined configuration for Kubernetes that follows GKE best practices, with Google managing the cluster configuration. Reducing the complexity that comes with workloads using Kubernetes, Google’s GKE Autopilot simplifies operations by managing infrastructure, control plane, and nodes, while reducing operational and maintenance costs. Consul is the latest partner product to be generally available, fleet-wide, on GKE Autopilot. By deploying Consul on GKE Autopilot, customers can connect services and applications across clouds, platforms, and services while realizing the benefits of a simplified Kubernetes experience. The key benefits of using Autopilot include more time to focus on building your application, a strong security posture out-of-the-box, and reduced pricing — paying only for what you use: Focus on building and deploying your applications: With Autopilot, Google manages the infrastructure using best practices for GKE. Using Consul, customers can optimize operations through centralized management and automation, saving valuable time and resources for developers. Out-of-the-box security: With years of Kubernetes experience, GKE Autopilot implements GKE-hardening guidelines and security best practices, while blocking features deemed less safe (i.e. privileged pod- and host-level access). As a part of HashiCorp’s zero trust security solution, Consul enables least-privileged access by using identity-based authorization and service-to-service encryption. Pay-as-you-go: GKE Autopilot’s pricing model simplifies billing forecasts and attribution because it's based on resources requested by your pods. Visit the Google Cloud and HashiCorp websites to learn more about GKE Autopilot pricing and HashiCorp Consul pricing. Deploying Consul on GKE Autopilot Deploying Consul on GKE Autopilot facilitates service networking across a multi-cloud environment or microservices architecture, allowing customers to quickly and securely deploy and manage Kubernetes clusters. With Consul integrated across Google Cloud Kubernetes, including GKE, GKE Autopilot, and Anthos GKE, Consul helps bolster application resilience, increase uptime, accelerate application deployment, and improve security across service-to-service communications for clusters, while reducing overall operational load. Today, you can deploy Consul service mesh on GKE Autopilot using the following configuration for Helm in your values.yaml file: global: name: consul connectInject: enabled: true cni: enabled: true logLevel: info cniBinDir: "/home/kubernetes/bin" cniNetDir: "/etc/cni/net.d"In addition, if you are using a Consul API gateway for north-south traffic, you will need to configure the Helm chart so you can leverage the existing Kubernetes Gateway API resources provided by default when provisioning GKE Autopilot. We recommend the configuration shown below for most deployments on GKE Autopilot as it provides the greatest flexibility by allowing both API gateway and service mesh workflows. Refer to Install Consul on GKE Autopilot for more information. global: name: consul connectInject: enabled: true apiGateway: manageExternalCRDs: false manageNonStandardCRDs: true cni: enabled: true logLevel: info cniBinDir: "/home/kubernetes/bin" cniNetDir: "/etc/cni/net.d"Learn more You can learn more about the process that Google Cloud uses to support HashiCorp Consul workloads on GKE Autopilot clusters with this GKE documentation and resources page. Here’s how to get started on Consul: Learn more in the Consul documentation. Begin using Consul 1.16 by installing the latest Helm chart, and learn how to use a multi-port service in Consul on Kubernetes deployments. Try Consul Enterprise by starting a free trial. Sign up for HashiCorp-managed HCP Consul. View the full article