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What is AKS cluster?
Aug 1, 2024 · Cluster modes. In AKS, you can create a cluster with the Automatic (preview) or Standard mode. AKS Automatic provides a more fully managed experience, managing cluster configuration, including nodes, scaling, security, and other preconfigured settings.
- What is Azure Kubernetes Service (AKS)? - learn.microsoft.com
Overview of AKS. AKS reduces the complexity and operational...
- Public and Private AKS Clusters Demystified - Microsoft ...
Azure Kubernetes Service (AKS) is the managed kubernetes...
- Cluster autoscaling in Azure Kubernetes Service (AKS) overview
If you want to enable, disable, or update the cluster...
- What is Azure Kubernetes Service (AKS)? - learn.microsoft.com
Overview of AKS. AKS reduces the complexity and operational overhead of managing Kubernetes by shifting that responsibility to Azure. When you create an AKS cluster, Azure automatically creates and configures a control plane for you at no cost.
Jan 16, 2023 · Azure Kubernetes Service (AKS) is the managed kubernetes service in Azure. It has two main components: worker nodes and control plane. The worker nodes are the VMs where customer applications will be deployed into. The control plane is the component that manages the applications and the worker nodes.
Feb 12, 2024 · The AKS control plane serves as the Kubernetes cluster’s brain, managing its state, scheduling workloads, and ensuring that the desired configuration is maintained.
- Overview
- About the cluster autoscaler
- Best practices and considerations
- Cluster autoscaler profile
- Common issues and mitigation recommendations
To keep up with application demands in Azure Kubernetes Service (AKS), you might need to adjust the number of nodes that run your workloads. The cluster autoscaler component watches for pods in your cluster that can't be scheduled because of resource constraints. When the cluster autoscaler detects issues, it scales up the number of nodes in the node pool to meet the application demand. It also regularly checks nodes for a lack of running pods and scales down the number of nodes as needed.
This article helps you understand how the cluster autoscaler works in AKS. It also provides guidance, best practices, and considerations when configuring the cluster autoscaler for your AKS workloads. If you want to enable, disable, or update the cluster autoscaler for your AKS workloads, see Use the cluster autoscaler in AKS.
Clusters often need a way to scale automatically to adjust to changing application demands, such as between workdays and evenings or weekends. AKS clusters can scale in the following ways:
•The cluster autoscaler periodically checks for pods that can't be scheduled on nodes because of resource constraints. The cluster then automatically increases the number of nodes. Manual scaling is disabled when you use the cluster autoscaler. For more information, see How does scale up work?.
•The Horizontal Pod Autoscaler uses the Metrics Server in a Kubernetes cluster to monitor the resource demand of pods. If an application needs more resources, the number of pods is automatically increased to meet the demand.
•The Vertical Pod Autoscaler automatically sets resource requests and limits on containers per workload based on past usage to ensure pods are scheduled onto nodes that have the required CPU and memory resources.
•When implementing availability zones with the cluster autoscaler, we recommend using a single node pool for each zone. You can set the --balance-similar-node-groups parameter to True to maintain a balanced distribution of nodes across zones for your workloads during scale up operations. When this approach isn't implemented, scale down operations can disrupt the balance of nodes across zones.
•For clusters with more than 400 nodes, we recommend using Azure CNI or Azure CNI Overlay.
•To effectively run workloads concurrently on both Spot and Fixed node pools, consider using priority expanders. This approach allows you to schedule pods based on the priority of the node pool.
•Exercise caution when assigning CPU/Memory requests on pods. The cluster autoscaler scales up based on pending pods rather than CPU/Memory pressure on nodes.
•For clusters concurrently hosting both long-running workloads, like web apps, and short/bursty job workloads, we recommend separating them into distinct node pools with Affinity Rules/expanders or using PriorityClass to help prevent unnecessary node drain or scale down operations.
•We don't recommend making direct changes to nodes in autoscaled node pools. All nodes in the same node group should have uniform capacity, labels, and system pods running on them.
Optimizing the cluster autoscaler profile
You should fine-tune the cluster autoscaler profile settings according to your specific workload scenarios while also considering tradeoffs between performance and cost. This section provides examples that demonstrate those tradeoffs. It's important to note that the cluster autoscaler profile settings are cluster-wide and applied to all autoscale-enabled node pools. Any scaling actions that take place in one node pool can affect the autoscaling behavior of other node pools, which can lead to unexpected results. Make sure you apply consistent and synchronized profile configurations across all relevant node pools to ensure you get your desired results. Example 1: Optimizing for performance For clusters that handle substantial and bursty workloads with a primary focus on performance, we recommend increasing the scan-interval and decreasing the scale-down-utilization-threshold. These settings help batch multiple scaling operations into a single call, optimizing scaling time and the utilization of compute read/write quotas. It also helps mitigate the risk of swift scale down operations on underutilized nodes, enhancing the pod scheduling efficiency. For clusters with daemonset pods, we recommend setting ignore-daemonset-utilization to true, which effectively ignores node utilization by daemonset pods and minimizes unnecessary scale down operations. Example 2: Optimizing for cost If you want a cost-optimized profile, we recommend setting the following parameter configurations: •Reduce scale-down-unneeded-time, which is the amount of time a node should be unneeded before it's eligible for scale down. •Reduce scale-down-delay-after-add, which is the amount of time to wait after a node is added before considering it for scale down. •Increase scale-down-utilization-threshold, which is the utilization threshold for removing nodes. •Increase max-empty-bulk-delete, which is the maximum number of nodes that can be deleted in a single call. Example 1: Optimizing for performance For clusters that handle substantial and bursty workloads with a primary focus on performance, we recommend increasing the scan-interval and decreasing the scale-down-utilization-threshold. These settings help batch multiple scaling operations into a single call, optimizing scaling time and the utilization of compute read/write quotas. It also helps mitigate the risk of swift scale down operations on underutilized nodes, enhancing the pod scheduling efficiency. For clusters with daemonset pods, we recommend setting ignore-daemonset-utilization to true, which effectively ignores node utilization by daemonset pods and minimizes unnecessary scale down operations.
Not triggering scale up operationsScale up operation failuresScale down operation failuresOther issuesNode pool in backoffNode pool in backoff was introduced in version 0.6.2 and causes the cluster autoscaler to back off from scaling a node pool after a failure. Depending on how long the scaling operations have been experiencing failures, it may take up to 30 minutes before making another attempt. You can reset the node pool's backoff state by disabling and then re-enabling autoscaling.Nov 28, 2022 · An AKS cluster is primarily represented by two components: the cluster itself with a particular version of Kubernetes and the resource group in which the cluster can be placed. An AKS cluster can be a single resource in a resource group or part of a shared resource group. Related: Discover How to Use and Manage Azure Portal.
Nov 11, 2023 · Azure Kubernetes Service is a managed Kubernetes service that makes building, deploying, and upgrading clusters easier. You also get basic views into your AKS clusters.
