The control plane of an ACK Pro cluster uses an auto scaling architecture. In ultra-large clusters or scenarios with sudden high-concurrency traffic, the response latency of auto scaling can affect business continuity. The ACK Pro preset control plane eliminates this uncertainty by pre-allocating and dedicating control plane resources, ensuring that API request concurrency and pod scheduling capacity remain at a high, predictable level. This feature is ideal for AI training and inference, ultra-large clusters, and mission-critical workloads.
ACK Pro clusters can dynamically adjust the resource configurations of control plane components like the API Server and etcd based on the actual cluster load. This is suitable for most common scenarios. However, for ultra-large clusters or in scenarios with sudden high-concurrency traffic, the response latency of auto scaling can affect business continuity. For example, when you start a large number of pods in batches for large-scale AI training, you may encounter scheduling waits or even invalid pods. During peak hours, slow API responses can impact business continuity.
The ACK Pro preset control plane eliminates the uncertainty of auto scaling by dedicating control plane resources and establishing a baseline configuration for the API Server. Instead of relying on reactive mechanisms to catch up, this approach ensures that control plane performance is always predictable.
The ACK Pro preset control plane runs in parallel with the standard ACK Pro control plane. Introducing the preset mode does not affect the operation of existing clusters.
Use cases
AI training and inference
Large-scale AI training jobs can involve the concurrent creation and scheduling of tens of thousands of pods, and they often require computation to start immediately after scheduling. The auto scaling latency of a standard control plane can result in invalid pods or interrupt the training process. A preset control plane ensures that the required scheduling capacity is ready the moment training starts.
Large-scale cluster deployments
For scenarios that consistently handle high API request concurrency, such as agent-based applications and microservices clusters, a preset control plane simplifies the O&M of large clusters through standardized specifications.
Mission-critical web applications
For latency-sensitive core services, control plane response latency can directly affect service availability. A preset control plane provides deterministic API request concurrency and pod scheduling rates to ensure business continuity.
Levels
Level specifications
The preset control plane is available in three levels. Each level defines the control plane capacity based on three core metrics.
The following table lists the upper limits of the control plane capacity. The actual performance also depends on your workload patterns, configurations, and adherence to Kubernetes best practices. We recommend that you configure your clusters and workloads according to our best practices.
Access to the Pro 4XL level requires an allowlist. Contact customer support to request access.
Performance metric | Pro XL | Pro 2XL | Pro 4XL |
API request concurrency (Seats)① | 3,900 | 7,800 | 15,600 |
pod scheduling rate (pods/second)② | 200 | 400 | 600 |
etcd database size (GB)③ | 16 | 16 | 16 |
Metric descriptions:
① API request concurrency (Seats): Measures the maximum concurrent request processing capacity of the API Server, which is expressed in Seats. The preset control plane ensures consistent request handling capacity in high-concurrency scenarios by dedicating baseline resources to the API Server.
② pod scheduling rate (pods/second): The rate at which the cluster scheduler assigns pods to nodes. The preset control plane guarantees a predictable scheduling rate through a fixed resource quota.
③ etcd database size: The capacity of the key-value database that stores the entire state of the cluster. The preset control plane uses a dedicated etcd architecture (a standard Pro cluster shares 8 GB). Some levels also support separate storage for Events data to prevent high-frequency event writes from affecting core state operations.
Level switching rules
You can upgrade or downgrade between Pro, Pro XL, Pro 2XL, and Pro 4XL levels, such as from Pro to Pro XL or from Pro XL to Pro 2XL.
To enable the Pro 4XL level, contact customer support to have your account added to the allowlist.
You cannot switch to an ACK Basic cluster.
Automatic level switching is not supported. Once a level is selected, the control plane operates at that fixed specification to ensure predictable performance. To adjust the capacity for your workload, you must monitor control plane metrics and manually upgrade or downgrade the level.
