Chapter 4. Testing and troubleshooting autoscaling

Use the Orchestration service (heat) to automatically scale instances up and down based on threshold definitions. To troubleshoot your environment, you can look for errors in the log files and history records.

4.1. Testing automatic scaling up of instances

You can use the Orchestration service (heat) to scale instances automatically based on the cpu_alarm_high threshold definition. When the CPU use reaches a value defined in the threshold parameter, another instance starts up to balance the load. The threshold value in the template.yaml file is set to 80%.

Procedure

  1. Log in to the host environment as the stack user.

  2. For standalone environments set the OS_CLOUD environment variable: 

    [stack@standalone ~]$ export OS_CLOUD=standalone

  3. For director environments source the stackrc file: 

    [stack@undercloud ~]$ source ~/stackrc

  4. Log in to the instance: 

    $ ssh -i ~/mykey.pem cirros@192.168.122.8

  5. Run multiple dd commands to generate the load: 

    [instance ~]$ sudo dd if=/dev/zero of=/dev/null &
[instance ~]$ sudo dd if=/dev/zero of=/dev/null &
[instance ~]$ sudo dd if=/dev/zero of=/dev/null &
  6. Exit from the running instance and return to the host.

  7. After you run the dd commands, you can expect to have 100% CPU use in the instance. Verify that the alarm has been triggered: 

    $ openstack alarm list
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+
| alarm_id                             | type                                       | name                                | state | severity | enabled |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+
| 022f707d-46cc-4d39-a0b2-afd2fc7ab86a | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_high-odj77qpbld7j | alarm | low      | True    |
| 46ed2c50-e05a-44d8-b6f6-f1ebd83af913 | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_low-m37jvnm56x2t  | ok    | low      | True    |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+

  8. After approximately 60 seconds, Orchestration starts another instance and adds it to the group. To verify that an instance has been created, enter the following command: 

    $ openstack server list
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+
| ID                                   | Name                                                  | Status | Task State | Power State | Networks                              |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+
| 477ee1af-096c-477c-9a3f-b95b0e2d4ab5 | ex-3gax-4urpikl5koff-yrxk3zxzfmpf-server-2hde4tp4trnk | ACTIVE | -          | Running     | internal1=10.10.10.13, 192.168.122.17 |
| e1524f65-5be6-49e4-8501-e5e5d812c612 | ex-3gax-5f3a4og5cwn2-png47w3u2vjd-server-vaajhuv4mj3j | ACTIVE | -          | Running     | internal1=10.10.10.9, 192.168.122.8   |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+

  9. After another short period of time, observe that the Orchestration service has autoscaled to three instances. The configuration is set to a maximum of three instances. Verify there are three instances: 

    $ openstack server list
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+
| ID                                   | Name                                                  | Status | Task State | Power State | Networks                              |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+
| 477ee1af-096c-477c-9a3f-b95b0e2d4ab5 | ex-3gax-4urpikl5koff-yrxk3zxzfmpf-server-2hde4tp4trnk | ACTIVE | -          | Running     | internal1=10.10.10.13, 192.168.122.17 |
| e1524f65-5be6-49e4-8501-e5e5d812c612 | ex-3gax-5f3a4og5cwn2-png47w3u2vjd-server-vaajhuv4mj3j | ACTIVE | -          | Running     | internal1=10.10.10.9, 192.168.122.8   |
| 6c88179e-c368-453d-a01a-555eae8cd77a | ex-3gax-fvxz3tr63j4o-36fhftuja3bw-server-rhl4sqkjuy5p | ACTIVE | -          | Running     | internal1=10.10.10.5, 192.168.122.5   |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+

4.2. Testing automatic scaling down of instances

You can use the Orchestration service (heat) to automatically scale down instances based on the cpu_alarm_low threshold. In this example, the instances are scaled down when CPU use is below 5%.

Procedure

  1. From within the workload instance, terminate the running dd processes and observe Orchestration begin to scale the instances back down. 

    $ killall dd
  2. Log in to the host environment as the stack user.

  3. For standalone environments set the OS_CLOUD environment variable: 

    [stack@standalone ~]$ export OS_CLOUD=standalone

  4. For director environments source the stackrc file: 

    [stack@undercloud ~]$ source ~/stackrc

  5. When you stop the dd processes, this triggers the cpu_alarm_low event alarm. As a result, Orchestration begins to automatically scale down and remove the instances. Verify that the corresponding alarm has triggered: 

    $ openstack alarm list
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+
| alarm_id                             | type                                       | name                                | state | severity | enabled |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+
| 022f707d-46cc-4d39-a0b2-afd2fc7ab86a | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_high-odj77qpbld7j | ok    | low      | True    |
| 46ed2c50-e05a-44d8-b6f6-f1ebd83af913 | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_low-m37jvnm56x2t  | alarm | low      | True    |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+

    After a few minutes, Orchestration continually reduce the number of instances to the minimum value defined in the min_size parameter of the scaleup_group definition. In this scenario, the min_size parameter is set to 1.

