• Overview
• System requirements
• Supported cloud providers
• Runner configuration
  • Runner global options
  • [[runners]] options
  • [runners.machine] options
  • [runners.cache] options
  • Additional configuration information
• Autoscaling algorithm and parameters
• How concurrent, limit and IdleCount generate the upper limit of running machines
• Autoscaling periods configuration
• Off Peak time mode configuration (Deprecated)
• Distributed runners caching
• Distributed container registry mirroring
• A complete example of config.toml

Runners autoscale configuration

Autoscale provides the ability to utilize resources in a more elastic and dynamic way.

GitLab Runner can autoscale, so that your infrastructure contains only as many build instances as are necessary at any time. If you configure GitLab Runner to only use autoscale, the system on which GitLab Runner is installed acts as a bastion for all the machines it creates. This machine is referred to as a “Runner Manager.”

Overview

When this feature is enabled and configured properly, jobs are executed on machines created on demand. Those machines, after the job is finished, can wait to run the next jobs or can be removed after the configured IdleTime. In case of many cloud providers this helps to utilize the cost of already used instances.

Below, you can see a real life example of the GitLab Runner autoscale feature, tested on GitLab.com for the GitLab Community Edition project:

Each machine on the chart is an independent cloud instance, running jobs inside of Docker containers.

System requirements

Before configuring autoscale, you must:

• Prepare your own environment.
• Optionally use a forked version of Docker machine supplied by GitLab, which has some additional fixes.

Supported cloud providers

The autoscale mechanism is based on Docker Machine. All supported virtualization/cloud provider parameters, are available at the Docker Machine drivers documentation.

Runner configuration

This section describes the significant autoscale parameters. For more configurations details read the advanced configuration.

Runner global options

[[runners]] options

[runners.machine] options

Configuration parameters details can be found in GitLab Runner - Advanced Configuration - The [runners.machine] section.

[runners.cache] options

Configuration parameters details can be found in GitLab Runner - Advanced Configuration - The [runners.cache] section

Additional configuration information

There is also a special mode, when you set IdleCount = 0. In this mode, machines are always created on-demand before each job (if there is no available machine in Idle state). After the job is finished, the autoscaling algorithm works the same as it is described below. The machine is waiting for the next jobs, and if no one is executed, after the IdleTime period, the machine is removed. If there are no jobs, there are no machines in Idle state.

Autoscaling algorithm and parameters

The autoscaling algorithm is based on these parameters:

• IdleCount
• IdleTime
• MaxGrowthRate
• limit

We say that each machine that does not run a job is in Idle state. When GitLab Runner is in autoscale mode, it monitors all machines and ensures that there is always an IdleCount of machines in Idle state.

If there is an insufficient number of Idle machines, GitLab Runner starts provisioning new machines, subject to the MaxGrowthRate limit. Requests for machines above the MaxGrowthRate value are put on hold until the number of machines being created falls below MaxGrowthRate.

At the same time, GitLab Runner is checking the duration of the Idle state of each machine. If the time exceeds the IdleTime value, the machine is automatically removed.

Example: Let’s suppose, that we have configured GitLab Runner with the following autoscale parameters:

[[runners]]
  limit = 10
  # (...)
  executor = "docker+machine"
  [runners.machine]
    MaxGrowthRate = 1
    IdleCount = 2
    IdleTime = 1800
    # (...)

At the beginning, when no jobs are queued, GitLab Runner starts two machines (IdleCount = 2), and sets them in Idle state. Notice that we have also set IdleTime to 30 minutes (IdleTime = 1800).

Now, let’s assume that 5 jobs are queued in GitLab CI. The first 2 jobs are sent to the Idle machines of which we have two. GitLab Runner now notices that the number of Idle is less than IdleCount (0 < 2), so it starts new machines. These machines are provisioned sequentially, to prevent exceeding the MaxGrowthRate.

The remaining 3 jobs are assigned to the first machine that is ready. As an optimization, this can be a machine that was busy, but has now completed its job, or it can be a newly provisioned machine. For the sake of this example, let us assume that provisioning is fast, and the provisioning of new machines completed before any of the earlier jobs completed.

We now have 1 Idle machine, so GitLab Runner starts another 1 new machine to satisfy IdleCount. Because there are no new jobs in queue, those two machines stay in Idle state and GitLab Runner is satisfied.

This is what happened: We had 2 machines, waiting in Idle state for new jobs. After the 5 jobs where queued, new machines were created, so in total we had 7 machines. Five of them were running jobs, and 2 were in Idle state, waiting for the next jobs.

The algorithm still works the same way; GitLab Runner creates a new Idle machine for each machine used for the job execution until IdleCount is satisfied. Those machines are created up to the number defined by limit parameter. If GitLab Runner notices that there is a limit number of total created machines, it stops autoscaling, and new jobs must wait in the job queue until machines start returning to Idle state.

In the above example we always have two idle machines. The IdleTime applies only when we are over the IdleCount. Then we try to reduce the number of machines to IdleCount.

