Container Service for Kubernetes:Configure the custom parameters of kube-scheduler

Prerequisites

An ACK Pro cluster, ACK Edge Pro cluster, or ACK Lingjun cluster that runs Kubernetes 1.20 or later is created. For more information, see Create an ACK managed cluster.

ACK Serverless Pro clusters in which kube-scheduler is installed also support custom kube-scheduler parameters. Only users in the whitelist can create ACK Serverless Pro clusters. For more information, see Create an ACK Serverless cluster.

Limits

Procedure

1. Log on to the ACK console. In the left-side navigation pane, click Clusters.

2. On the Clusters page, click the name of the cluster that you want to manage and choose Operations > Add-ons in the left-side navigation pane.

3. On the Core Components tab, find Kube Scheduler and click Configuration in the lower-right corner.

4. In the Kube Scheduler Parameters dialog box, configure the custom parameters and click OK.

   The features and custom parameters vary based on the kube-scheduler version. For more information about the features supported by different kube-scheduler versions, see kube-scheduler. The following table describes the custom parameters.

   Parameter	Description	Data type	Valid value	Default value
   podMaxBackoffSeconds	The maximum backoff delay for rescheduling pods in seconds. After a pod fails to be scheduled, the pod cannot be rescheduled until the delay ends.	int	[1,100000]	10
   Preferably Use Bin Packing During Pod Scheduling (Pods Are Evenly Distributed among Nodes When Unselected)	Specifies whether to schedule pods based on the binpack algorithm.	bool	false true	true
   binpackPluginWeight	The weight of the node score calculated by the binpack plug-in. Note This parameter takes effect only if you select Preferably Use Bin Packing During Pod Scheduling.	int	[1,100000]	100
   binpackResourceWeight	The weight of each resource when the binpack plug-in calculates the node score. Note This parameter takes effect only if you select Preferably Use Bin Packing During Pod Scheduling.	resourceName: string resourceWeight: int	Schema validation is performed on the resourceName parameter. The value of the resourceName parameter can contain only letters, digits, periods (.), forward slashes (/), and hyphens (-). The value of the resourceWeight parameter must be an integer from 1 to 10000.	cpu:1 memory:1
   connectionQPS	The QPS limit of the Kubernetes API server.	int	[1,1000]	50
   connectionBurst	The burst QPS when the QPS limit of the Kubernetes API server is reached.	int	[1,1000]	100
   percentageOfNodesToScore	The percentage of nodes suitable for pod scheduling. Default value: 0. When the default value is used, a proportion of nodes in the cluster are scored. The proportion ranges from 5% to 50% based on the cluster size.	int	[0,100]	0
   Enable Node Scoring for GPU Sharing (Preferably Share One GPU with Multiple Tasks on One Node)	Specifies whether to schedule GPU-accelerated pods to GPUs that request more memory and computing power when you use GPU sharing. To use GPU sharing, you must install the cloud-native AI suite.	bool	false true	true
   Enable Load-aware Scheduling (Unselected by Default)	To enable load-aware scheduling, you must install the ack-koordinator component.	bool	false true	false
   loadAwareResourceWeight	The weights of CPU resources and memory resources for load-aware scheduling. If you do not specify a weight for a resource, the weight is set to 0.	resourceName: string resourceWeight: int	The schema of the resourceName parameter is verified. The value of resourceName can only be cpu or memory. The value of the resourceWeight parameter must be an integer from 1 to 10000.	cpu=1 memory=1
   loadAwareAggregatedUsageAggregationType	The type of data aggregation for the statistics. Valid values: avg: calculates the average value. p50: calculates 50% of the statistics. p90, p95, and p99: calculates 90% of the statistics, calculates 95% of the statistics, and calculates 99% of the statistics.	enum	p50 p90 p95 p99 avg	avg

The following section describes how to configure custom binpack parameters.

Custom binpack parameters

Comparison between the binpack algorithm and the spread algorithm

Configure custom binpack parameters

You can select Preferably Use Bin Packing During Pod Scheduling and specify the weight of the node score calculated by the binpack plug-in. A larger weight indicates a higher probability that pods are scheduled to the same node. You can also specify the resource names based on which the binpack plug-in calculates the node score and the weight of each resource in the node score calculation. A greater value indicates a greater influence on the node score.

1. Log on to the ACK console. In the left-side navigation pane, click Clusters.

2. On the Clusters page, click the name of the cluster that you want to manage and choose Operations > Add-ons in the left-side navigation pane.

3. On the Core Components tab, find Kube Scheduler and click Configuration in the lower-right corner of the card.

4. On the Kube Scheduler Parameters page, configure the custom parameters.

   1. Select Preferably Use Bin Packing During Pod Scheduling to enable the binpack algorithm.

   2. Set the binpackPluginWeight parameter to specify the weight of the node score calculated by the binpack plug-in. For more information, see binpack weight.

      In most cases, you can use the default settings. If pods are not scheduled as expected after you enable binpack, you can set the weight to a greater value, such as 200.

   3. Specify the resource names based on which the binpack plug-in calculates the node score and the weight of each resource.

      • For more information, see Enabling bin packing using MostAllocated strategy. The name and weight parameters in the scoringStrategy:resources section specify the weight of each resource in the node score calculation section. You can configure the name and weight parameters in the scoringStrategy:resources section.

      • The name and weight parameters in the scoringStrategy:resources section correspond to the resourceName and resourceWeight parameters in the ACK console. You can click Add or Delete to add or delete resources.

      Note

      If you select Preferably Use Bin Packing During Pod Scheduling without specifying the resourceName and resourceWeight parameters, the default CPU and memory settings in the following figure are used in node score calculation.