4.3. Troubleshooting for autoscaling

If your environment is not working properly, you can look for errors in the log files and history records.

Procedure

  1. Log in to the host environment as the stack user.

  2. For standalone environments set the OS_CLOUD environment variable: 

    [stack@standalone ~]$ export OS_CLOUD=standalone

  3. For director environments source the stackrc file: 

    [stack@undercloud ~]$ source ~/stackrc

  4. To retrieve information on state transitions, list the stack event records: 

    $ openstack stack event list example
2017-03-06 11:12:43Z [example]: CREATE_IN_PROGRESS  Stack CREATE started
2017-03-06 11:12:43Z [example.scaleup_group]: CREATE_IN_PROGRESS  state changed
2017-03-06 11:13:04Z [example.scaleup_group]: CREATE_COMPLETE  state changed
2017-03-06 11:13:04Z [example.scaledown_policy]: CREATE_IN_PROGRESS  state changed
2017-03-06 11:13:05Z [example.scaleup_policy]: CREATE_IN_PROGRESS  state changed
2017-03-06 11:13:05Z [example.scaledown_policy]: CREATE_COMPLETE  state changed
2017-03-06 11:13:05Z [example.scaleup_policy]: CREATE_COMPLETE  state changed
2017-03-06 11:13:05Z [example.cpu_alarm_low]: CREATE_IN_PROGRESS  state changed
2017-03-06 11:13:05Z [example.cpu_alarm_high]: CREATE_IN_PROGRESS  state changed
2017-03-06 11:13:06Z [example.cpu_alarm_low]: CREATE_COMPLETE  state changed
2017-03-06 11:13:07Z [example.cpu_alarm_high]: CREATE_COMPLETE  state changed
2017-03-06 11:13:07Z [example]: CREATE_COMPLETE  Stack CREATE completed successfully
2017-03-06 11:19:34Z [example.scaleup_policy]: SIGNAL_COMPLETE  alarm state changed from alarm to alarm (Remaining as alarm due to 1 samples outside threshold, most recent: 95.4080102993)
2017-03-06 11:25:43Z [example.scaleup_policy]: SIGNAL_COMPLETE  alarm state changed from alarm to alarm (Remaining as alarm due to 1 samples outside threshold, most recent: 95.8869217299)
2017-03-06 11:33:25Z [example.scaledown_policy]: SIGNAL_COMPLETE  alarm state changed from ok to alarm (Transition to alarm due to 1 samples outside threshold, most recent: 2.73931707966)
2017-03-06 11:39:15Z [example.scaledown_policy]: SIGNAL_COMPLETE  alarm state changed from alarm to alarm (Remaining as alarm due to 1 samples outside threshold, most recent: 2.78110858552)

