ACK provides the auto scaling component (cluster-autoscaler) for nodes to automatically scale in and out. Regular instances, GPU-accelerated instances, and preemptible instances can be automatically added to or removed from an ACK cluster based on your requirements. This feature provides multiple scaling modes, supports various instance types and multi-zone instances, and meets your scaling requirements in different scenarios.

How it works

The auto scaling mechanism of Kubernetes is different from the traditional scaling model, which is based on resource usage. This is also the most difficult part when developers migrate workloads from traditional data centers or other orchestration systems (such as Swarm) to Kubernetes.

The traditional scaling model is designed based on resource usage. Assume that a cluster has three nodes. When the CPU usage or memory usage exceeds the scaling threshold, new nodes are added to the cluster. However, you need to consider the following issues when you use the traditional scaling model:

In a Kubernetes cluster, the resource usage of hot nodes is higher than that of the other nodes. In this case, scaling events may not be triggered in time if the average resource usage is used to set the scaling threshold. If the lowest node resource usage is used to set the scaling threshold, the newly added nodes may not be used. This may cause a waste of resources.

In a Kubernetes cluster, a pod is the smallest unit that runs an application. Pods are deployed on different nodes in a Kubernetes cluster. When auto scaling is triggered for a cluster or a node in the cluster, pods with high resource usage are not replicated to scale out the related applications, and the resource limits of these pods are not changed. In this case, the load on the nodes where these pods are deployed is not reduced because the workloads cannot be migrated to newly added nodes.

If the resource usage of a node is used to determine whether to trigger scale-in events for the node, pods with large resource requests and low resource usage may be evicted. If a Kubernetes cluster has a large number of such pods, the resources of the cluster will be fully occupied. As a result, some pods cannot be scheduled.

How does the auto scaling mechanism of Kubernetes fix this issue? The auto scaling mechanism of Kubernetes fixes this issue by decoupling the scheduling of pods from the scaling of computing resources in a cluster.

In simple terms, pod replicas are scaled based on resource usage. This is how pods are scaled. However, when the computing resources are insufficient for scheduling a pod, a scale-out event is triggered. After new nodes are added to the cluster, pending pods are automatically scheduled to these nodes. This way, the load on nodes is reduced. The following section describes the details of the auto scaling mechanism of Kubernetes:

The cluster-autoscaler component triggers scaling events based on the monitoring result of pending pods. A pod becomes pending when computing resources are insufficient for scheduling the pod. In this case, cluster-autoscaler will simulate the scheduling to determine which scaling group can provide the node where the pending pod can be deployed. This way, cluster-autoscaler finds out a scaling group that meets the requirement for scheduling the pending pod. If a scaling group meets the requirement, nodes from this scaling group are added to the cluster.

In simple terms, a scaling group is abstracted into a node in the simulation. Each instance specification of the scaling group corresponds to an equivalent amount of CPU, memory, and GPU resources. The labels and taints of the scaling group are also added to the abstract node. The abstract node is used to simulate the scheduling of the pending pod. If the pending pod can be scheduled to the abstract node, cluster-autoscaler will calculate the number of instances that need to be added from the scaling group to meet the scaling requirement.

Only nodes that are added by scaling events can be removed. The original existing nodes cannot be managed by cluster-autoscaler. Each node is separately evaluated to determine whether scale-in events need to be triggered. If the resource usage of a node drops below the scale-in threshold, the node is evaluated to determine whether it can be removed. In this case, cluster-autoscaler simulates the eviction of all workloads on the node to determine whether the node can be completely drained. If specific pods, such as pods of non-DaemonSet applications and pods that are controlled by PodDisruptionBudgets (PDBs), are deployed in the kube-system namespace of the node, the node will not be drained or removed. Another node without such pods will be scaled-in. A node is drained before it is removed. After pods on the node are evicted to other nodes, the node can be removed.

Different scaling groups represent abstract nodes of different specifications. A scoring mechanism similar to a scheduling policy is designed to select from multiple scaling groups. Abstract nodes are first filtered by the scheduling policy. Then, the abstract nodes that conform to the scheduling policy are selected based on the affinity settings. If no scheduling policy or affinity settings are configured, the least-waste mechanism is used as the default policy for selecting from multiple abstract nodes. The least-waste mechanism selects the scaling group that has the fewest resources after the scaling activity. By default, if a scaling group of CPU instances and another scaling group of GPU-accelerated instances both meet the requirements, the scaling group of CPU-accelerated instances is selected.

