Practice of Troubleshooting Unexecuted Auto Scaling Rules
Last updated:2026-03-27 14:24:33
Practice of Troubleshooting Unexecuted Auto Scaling Rules
Last updated: 2026-03-27 14:24:33
Auto Scaling policy not triggered
1. No scale-out resource specifications are configured. The scale-out rule cannot be triggered. It is recommended to add configurations for scaling specifications. At least 1 elastic resource specification should be set.
Cause: Under Console > Auto Scaling > Scaling Specification Management, no node specifications are added, and the scale-out rule cannot be triggered.
Solution: Choose Console > Auto Scaling > Scaling Specification Management > Add Specifications to add the desired node specifications.
2. Elastic resources have reached the maximum node limit. Scale-out cannot be triggered. It is recommended to adjust the maximum node limit.
Cause: The current number of elastic nodes equals the maximum node limit, and the scale-out rule cannot be triggered.
Solution: If further scale-out is required, set the maximum node limit again.
3. Elastic resources have reached the minimum node limit. Scale-in cannot be triggered. It is recommended to adjust the minimum node limit.
Cause: The current number of elastic nodes is less than the minimum node limit, and the scale-in rule cannot be triggered.
Solution: If further scale-in is required, set the minimum node limit again.
4. No elastic resources are available in the cluster. The scale-in rule cannot be triggered. It is recommended to supplement elastic resources before executing the scale-in rule.
Cause: The current cluster has no elastic nodes, and the scale-in rule cannot be triggered.
Solution: If further scale-in is required, supplement elastic resources before executing the scale-in rule.
5. No eligible elastic resources (excluding scheduled termination resources) are available in the cluster. The scale-in rule cannot be triggered. It is recommended to supplement elastic resources before executing the scale-in rule.
Cause: All current elastic nodes are set for scheduled termination, and the scale-in rule cannot be triggered.
Solution: If further scale-in for scheduled termination nodes is required, you can perform a manual scale-in.
6. The execution time range for time-based scaling has expired. If you want to continue using this rule for auto scaling, it is recommended to modify the effective time range of the rule.
Cause: The execution time range for time-based scaling has expired. The rule status is disabled, and the scaling rule cannot be triggered.
Solution: If continued execution is required, edit the rule to modify the execution time range and enable the rule status.
7. The cluster is in the cooldown period and cannot trigger scaling temporarily. It is recommended to adjust the cooldown period for the scaling rules.
Cause: The cluster is currently in the cooldown period of another scaling operation and cannot trigger the scaling rules.
Solution: You can shorten the cooldown period of other rules or extend the expiration retry time of this scale-out rule.
Auto Scaling policy execution failed
1. The cluster is not in a scalable status.It is recommended to try again later or Submit a Ticket to contact internal R&D personnel.
Cause: The scale-out rule is triggered, but the current cluster is in a non-running status due to component installation or other reasons, resulting in a scale-out failure.
Solution: You can manually scale out or edit the rule to extend the expiration retry time, ensuring the rule can be executed.
2. The account balance is insufficient. The scale-out rule fails to execute. It is recommended to top up your account to ensure a sufficient balance.
Cause: The scale-out rule is triggered, but the balance is insufficient when placing an order.
3. The preset scale-out resource specifications are sold out. Scale-out cannot be triggered. It is recommended to switch to the specifications with sufficient resources.
Cause: The scale-out rule is triggered, but the selected model specifications in the current availability zone (AZ) are sold out.
Solution: Set the node specifications with sufficient resources again.
4. Fails to deliver elastic scale-out resources. It is recommended to try again later or Submit a Ticket to contact internal R&D personnel.
Cause: The scale-out rule is triggered, and the preset model specifications have sufficient inventory, but the delivery fails due to various reasons after the order is placed.
Solution: You can perform a manual scale-out or try again later.
5. The model quota is insufficient. It is recommended to adjust the resource quota or switch to a model with a sufficient quota.
Cause: The scale-out rule is triggered, but the current account has insufficient quota for the preset model specifications in the current AZ, resulting in a scale-out failure.
Solution: You can apply for a quota increase or switch to a model with a sufficient quota.
6. The disk space for elastic scale-out resources is insufficient. It is recommended to switch to a disk type with sufficient resource specifications or Submit a Ticket to contact internal R&D personnel.
Cause: The scale-out rule is triggered, but the disk type associated with the preset resources has insufficient resources, resulting in a scale-out failure.
Solution: Set the disk type with sufficient resources again.
7. The cluster scale-in process is mutually exclusive for identical nodes. Some Task nodes (%s) specified by the auto scaling are already in the scale-in process.
