Merge pull request #280140 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: Taojunshen

articles/aks/advanced-network-observability-cli.md

@@ -585,7 +585,7 @@ rm hubble-linux-${HUBBLE_ARCH}.tar.gz{,.sha256sum}
 1. Set up port forwarding for Hubble UI using the `kubectl port-forward` command.
 
     ```azurecli-interactive
-    kubectl port-forward svc/hubble-ui 12000:80
+    kubectl -n kube-system port-forward svc/hubble-ui 12000:80
     ```
 
 1. Access Hubble UI by entering `http://localhost:12000/` into your web browser.

articles/aks/cluster-autoscaler-overview.md

@@ -11,7 +11,7 @@ ms.author: schaffererin
 
 # Cluster autoscaling in Azure Kubernetes Service (AKS) overview
 
-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.
+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 unscheduled pods, it scales up the number of nodes in the node pool to meet the application demand. It also regularly checks nodes that don't have any scheduled 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](./cluster-autoscaler.md).
 
@@ -25,7 +25,7 @@ Clusters often need a way to scale automatically to adjust to changing applicati
 
 :::image type="content" source="media/cluster-autoscaler/cluster-autoscaler.png" alt-text="Screenshot of how the cluster autoscaler and horizontal pod autoscaler often work together to support the required application demands.":::
 
-It's a common practice to enable cluster autoscaler for nodes and either the Vertical Pod Autoscaler or Horizontal Pod Autoscaler for pods. When you enable the cluster autoscaler, it applies the specified scaling rules when the node pool size is lower than the minimum or greater than the maximum. The cluster autoscaler waits to take effect until a new node is needed in the node pool or until a node might be safely deleted from the current node pool. For more information, see [How does scale down work?](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-scale-down-work)
+It's a common practice to enable cluster autoscaler for nodes and either the Vertical Pod Autoscaler or Horizontal Pod Autoscaler for pods. When you enable the cluster autoscaler, it applies the specified scaling rules when the node pool size is lower than the minimum node count, up to the maximum node count. The cluster autoscaler waits to take effect until a new node is needed in the node pool or until a node might be safely deleted from the current node pool. For more information, see [How does scale down work?](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-scale-down-work)
 
 ## Best practices and considerations
 
@@ -34,7 +34,7 @@ It's a common practice to enable cluster autoscaler for nodes and either the Ver
 * To **effectively run workloads concurrently on both Spot and Fixed node pools**, consider using [*priority expanders*](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-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](./operator-best-practices-advanced-scheduler.md#node-affinity)/[expanders](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) or using [PriorityClass](https://kubernetes.io/docs/concepts/scheduling-eviction/pod-priority-preemption/#priorityclass) to help prevent unnecessary node drain or scale down operations.
-* In an autoscaler-enabled node pool, scale down nodes by removing workloads, instead of manually reducing the node count. This can be problematic if the node pool is already at maximum capacity or if there are active workloads running on the nodes, potentially causing unexpected behavior by the cluster autoscaler 
+* In an autoscaler-enabled node pool, scale down nodes by removing workloads, instead of manually reducing the node count. This can be problematic if the node pool is already at maximum capacity or if there are active workloads running on the nodes, potentially causing unexpected behavior by the cluster autoscaler.
 * Nodes don't scale up if pods have a PriorityClass value below -10. Priority -10 is reserved for [overprovisioning pods](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-can-i-configure-overprovisioning-with-cluster-autoscaler). For more information, see [Using the cluster autoscaler with Pod Priority and Preemption](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-cluster-autoscaler-work-with-pod-priority-and-preemption).
 * **Don't combine other node autoscaling mechanisms**, such as Virtual Machine Scale Set autoscalers, with the cluster autoscaler.
 * The cluster autoscaler **might be unable to scale down if pods can't move, such as in the following situations**:
@@ -43,7 +43,8 @@ It's a common practice to enable cluster autoscaler for nodes and either the Ver
   * A pod uses node selectors or anti-affinity that can't be honored if scheduled on a different node.
     For more information, see [What types of pods can prevent the cluster autoscaler from removing a node?](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-types-of-pods-can-prevent-ca-from-removing-a-node).
 >[!IMPORTANT]
-> **Do not make changes to individual nodes within the autoscaled node pools**. All nodes in the same node group should have uniform capacity, labels, taints and system pods running on them. 
+> **Don't make changes to individual nodes within the autoscaled node pools**. All nodes in the same node group should have uniform capacity, labels, taints and system pods running on them.
+* The cluster autoscaler isn't responsible for enforcing a "maximum node count" in a cluster node pool irrespective of pod scheduling considerations. If any non-cluster autoscaler actor sets the node pool count to a number beyond the cluster autoscaler's configured maximum, the cluster autoscaler doesn't automatically remove nodes. The cluster autoscaler scale down behaviors remain scoped to removing only nodes that have no scheduled pods. The sole purpose of the cluster autoscaler's max node count configuration is to enforce an upper limit for scale up operations. It doesn't have any effect on scale down considerations.
 
 ## Cluster autoscaler profile
 
@@ -57,7 +58,7 @@ It's important to note that the cluster autoscaler profile settings are cluster-
 
 #### 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. Also increase `ok-total-unready-count`and `max-total-unready-percentage`. 
+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. Also increase `ok-total-unready-count`and `max-total-unready-percentage`.
 
 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. See [profile for bursty workloads](./cluster-autoscaler.md#configure-cluster-autoscaler-profile-for-bursty-workloads)
 
@@ -69,7 +70,7 @@ If you want a [cost-optimized profile](./cluster-autoscaler.md#configure-cluster
 * 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.
 * Set `skip-nodes-with-local-storage` to false.
-* Increase `ok-total-unready-count`and `max-total-unready-percentage` 
+* Increase `ok-total-unready-count`and `max-total-unready-percentage`.
 
 ## Common issues and mitigation recommendations
 View scaling failures and scale-up not triggered events via [CLI or Portal](./cluster-autoscaler.md#retrieve-cluster-autoscaler-logs-and-status).
@@ -113,4 +114,3 @@ Depending on how long the scaling operations have been experiencing failures, it
 <!-- LINKS --->
 [vertical-pod-autoscaler]: vertical-pod-autoscaler.md
 [horizontal-pod-autoscaler]:concepts-scale.md#horizontal-pod-autoscaler
-