  5. Read the alarm history log: 

    $ openstack alarm-history show 022f707d-46cc-4d39-a0b2-afd2fc7ab86a
+----------------------------+------------------+-----------------------------------------------------------------------------------------------------+--------------------------------------+
| timestamp                  | type             | detail                                                                                              | event_id                             |
+----------------------------+------------------+-----------------------------------------------------------------------------------------------------+--------------------------------------+
| 2017-03-06T11:32:35.510000 | state transition | {"transition_reason": "Transition to ok due to 1 samples inside threshold, most recent:             | 25e0e70b-3eda-466e-abac-42d9cf67e704 |
|                            |                  | 2.73931707966", "state": "ok"}                                                                      |                                      |
| 2017-03-06T11:17:35.403000 | state transition | {"transition_reason": "Transition to alarm due to 1 samples outside threshold, most recent:         | 8322f62c-0d0a-4dc0-9279-435510f81039 |
|                            |                  | 95.0964497325", "state": "alarm"}                                                                   |                                      |
| 2017-03-06T11:15:35.723000 | state transition | {"transition_reason": "Transition to ok due to 1 samples inside threshold, most recent:             | 1503bd81-7eba-474e-b74e-ded8a7b630a1 |
|                            |                  | 3.59330523447", "state": "ok"}                                                                      |                                      |
| 2017-03-06T11:13:06.413000 | creation         | {"alarm_actions": ["trust+http://fca6e27e3d524ed68abdc0fd576aa848:delete@192.168.122.126:8004/v1/fd | 224f15c0-b6f1-4690-9a22-0c1d236e65f6 |
|                            |                  | 1c345135be4ee587fef424c241719d/stacks/example/d9ef59ed-b8f8-4e90-bd9b-                              |                                      |
|                            |                  | ae87e73ef6e2/resources/scaleup_policy/signal"], "user_id": "a85f83b7f7784025b6acdc06ef0a8fd8",      |                                      |
|                            |                  | "name": "example-cpu_alarm_high-odj77qpbld7j", "state": "insufficient data", "timestamp":           |                                      |
|                            |                  | "2017-03-06T11:13:06.413455", "description": "Scale up if CPU > 80%", "enabled": true,              |                                      |
|                            |                  | "state_timestamp": "2017-03-06T11:13:06.413455", "rule": {"evaluation_periods": 1, "metric":        |                                      |
|                            |                  | "cpu_util", "aggregation_method": "mean", "granularity": 300, "threshold": 80.0, "query": "{\"=\":   |                                      |
|                            |                  | {\"server_group\": \"d9ef59ed-b8f8-4e90-bd9b-ae87e73ef6e2\"}}", "comparison_operator": "gt",        |                                      |
|                            |                  | "resource_type": "instance"}, "alarm_id": "022f707d-46cc-4d39-a0b2-afd2fc7ab86a",                   |                                      |
|                            |                  | "time_constraints": [], "insufficient_data_actions": null, "repeat_actions": true, "ok_actions":    |                                      |
|                            |                  | null, "project_id": "fd1c345135be4ee587fef424c241719d", "type":                                     |                                      |
|                            |                  | "gnocchi_aggregation_by_resources_threshold", "severity": "low"}                                    |                                      |
+----------------------------+------------------+-----------------------------------------------------------------------------------------------------+-------------------------------------

  6. To view the records of scale-out or scale-down operations that heat collects for the existing stack, you can use the awk command to parse the heat-engine.log: 

    $ awk '/Stack UPDATE started/,/Stack CREATE completed successfully/ {print $0}' /var/log/containers/heat/heat-engine.log

  7. To view aodh-related information, examine the evaluator.log: 

    $ grep -i alarm /var/log/containers/aodh/evaluator.log | grep -i transition

4.4. Using CPU telemetry values for autoscaling threshold when using rate:mean aggregration

When using the OS::Heat::Autoscaling heat orchestration template (HOT) and setting a threshold value for CPU, the value is expressed in nanoseconds of CPU time which is a dynamic value based on the number of virtual CPUs allocated to the instance workload. In this reference guide we’ll explore how to calculate and express the CPU nanosecond value as a percentage when using the Gnocchi rate:mean aggregration method.

4.4.1. Calculating CPU telemetry values as a percentage

CPU telemetry is stored in Gnocchi (OpenStack time-series data store) as CPU utilization in nanoseconds. When using CPU telemetry to define autoscaling thresholds it is useful to express the values as a percentage of CPU utilization since that is more natural when defining the threshold values. When defining the scaling policies used as part of an autoscaling group, we can take our desired threshold defined as a percentage and calculate the required threshold value in nanoseconds which is used in the policy definitions.

Value (ns)Granularity (s)Percentage

60000000000

60

100

54000000000

60

90

48000000000

60

80

42000000000

60

70

36000000000

60

60

30000000000

60

50

24000000000

60

40

18000000000

60

30

12000000000

60

20

6000000000

60

10

4.4.2. Displaying instance workload vCPU as a percentage

You can display the gnocchi-stored CPU telemetry data as a percentage rather than the nanosecond values for instances by using the openstack metric aggregates command.

Prerequisites

  • Create a heat stack using the autoscaling group resource that results in an instance workload.

Procedure

  1. Login to your OpenStack environment as the cloud adminstrator.

  2. Retrieve the ID of the autoscaling group heat stack: 

    $ openstack stack show vnf -c id -c stack_status
+--------------+--------------------------------------+
| Field        | Value                                |
+--------------+--------------------------------------+
| id           | e0a15cee-34d1-418a-ac79-74ad07585730 |
| stack_status | CREATE_COMPLETE                      |
+--------------+--------------------------------------+

  3. Set the value of the stack ID to an environment variable: 

    $ export STACK_ID=$(openstack stack show vnf -c id -f value)