The success rate of auto scaling depends on the following conditions:

  • Whether the scaling group conforms to the scheduling policy

    When you create a scaling group, you must know the pod scheduling policies that the scaling group meets. If you do not know the pod scheduling policies that the scaling group meets, you can simulate the scaling activity by using node selectors of the pending pod and the labels of the scaling group.

  • Whether computing resources are sufficient

    After the scaling simulation is complete, an eligible scaling group is used for scaling. However, the scaling fails if the Elastic Compute Service (ECS) specifications of the scaling group are out of stock. To avoid this issue and improve the success rate of auto scaling, you can configure ECS instances of multiple zones and specifications for the scaling group.

  • Method 1: Perform auto scaling in swift mode. After a scaling group has experienced a scale-in event and a scale-out event, the scaling group is in swift mode.
  • Method 2: Use custom images that are developed based on Alibaba Cloud Linux 2 (previously know as Aliyun Linux 2). This allows you to speed up the delivery of Infrastructure as a Service (IaaS) resources by 50%.

Considerations

  • For each account, the default CPU quota of pay-as-you-go instances in each region is 50 vCPUs. You can create up to 48 custom route entries in each route table in a virtual private cloud (VPC). To request a quota increase, submit a ticket.
  • The stock availability of a specific ECS instance type greatly fluctuates. We recommend that you specify multiple instance types for a scaling group. This improves the success rate of auto scaling.
  • In swift mode, when a node is shut down and reclaimed, it stops running and remains in the NotReady state. When a scale-out event is triggered, the state of the node is changed to Ready.
  • When a node is shut down and reclaimed in swift mode, you are charged for only the storage costs of the disk. This rule does not apply to nodes that use local disks, such as the instance type of ecs.d1ne.2xlarge, where the computing costs are also charged. If the stock of the node resources is sufficient, nodes can be launched in a short period of time.
  • If elastic IP addresses (EIPs) are bound to pods, we recommend that you do not delete the scaling group or the ECS nodes that are added from the scaling group in the ECS console. Otherwise, these EIPs cannot be automatically released.

Step 1: Go to the Configure Auto Scaling page

  1. Log on to the ACK console.
  2. In the left-side navigation pane, click Clusters.
  3. On the Clusters page, perform the following steps to go to the Configure Auto Scaling page.
    You can go to the Configure Auto Scaling page in the following ways:
    • Method 1: Find the cluster that you want to manage and choose More > Auto Scaling in the Actions column.
    • Method 2:
      1. Find the cluster that you want to manage and click Details in the Actions column.
      2. In the left-side navigation pane, choose Nodes > Node Pools.
      3. In the upper-right corner of the Node Pools page, click Configure Auto Scaling.

Step 2: Authorization

You need to perform authorization in the following scenarios:

The current account has limited permissions on nodes in the cluster

If the current account has limited permissions on nodes in the cluster, you need to assign the AliyunCSManagedAutoScalerRole Resource Access Management (RAM) role to your account.
Note You only need to perform the authorization once for each Alibaba Cloud account.
  1. Activate Auto Scaling (ESS).
    1. In the dialog box that appears, click the first hyperlink to go to the ESS console.
    2. Click Activate Auto Scaling to go to the Enable Service page.
    3. Select the I agree with Auto Scaling Agreement of Service check box and click Enable Now.
    4. On the Activated page, click Console to go to the ESS console.
    5. Click Go to Authorize to go to the Cloud Resource Access Authorization page, and then authorize ESS to access other cloud resources.
    6. Click Agree to Authorization.
  2. Assign the RAM role.
    1. Click the second hyperlink in the dialog box.
      Note You must log on to the RAM console by using an Alibaba Cloud account.
    2. On the Cloud Resource Access Authorization page, click Agree to Authorization Policy.