Standard control plane vs. preset control plane
Dimension | Standard ACK Pro control plane | ACK Pro preset control plane |
control plane resource configuration | Auto scaling, dynamically adjusted by load | Pre-allocated fixed capacity, always sufficient |
API concurrency guarantee | Dynamic, capped at the Pro XL level | Each level has a defined Seats specification |
pod scheduling rate guarantee | Dynamic, capped at the Pro XL level | Each level has a defined pods/second value |
etcd configuration | 8 GB | 16 GB |
billing method | resource plan or pay-as-you-go | pay-as-you-go only |
Use cases | General workloads that can tolerate auto scaling latency | Large-scale, high-concurrency workloads that require predictable performance |
Billing
The preset control plane uses the same billing logic as an ACK Pro cluster.
Billing occurs on the hour. At the start of each hour, the system bills you based on the current control plane level of your cluster.
After you switch levels, the new billing rate takes effect from the beginning of the hour in which the switch occurred. For example, if you switch from Pro to Pro XL at 10:20, you are billed at the Pro XL rate for the entire 10:00 to 11:00 billing cycle.
Only the pay-as-you-go billing method is supported. Existing prepaid resource plans for an ACK Pro cluster apply only to standard ACK Pro clusters and not to the new preset control plane levels.
For more information, see Cluster management fees.
Capacity planning and metric monitoring
To decide whether to switch levels and select the appropriate capacity, we recommend monitoring the following dashboards.
Core capacity metrics
On the Cluster Information page of your cluster, go to the Cluster Monitoring tab. Focus on the following capacity metrics and compare them with the preset control plane Level specifications to select the right level:
API request concurrency: Corresponds to the Seats specification for each level and reflects the concurrent processing pressure on the API Server.
Pod scheduling rate: Observe the number of pod scheduling requests processed per second to determine if the scheduler is a bottleneck.
Database size: Monitor the etcd storage usage to ensure the data volume is within the safe limits of the current level.
Resource usage
In the left-side navigation pane of the cluster details page, choose .
Go to the Key Component Monitoring tab and then switch between the component tabs below to view details. We recommend that you monitor the memory and CPU resource usage of the API Server, etcd, and scheduler components. If the usage level remains in the
highstate (utilization ≥ 80%) for a sustained period, it indicates that the current control plane resources are under pressure. Upgrade your cluster to a higher level promptly.For detailed metric descriptions, see the related documentation.
Usage notes
To find the optimal level for your cluster, switch to a higher level, run a stress test that simulates peak loads, and observe the control plane's resource usage. Then, select the level that best fits the performance data.
Rollback limitation: The etcd database of a standard ACK Pro control plane has a capacity limit of 8 GB. If the etcd database usage exceeds 8 GB while your cluster is running in preset control plane mode, you must first reduce the database size to below 8 GB before you can revert to the standard ACK Pro control plane.
Data plane component scaling: After you enable the preset control plane, we recommend that you also monitor the resource usage of data plane components, such as CoreDNS and metrics-server. Scale these components appropriately based on the actual load to prevent the data plane from becoming a performance bottleneck.
Get started
You can configure a preset control plane level when you create a new cluster or for an existing one.
To upgrade a cluster, see Upgrade a cluster.
New cluster
By default, the creation process is the same as for an ACK Pro cluster. You must explicitly select a preset control plane level to enable this feature.
Log on to the Container Service for Kubernetes (ACK) console. In the left-side navigation pane, choose Clusters and then click Create Kubernetes Cluster.
For more information, see Create an ACK Pro cluster.
In the Cluster Configurations step, set Cluster Specification to a preset control plane level (Pro XL, Pro 2XL, or Pro 4XL) as prompted.
After the cluster is created, the control plane runs with the specifications of the selected level.
Existing cluster
For an existing ACK Pro cluster, check its current resource usage on the control plane component monitoring dashboard. You can then upgrade the level based on resource usage alerts or in preparation for peak traffic.
On the ACK Clusters page, click the name of your cluster. In the left navigation pane, click Cluster Information.
Click the Basic Information tab. In the Basic Information section, find the Cluster Specification area and follow the prompts to enable or configure a preset control plane level.
References
Service Level Agreement (SLA):
The preset control plane feature adds new performance levels to ACK Pro clusters. These levels offer the same service guarantees as a standard ACK Pro cluster. For more information, see Alibaba Cloud Container Service for Kubernetes Service Level Agreement.
Control plane component monitoring: