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Auto Scaling for Instances

Red Hat OpenStack Platform 16.0

Configuring Auto Scaling in Red Hat OpenStack Platform

OpenStack Documentation Team

Abstract

Automatically scale out your Compute instances in response to system usage.

Chapter 1. About This Guide
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Warning

Red Hat is currently reviewing the information and procedures provided in this guide for this release.

This document is based on the Red Hat OpenStack Platform 12 document, available at https://access.redhat.com/documentation/en-us/red_hat_openstack_platform/?version=12.

If you require assistance for the current Red Hat OpenStack Platform release, please contact Red Hat support.

Automatically scale out your Compute instances in response to heavy system use. You can add pre-defined rules that consider factors such as CPU or memory use and you can configure Orchestration (heat) to add and remove additional instances automatically, when they are needed.

2.1. Overview of auto scaling architecture
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2.1.1. Orchestration
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The core component providing auto scaling is Orchestration (heat). Use Orchestration to define rules using human-readable YAML templates. These rules are applied to evaluate system load based on Telemetry data to find out whether you need to add more instances into the stack. When the load drops, Orchestration can automatically remove the unused instances again.

2.1.2. Telemetry
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Telemetry monitors the performance of your Red Hat OpenStack Platform environment, collecting data on CPU, storage, and memory utilization for instances and physical hosts. Orchestration templates examine Telemetry data to assess whether any pre-defined action should start.

2.1.3. Key terms
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Stack
A collection of resources that are necessary to operate an application. A stack can be as simple as a single instance and its resources, or as complex as multiple instances with all the resource dependencies that comprise a multi-tier application.
Templates

YAML scripts that define a series of tasks for heat to execute. For example, it is preferable to use separate templates for certain functions:

  • Template file: Define thresholds that Telemetry should respond to, and define the auto scaling group.
  • Environment file: Defines the build information for your environment: which flavor and image to use, how to configure the virtual network, and what software to install.

2.2. Example: Auto scaling based on CPU use
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In this example, Orchestration examines Telemetry data, and automatically increases the number of instances in response to high CPU use. Create a stack template and environment template to define the rules and subsequent configuration. This example uses existing resources, such as networks), and uses names that might be different to those in your own environment.

Note

The cpu_util metric was deprecated and removed from Red Hat OpenStack Platform.

Procedure

  1. Create the environment template, describing the instance flavor, networking configuration, and image type. Save the template in the /home/<user>/stacks/example1/cirros.yaml file. Replace the <user> variable with a real user name. 

    heat_template_version: 2016-10-14
description: Template to spawn an cirros instance.

parameters:
  metadata:
    type: json
  image:
    type: string
    description: image used to create instance
    default: cirros
  flavor:
    type: string
    description: instance flavor to be used
    default: m1.tiny
  key_name:
    type: string
    description: keypair to be used
    default: mykeypair
  network:
    type: string
    description: project network to attach instance to
    default: internal1
  external_network:
    type: string
    description: network used for floating IPs
    default: external_network

resources:
  server:
    type: OS::Nova::Server
    properties:
      block_device_mapping:
        - device_name: vda
          delete_on_termination: true
          volume_id: { get_resource: volume }
      flavor: {get_param: flavor}
      key_name: {get_param: key_name}
      metadata: {get_param: metadata}
      networks:
        - port: { get_resource: port }

  port:
    type: OS::Neutron::Port
    properties:
      network: {get_param: network}
      security_groups:
        - default

  floating_ip:
    type: OS::Neutron::FloatingIP
    properties:
      floating_network: {get_param: external_network}

  floating_ip_assoc:
    type: OS::Neutron::FloatingIPAssociation
    properties:
      floatingip_id: { get_resource: floating_ip }
      port_id: { get_resource: port }

  volume:
    type: OS::Cinder::Volume
    properties:
      image: {get_param: image}
      size: 1

  2. Register the Orchestration resource in ~/stacks/example1/environment.yaml: 

    resource_registry:

    "OS::Nova::Server::Cirros": ~/stacks/example1/cirros.yaml

  3. Create the stack template. Describe the CPU thresholds to watch for and how many instances to add. An instance group is also created that defines the minimum and maximum number of instances that can participate in this template.

    Note

    The cpu_util metric was deprecated and removed from Red Hat OpenStack Platform. To obtain the equivalent functionality, use the cumulative cpu metric and an archive policy that includes the rate:mean aggregation method. For example, ceilometer-high-rate and ceilometer-low-rate. You must convert the threshold value from % to ns to use the cpu metric for the CPU utilisation alarm. The formula is: time_ns = 1,000,000,000 x {granularity} x {percentage_in_decimal}. For example, for a threshold of 80% with a granularity of 1s, the threshold is 1,000,000,000 x 1 x 0.8 = 800,000,000.0

  4. Save the following values in ~/stacks/example1/template.yaml: 

    heat_template_version: 2016-10-14
description: Example auto scale group, policy and alarm
resources:
  scaleup_group:
    type: OS::Heat::AutoScalingGroup
    properties:
      cooldown: 300
      desired_capacity: 1
      max_size: 3
      min_size: 1
      resource:
        type: OS::Nova::Server::Cirros
        properties:
          metadata: {"metering.server_group": {get_param: "OS::stack_id"}}

  scaleup_policy:
    type: OS::Heat::ScalingPolicy
    properties:
      adjustment_type: change_in_capacity
      auto_scaling_group_id: { get_resource: scaleup_group }
      cooldown: 300
      scaling_adjustment: 1

  scaledown_policy:
    type: OS::Heat::ScalingPolicy
    properties:
      adjustment_type: change_in_capacity
      auto_scaling_group_id: { get_resource: scaleup_group }
      cooldown: 300
      scaling_adjustment: -1

  cpu_alarm_high:
    type: OS::Aodh::GnocchiAggregationByResourcesAlarm
    properties:
      description: Scale up if CPU > 80%
      metric: cpu
      aggregation_method: rate:mean
      granularity: 1
      evaluation_periods: 3
      threshold: 800000000.0
      resource_type: instance
      comparison_operator: gt
      alarm_actions:
        - str_replace:
            template: trust+url
            params:
              url: {get_attr: [scaleup_policy, signal_url]}
      query:
        str_replace:
          template: '{"=": {"server_group": "stack_id"}}'
          params:
            stack_id: {get_param: "OS::stack_id"}

  cpu_alarm_low:
    type: OS::Aodh::GnocchiAggregationByResourcesAlarm
    properties:
      metric: cpu
      aggregation_method: rate:mean
      granularity: 1
      evaluation_periods: 3
      threshold: 200000000.0
      resource_type: instance
      comparison_operator: lt
      alarm_actions:
        - str_replace:
            template: trust+url
            params:
              url: {get_attr: [scaledown_policy, signal_url]}
      query:
        str_replace:
          template: '{"=": {"server_group": "stack_id"}}'
          params:
            stack_id: {get_param: "OS::stack_id"}

outputs:
  scaleup_policy_signal_url:
    value: {get_attr: [scaleup_policy, signal_url]}

  scaledown_policy_signal_url:
    value: {get_attr: [scaledown_policy, signal_url]}

  5. Enter the following command to build the environment and deploy the instance: 

    $ openstack stack create  -t template.yaml -e environment.yaml example
+---------------------+--------------------------------------------+
| Field               | Value                                      |
+---------------------+--------------------------------------------+
| id                  | 248a98bb-f56e-4934-a281-fffde62d78d8       |
| stack_name          | example                                   |
| description         | Example auto scale group, policy and alarm |
| creation_time       | 2017-03-06T15:00:29Z                       |
| updated_time        | None                                       |
| stack_status        | CREATE_IN_PROGRESS                         |
| stack_status_reason | Stack CREATE started                       |
+---------------------+--------------------------------------------+

  6. Orchestration creates the stack and launches a defined minimum number of cirros instances, as defined in the min_size parameter of the scaleup_group definition. Verify that the instances were created successfully: 

    $ openstack server list
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+-------------------------------------+
| ID                                   | Name                                                  | Status | Task State | Power State | Networks                            |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+-------------------------------------+
| e1524f65-5be6-49e4-8501-e5e5d812c612 | ex-3gax-5f3a4og5cwn2-png47w3u2vjd-server-vaajhuv4mj3j | ACTIVE | -          | Running     | internal1=10.10.10.9, 192.168.122.8 |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+-------------------------------------+

  7. Orchestration also creates two cpu alarms which are used to trigger scale-up or scale-down events, as defined in cpu_alarm_high and cpu_alarm_low. Verify that the triggers exist: 

    $ 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 | insufficient data | low      | True    |
| 46ed2c50-e05a-44d8-b6f6-f1ebd83af913 | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_low-m37jvnm56x2t  | insufficient data | low      | True    |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------------------+----------+---------+

2.2.1. Testing auto scaling up instances
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Orchestration can 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 above template.yaml file is set to 80%.

Procedure

  1. Log on to the instance and run several dd commands to generate the load: 

    $ ssh -i ~/mykey.pem cirros@192.168.122.8
$ sudo dd if=/dev/zero of=/dev/null &
$ sudo dd if=/dev/zero of=/dev/null &
$ sudo dd if=/dev/zero of=/dev/null &

  2. You can expect to have 100% CPU use in the cirros instance. Verify that the 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 | alarm | low      | True    |
| 46ed2c50-e05a-44d8-b6f6-f1ebd83af913 | gnocchi_aggregation_by_resources_threshold | example-cpu_alarm_low-m37jvnm56x2t  | ok    | low      | True    |
+--------------------------------------+--------------------------------------------+-------------------------------------+-------+----------+---------+

  3. After approximately 60 seconds, Orchestration starts another instance and adds it into the group. To verify this, 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   |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+

  4. After another short period of time, observe that Orchestration has auto scaled again to three instances. The configuration is set to a maximum of three instances, so it cannot scale any higher. Use the following command to verify that Orchestration has auto-scaled again to 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   |
+--------------------------------------+-------------------------------------------------------+--------+------------+-------------+---------------------------------------+

2.2.2. Automatically scaling down instances
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Orchestration can automatically scale down instances based on the cpu_alarm_low threshold. In this example, the instances scale down when CPU use is below 5%.

Procedure

  1. Terminate the running dd processes and observe Orchestration begin to scale the instances down: 

    $ killall dd

  2. When you stop the dd processes, the cpu_alarm_low event triggers. 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 several minutes, Orchestration continually reduces 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.

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

  1. To view information on state transitions, you can 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)

  2. To 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"}                                    |                                      |
+----------------------------+------------------+-----------------------------------------------------------------------------------------------------+-------------------------------------

  3. To see the records of scale-out or scale-down operations that heat collects for the existing stack, 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

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

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

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