The current account has unlimited permissions on nodes in the cluster

  1. Activate ESS.
    1. In the dialog box that appears, click the first hyperlink to go to the ESS console.
    2. Click Activate Auto Scaling to go to the Enable Service page.
    3. Select the I agree with Auto Scaling Agreement of Service check box and click Enable Now.
    4. On the Activated page, click Console to go to the ESS console.
    5. Click Go to Authorize to go to the Cloud Resource Access Authorization page, and then authorize ESS to access other cloud resources.
    6. Click Agree to Authorization.
    If the authorization is successful, you are redirected to the ESS console. Close the page and assign the RAM role.
  2. Assign the RAM role.
    1. Click the second hyperlink in the dialog box to go to the RAM console.
      Note You must log on to the RAM console by using an Alibaba Cloud account.
    2. Click the Permissions tab and click the name of the policy that you want to manage. The details page of the policy appears.
    3. Click Modify Policy Document. The Modify Policy Document panel appears on the right side of the page.
    4. In the Policy Document section, add the following policy content to the Action field and click OK.
      "ess:Describe*", 
"ess:CreateScalingRule", 
"ess:ModifyScalingGroup", 
"ess:RemoveInstances", 
"ess:ExecuteScalingRule", 
"ess:ModifyScalingRule", 
"ess:DeleteScalingRule", 
"ecs:DescribeInstanceTypes",
"ess:DetachInstances",
"vpc:DescribeVSwitches"
      Note Add a comma (,) to the end of the bottom line in the Action field before you add the policy content.

An auto-scaling node pool in the cluster is required to be associated with an EIP

If an auto-scaling node pool in the cluster is required to be associated with an EIP, perform the following steps to grant permissions.

  1. Activate ESS.
    1. In the dialog box that appears, click the first hyperlink to go to the ESS console.
    2. Click Activate Auto Scaling to go to the Enable Service page.
    3. Select the I agree with Auto Scaling Agreement of Service check box and click Enable Now.
    4. On the Activated page, click Console to go to the ESS console.
    5. Click Go to Authorize to go to the Cloud Resource Access Authorization page, and then authorize ESS to access other cloud resources.
    6. Click Agree to Authorization.
    If the authorization is successful, you are redirected to the ESS console. Close the page and assign the RAM role.
  2. Assign the RAM role.
    1. Click the second hyperlink in the dialog box to go to the RAM console.
      Note You must log on to the RAM console by using an Alibaba Cloud account.
    2. Click the Permissions tab and click the name of the policy that you want to manage. The details page of the policy appears.
    3. Click Modify Policy Document. The Modify Policy Document panel appears on the right side of the page.
    4. In the Policy Document section, add the following policy content to the Action field:
      "ess:Describe*", 
"ess:CreateScalingRule", 
"ess:ModifyScalingGroup", 
"ess:RemoveInstances", 
"ess:ExecuteScalingRule", 
"ess:ModifyScalingRule", 
"ess:DeleteScalingRule", 
"ecs:DescribeInstanceTypes",
"ess:DetachInstances",
"vpc:DescribeVSwitches"
"ecs:AllocateEipAddress",
"ecs:AssociateEipAddress",
"ecs:DescribeEipAddresses",
"ecs:DescribeInstanceTypes",
"ecs:DescribeInvocationResults",
"ecs:DescribeInvocations",
"ecs:ReleaseEipAddress",
"ecs:RunCommand",
"ecs:UnassociateEipAddress",
"ess:CompleteLifecycleAction",
"ess:CreateScalingRule",
"ess:DeleteScalingRule",
"ess:Describe*",
"ess:DetachInstances",
"ess:ExecuteScalingRule",
"ess:ModifyScalingGroup",
"ess:ModifyScalingRule",
"ess:RemoveInstances",
"vpc:AllocateEipAddress",
"vpc:AssociateEipAddress",
"vpc:DescribeEipAddresses",
"vpc:DescribeVSwitches",
"vpc:ReleaseEipAddress",
"vpc:UnassociateEipAddress",
"vpc:TagResources"
      Note Add a comma (,) to the end of the bottom line in the Action field before you add the policy content.
    5. On the RAM Roles page, click the name of the RAM role that you want to manage. On the details page of the RAM role, click the Trust Policy Management tab and click Edit Trust Policy. In the Edit Trust Policy panel, add oos.aliyuncs.com to the Service field, as shown in the following figure. Click OK.
      oos