  4. Return the metrics as an aggregate by resource type instance (server ID) with the value calculated as a percentage. The aggregate is returned as a value of nanoseconds of CPU time. We divide that number by 1000000000 to get the value in seconds. We then divide the value by our granularity, which in this example is 60 seconds. That value is then converted to a percentage by multiplying by 100. Finally, we divide the total value by the number of vCPU provided by the flavor assigned to the instance, in this example a value of 2 vCPU, providing us a value expressed as a percentage of CPU time: 

    $ openstack metric aggregates --resource-type instance --sort-column timestamp --sort-descending '(/ (* (/ (/ (metric cpu rate:mean) 1000000000) 60) 100) 2)' server_group="$STACK_ID"
+----------------------------------------------------+---------------------------+-------------+--------------------+
| name                                               | timestamp                 | granularity |              value |
+----------------------------------------------------+---------------------------+-------------+--------------------+
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T21:03:00+00:00 |        60.0 |  3.158333333333333 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T21:02:00+00:00 |        60.0 | 2.6333333333333333 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T21:02:00+00:00 |        60.0 |  2.533333333333333 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T21:01:00+00:00 |        60.0 |  2.833333333333333 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T21:01:00+00:00 |        60.0 | 3.0833333333333335 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T21:00:00+00:00 |        60.0 | 13.450000000000001 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T21:00:00+00:00 |        60.0 |               2.45 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T21:00:00+00:00 |        60.0 | 2.6166666666666667 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T20:59:00+00:00 |        60.0 | 60.583333333333336 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:59:00+00:00 |        60.0 |               2.35 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:59:00+00:00 |        60.0 |              2.525 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T20:58:00+00:00 |        60.0 |  71.35833333333333 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:58:00+00:00 |        60.0 |              3.025 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:58:00+00:00 |        60.0 |                9.3 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T20:57:00+00:00 |        60.0 |  66.19166666666668 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:57:00+00:00 |        60.0 |              2.275 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:57:00+00:00 |        60.0 |  56.31666666666667 |
| 61bfb555-9efb-46f1-8559-08dec90f94ed/cpu/rate:mean | 2022-11-07T20:56:00+00:00 |        60.0 |  59.50833333333333 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:56:00+00:00 |        60.0 |              2.375 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:56:00+00:00 |        60.0 | 63.949999999999996 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:55:00+00:00 |        60.0 | 15.558333333333335 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:55:00+00:00 |        60.0 |              93.85 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:54:00+00:00 |        60.0 |  59.54999999999999 |
| 199b0cb9-6ed6-4410-9073-0fb2e7842b65/cpu/rate:mean | 2022-11-07T20:54:00+00:00 |        60.0 |  61.23333333333334 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:53:00+00:00 |        60.0 |  74.73333333333333 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:52:00+00:00 |        60.0 |  57.86666666666667 |
| a95ab818-fbe8-4acd-9f7b-58e24ade6393/cpu/rate:mean | 2022-11-07T20:51:00+00:00 |        60.0 | 60.416666666666664 |
+----------------------------------------------------+---------------------------+-------------+--------------------+

4.4.3. Retrieving available telemetry for an instance workload

Retrieve the available telemetry for an instance workload and express the vCPU utilization as a percentage.

Prerequisites

  • Create a heat stack using the autoscaling group resource that results in an instance workload.

Procedure

  1. Login to your OpenStack environment as the cloud adminstrator.

  2. Retrieve the ID of the autoscaling group heat stack: 

    $ openstack stack show vnf -c id -c stack_status
+--------------+--------------------------------------+
| Field        | Value                                |
+--------------+--------------------------------------+
| id           | e0a15cee-34d1-418a-ac79-74ad07585730 |
| stack_status | CREATE_COMPLETE                      |
+--------------+--------------------------------------+

  3. Set the value of the stack ID to an environment variable: 

    $ export STACK_ID=$(openstack stack show vnf -c id -f value)

  4. Retrieve the ID of the workload instance you want to return data for. We are using the server list long form and filtering for instances that are part of our autoscaling group: 

    $ openstack server list --long --fit-width | grep "metering.server_group='$STACK_ID'"
| bc1811de-48ed-44c1-ae22-c01f36d6cb02 | vn-xlfb4jb-yhbq6fkk2kec-qsu2lr47zigs-vnf-y27wuo25ce4e | ACTIVE | None       | Running     | private=192.168.100.139, 192.168.25.179 | fedora36   | d21f1aaa-0077-4313-8a46-266c39b705c1 | m1.small    | 692533fe-0912-417e-b706-5d085449db53 | nova              | standalone.localdomain | metering.server_group='e0a15cee-34d1-418a-ac79-74ad07585730' |