Cause: The scale-in rule is triggered, but some nodes specified by the current scale-in rule are already in the scale-in process.
Solution: You can perform a manual scale-in or try again later.
8. Cluster process conflict, try again later.
Cause: The Auto Scaling rule is triggered. If other scaling processes exist in the cluster, the current scaling rule will fail to execute.
Solution: You can edit the rule to extend the expiration retry time, ensuring the rule can be executed.
9. The elastic IP addresses in the subnet bound to the cluster are insufficient. It is recommended to switch to another subnet within the same VPC.
Cause: The scale-out rule is triggered, but the subnet bound to the cluster has insufficient elastic IP addresses, resulting in a scale-out failure.
Solution: Switch to another subnet within the same VPC.
10. The current setting for expiration retry time is too short. The rule is not triggered for scaling within the expiration retry time.
Cause: During the time between the triggering and the expiration retry of a time-based scaling rule, if other Auto Scaling processes exist in the cluster, the current rule will not be executed.
Solution: You can edit the rule to extend the expiration retry time, ensuring the rule can be executed.
Auto Scaling Policy Executed Partially Successfully
1. Elastic scale-out resources have reached the maximum node limit. The scale-out rule is executed partially successfully. If further scale-out is required, it is recommended to adjust the maximum node limit.
Cause: The scale-out rule is triggered, but the total number of elastic nodes equals the maximum node limit after the current scale-out rule is executed successfully.
Solution: If further scale-out is required, set the maximum node limit again.
2. Elastic resources have reached the minimum node limit. The scale-in rule is executed partially successfully. If further scale-in is required, it is recommended to adjust the minimum node limit.
Cause: The scale-in rule is triggered, but the total number of elastic nodes is less than the minimum node limit after the current scale-in rule is executed successfully.
Solution: If further scale-in is required, set the minimum node limit again.
3. The inventory of the preset scale-out resource specifications is insufficient. Only partial resources are supplemented. It is recommended to manually scale out resources with sufficient inventory to supplement the lack of required resources.
Cause: The scale-out rule is triggered, but the selected model in the current AZ has insufficient resources.
Solution: Set the node specifications with sufficient resources again.
4. The actual delivery quantity is less than the target scale-out quantity. Only partial resources are supplemented. It is recommended to manually scale out resources with sufficient inventory to supplement the lack of required resources.
Cause: The scale-out rule is triggered, and the preset model specifications have sufficient inventory, but the actual delivery quantity is less than the target scale-out quantity due to various reasons after the order is placed.
Solution: You can perform a manual scale-out or try again later.
5. The model quota is insufficient. Only partial resources are supplemented. It is recommended to adjust the resource quota or switch to a model with a sufficient quota.
Cause: The scale-out rule is triggered, but the current account has insufficient quota for the preset model specifications in the current AZ. As a result, only a partial resource supplement is completed.
Solution: You can apply for a quota increase or switch to a model with a sufficient quota.
6. The disk space for elastic scale-out resources is insufficient. It is recommended to switch to a disk type with sufficient resource specifications or Submit a Ticket to contact internal R&D personnel.
Cause: The scale-out rule is triggered, but the disk type associated with the preset resources has insufficient resources, resulting in a scale-out failure.
Solution: Set the disk type with sufficient resources again.
7. The elastic scale-in resources (excluding scheduled termination resources) are insufficient. Only some nodes are scaled in. It is recommended to supplement elastic resources before executing the scale-in rule.
Cause: The scale-in rule is triggered, but after excluding scheduled termination nodes, the current number of elastic nodes is less than the target scale-in quantity.
Solution: If further scale-in for scheduled termination nodes is required, you can perform a manual scale-in.
8. The elastic IP addresses in the subnet bound to the cluster are insufficient. It is recommended to switch to another subnet within the same VPC.
Cause: The scale-out rule is triggered, but the subnet bound to the cluster has insufficient elastic IP addresses, resulting in a partially successful scale-out.
Solution: Switch to another subnet within the same VPC.
9. The account balance is insufficient. The resource supplement fails. It is recommended to top up the account to ensure a sufficient balance.
Cause: The scale-out rule with "Achieve Targets as Possible" enabled is triggered, but the balance is insufficient when retrying to place an order during the resource supplement retry.
10. Cluster process conflict, try again later.
Cause: The scaling rule with "Achieve Targets as Possible" enabled is triggered. If other scaling processes exist in the cluster during the resource supplement retry, the retry will fail to execute.
Solution: You can perform a manual scale-out or try again later.
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