Step 3: Configure auto scaling

  1. On the Configure Auto Scaling page, set the following parameters and click Submit.
    Parameter Description
    Cluster The name of the cluster for which you want to enable auto scaling.
    Scale-in Threshold In a scaling group managed by cluster-autoscaler, the system automatically calculates the ratio of the requested resources to the resource capacity of a node. If the ratio is lower than the threshold, nodes are removed from the ACK cluster.
    Note Scale-out events are automatically triggered based on pod scheduling. Therefore, you only need to configure scale-in rules.
    Defer Scale-in For The amount of time that the cluster must wait before the cluster scales in. Unit: minutes. The default value is 10 minutes.
    Cooldown The nodes that have been added for scale-out can be used for scale-in only after the specified cooldown period.
  2. Select an instance type based on your workload requirements, such as a regular instance, a GPU-accelerated instance, or a preemptible instance. Then, click Create.
  3. In the Auto Scaling Group Configuration dialog box, set the following parameters to create a scaling group.
    Parameter Description
    Region The region where the scaling group is deployed. The scaling group and the cluster must be deployed in the same region. You cannot change the region after the scaling group is created.
    VPC The scaling group and the cluster must be deployed in the same VPC.
    VSwitch The vSwitches of the scaling group. The zone and CIDR block of each vSwitch belong to those of the scaling group.
  4. Configure worker nodes.
    Parameter Description
    Node Type The types of nodes in the scaling group. The node types must be the same as those that are selected when the cluster was created.
    Instance Type The instance types in the scaling group.
    Selected Types The instance types that you have selected. You can select up to 10 instance types.
    System Disk The system disk of the scaling group.
    Mount Data Disk Specifies whether to mount data disks to the scaling group. By default, no data disk is mounted.
    Quantity The number of instances in the scaling group.
    Note
    • Existing instances in the cluster are excluded.
    • By default, the minimum number of instances is 0. If you specify one or more instances, the system will add instances to the scaling group. When a scale-out event is triggered, instances in the scaling group are added to the cluster to which the scaling group is bound.
    Key Pair The key pair that is used to log on to the nodes in the scaling group. You can create key pairs in the ECS console.
    Note You can log on to the nodes only by using key pairs.
    Scaling Mode You can select Standard or Swift.
    RDS Whitelist The Relational Database Service (RDS) instances that can be accessed by nodes that are added by scale-out events.
    Node Label Node labels are automatically added to nodes that are added to the cluster by scale-out events.
    Taints After you add taints to a node, ACK does not schedule pods to the node.
  5. Click OK to create the scaling group.

Check the results

  1. On the Auto Scaling page, you can find the newly created scaling group below Regular Instance.
    Auto Scaling
  2. On the Clusters page, find the cluster that you want to manage and click the name of the cluster or click Details in the Actions column. The details page of the cluster appears.
  3. In the left-side navigation pane of the details page, choose Workloads > Deployments.
  4. On the Deployments tab, select the kube-system namespace. You can find the cluster-autoscaler component. This indicates that the scaling group is created.

FAQ

  • Why does the auto scaling component fail to add nodes after a scale-out event is triggered?
    Check for the following issues:
    • Whether instance types that are configured for the scaling group meet the requested resources of pods. By default, system components are installed for each node. Therefore, the requested resources of pods on a node must be less than the resource capacity of the instance type of the node.
    • Whether you have performed authorization steps as described. You must perform the authorization for each cluster that is involved in the scale-out event.
  • Why does the auto scaling component fail to remove nodes after a scale-in event is triggered?
    Check for the following issues:
    • Whether the ratio of the requested resources to the resource capacity of each node is higher than the scale-in threshold.
    • Whether the pods in the kube-system namespace are running on nodes.
    • Whether the pods are configured with a forcible scheduling policy, which forbids these pods to be scheduled to other nodes.
    • Whether the pods on the nodes are configured with a PodDisruptionBudget and the number of pods has reached the specified minimum value.

    For more frequently asked questions about the auto scaling component, visit the open source community.

  • How does the system select from multiple scaling groups for a scale-out event?

    When pods cannot be scheduled to nodes, the auto scaling component will simulate the scheduling of pods based on the configuration of the scaling group. The configuration includes the labels, taints, and instance specifications. If a scaling group that meets the requirements, this scaling group is selected for the scale-out event. If more than one scaling group meet the requirements, the system chooses the scaling group that has the fewest remaining resources after the simulation.