  5. Set the instance ID for one of the returned instance workload names: 

    $ INSTANCE_NAME='vn-xlfb4jb-yhbq6fkk2kec-qsu2lr47zigs-vnf-y27wuo25ce4e' ; export INSTANCE_ID=$(openstack server list --name $INSTANCE_NAME -c ID -f value)

  6. Verify metrics have been stored for the instance resource ID. If no metrics are available it’s possible not enough time has elapsed since the instance was created. If enough time has elapsed, you can check the logs for the data collection service in /var/log/containers/ceilometer/ and logs for the time-series database service gnocchi in /var/log/containers/gnocchi/: 

    $ openstack metric resource show --column metrics $INSTANCE_ID
+---------+---------------------------------------------------------------------+
| Field   | Value                                                               |
+---------+---------------------------------------------------------------------+
| metrics | compute.instance.booting.time: 57ca241d-764b-4c58-aa32-35760d720b08 |
|         | cpu: d7767d7f-b10c-4124-8893-679b2e5d2ccd                           |
|         | disk.ephemeral.size: 038b11db-0598-4cfd-9f8d-4ba6b725375b           |
|         | disk.root.size: 843f8998-e644-41f6-8635-e7c99e28859e                |
|         | memory.usage: 1e554370-05ac-4107-98d8-9330265db750                  |
|         | memory: fbd50c0e-90fa-4ad9-b0df-f7361ceb4e38                        |
|         | vcpus: 0629743e-6baa-4e22-ae93-512dc16bac85                         |
+---------+---------------------------------------------------------------------+

  7. Verify there are available measures for the resource metric and note the granularity value as we’ll use it when running the openstack metric aggregates command: 

    $ openstack metric measures show --resource-id $INSTANCE_ID --aggregation rate:mean cpu
+---------------------------+-------------+---------------+
| timestamp                 | granularity |         value |
+---------------------------+-------------+---------------+
| 2022-11-08T14:12:00+00:00 |        60.0 | 71920000000.0 |
| 2022-11-08T14:13:00+00:00 |        60.0 | 88920000000.0 |
| 2022-11-08T14:14:00+00:00 |        60.0 | 76130000000.0 |
| 2022-11-08T14:15:00+00:00 |        60.0 | 17640000000.0 |
| 2022-11-08T14:16:00+00:00 |        60.0 |  3330000000.0 |
| 2022-11-08T14:17:00+00:00 |        60.0 |  2450000000.0 |
...

  8. Retrieve the number of vCPU cores applied to the workload instance by reviewing the configured flavor for the instance workload: 

    $ openstack server show $INSTANCE_ID -cflavor -f value
m1.small (692533fe-0912-417e-b706-5d085449db53)

$ openstack flavor show 692533fe-0912-417e-b706-5d085449db53 -c vcpus -f value
2

  9. Return the metrics as an aggregate by resource type instance (server ID) with the value calculated as a percentage. The aggregate is returned as a value of nanoseconds of CPU time. We divide that number by 1000000000 to get the value in seconds. We then divide the value by our granularity, which in this example is 60 seconds (as previously retrieved with openstack metric measures show command). That value is then converted to a percentage by multiplying by 100. Finally, we divide the total value by the number of vCPU provided by the flavor assigned to the instance, in this example a value of 2 vCPU, providing us a value expressed as a percentage of CPU time: 

    $ openstack metric aggregates --resource-type instance --sort-column timestamp --sort-descending '(/ (* (/ (/ (metric cpu rate:mean) 1000000000) 60) 100) 2)' id=$INSTANCE_ID
+----------------------------------------------------+---------------------------+-------------+--------------------+
| name                                               | timestamp                 | granularity |              value |
+----------------------------------------------------+---------------------------+-------------+--------------------+
| bc1811de-48ed-44c1-ae22-c01f36d6cb02/cpu/rate:mean | 2022-11-08T14:26:00+00:00 |        60.0 |               2.45 |
| bc1811de-48ed-44c1-ae22-c01f36d6cb02/cpu/rate:mean | 2022-11-08T14:25:00+00:00 |        60.0 |             11.075 |
| bc1811de-48ed-44c1-ae22-c01f36d6cb02/cpu/rate:mean | 2022-11-08T14:24:00+00:00 |        60.0 |               61.3 |
| bc1811de-48ed-44c1-ae22-c01f36d6cb02/cpu/rate:mean | 2022-11-08T14:23:00+00:00 |        60.0 |  74.78333333333332 |
| bc1811de-48ed-44c1-ae22-c01f36d6cb02/cpu/rate:mean | 2022-11-08T14:22:00+00:00 |        60.0 | 55.383333333333326 |
...