Scaling down: After the job is finished, the machine is set to Idle state and is waiting for the next jobs to be executed. Let’s suppose that we have no new jobs in the queue. After the time designated by IdleTime passes, the Idle machines are removed. In our example, after 30 minutes, all machines are removed (each machine after 30 minutes from when last job execution ended) and GitLab Runner starts to keep an IdleCount of Idle machines running, just like at the beginning of the example.

So, to sum up:

1. We start GitLab Runner
2. GitLab Runner creates 2 idle machines
3. GitLab Runner picks one job
4. GitLab Runner creates one more machine to fulfill the strong requirement of always having the two idle machines
5. Job finishes, we have 3 idle machines
6. When one of the three idle machines goes over IdleTime from the time when last time it picked the job it is removed
7. GitLab Runner always has at least 2 idle machines waiting for fast picking of the jobs

Below you can see a comparison chart of jobs statuses and machines statuses in time:

How concurrent, limit and IdleCount generate the upper limit of running machines

A magic equation doesn’t exist to tell you what to set limit or concurrent to. Act according to your needs. Having IdleCount of Idle machines is a speedup feature. You don’t need to wait 10s/20s/30s for the instance to be created. But as a user, you’d want all your machines (for which you need to pay) to be running jobs, not stay in Idle state. So you should have concurrent and limit set to values that run the maximum count of machines you are willing to pay for. As for IdleCount, it should be set to a value that generates a minimum amount of not used machines when the job queue is empty.

Let’s assume the following example:

concurrent=20

[[runners]]
  limit = 40
  [runners.machine]
    IdleCount = 10

In the above scenario the total amount of machines we could have is 30. The limit of total machines (building and idle) can be 40. We can have 10 idle machines but the concurrent jobs are 20. So in total we can have 20 concurrent machines running jobs and 10 idle, summing up to 30.

But what happens if the limit is less than the total amount of machines that could be created? The example below explains that case:

concurrent=20

[[runners]]
  limit = 25
  [runners.machine]
    IdleCount = 10

In this example, you can have a maximum of 20 concurrent jobs and 25 machines. In the worst case scenario, you can’t have 10 idle machines, but only 5, because the limit is 25.

Autoscaling periods configuration

Autoscaling can be configured to have different values depending on the time period. Organizations might have regular times when spikes of jobs are being executed, and other times with few to no jobs. For example, most commercial companies work from Monday to Friday in fixed hours, like 10am to 6pm. On nights and weekends for the rest of the week, and on the weekends, no pipelines are started.

These periods can be configured with the help of [[runners.machine.autoscaling]] sections. Each of them supports setting IdleCount and IdleTime based on a set of Periods.

How autoscaling periods work

In the [runners.machine] settings, you can add multiple [[runners.machine.autoscaling]] sections, each one with its own IdleCount, IdleTime, Periods and Timezone properties. A section should be defined for each configuration, proceeding in order from the most general scenario to the most specific scenario.

All sections are parsed. The last one to match the current time is active. If none match, the values from the root of [runners.machine] are used.

For example:

[runners.machine]
  MachineName = "auto-scale-%s"
  MachineDriver = "google"
  IdleCount = 10
  IdleTime = 1800
  [[runners.machine.autoscaling]]
    Periods = ["* * 9-17 * * mon-fri *"]
    IdleCount = 50
    IdleTime = 3600
    Timezone = "UTC"
  [[runners.machine.autoscaling]]
    Periods = ["* * * * * sat,sun *"]
    IdleCount = 5
    IdleTime = 60
    Timezone = "UTC"

In this configuration, every weekday between 9 and 16:59 UTC, machines are overprovisioned to handle the large traffic during operating hours. On the weekend, IdleCount drops to 5 to account for the drop in traffic. The rest of the time, the values are taken from the defaults in the root - IdleCount = 10 and IdleTime = 1800.

You can specify the Timezone of a period, for example "Australia/Sydney". If you don’t, the system setting of the host machine of every runner is used. This default can be stated as Timezone = "Local" explicitly.

More information about the syntax of [[runner.machine.autoscaling]] sections can be found in GitLab Runner - Advanced Configuration - The [runners.machine] section.

Off Peak time mode configuration (Deprecated)

Autoscale can no longer be configured with Off Peak time mode periods. Convert it into autoscaling periods instead.

Converting an Off Peak configuration into an autoscaling one

To convert an Off Peak Configuration into an autoscaling one, create a [[runners.machine.autoscaling]] section and populate it as follows:

• Periods field takes the value from OffpeakPeriods
• IdleCount field takes the value from OffpeakIdleCount
• IdleTime field takes the value from OffpeakIdleTime
• Timezone field takes the value from OffpeakTimezone

As an example, consider the conversion of the following Offpeak configuration:

[runners.machine]
  MachineName = "auto-scale-%s"
  MachineDriver = "google"
  IdleCount = 10
  IdleTime = 1800
  OffPeakPeriods = ["* * 9-17 * * mon-fri *"]
  OffPeakIdleCount = 50
  OffPeakIdleTime = 3600
  OffPeakTimezone = "UTC"

The conversion results in:

[runners.machine]
  MachineName = "auto-scale-%s"
  MachineDriver = "google"
  IdleCount = 10
  IdleTime = 1800
  [[runners.machine.autoscaling]]
    Periods = ["* * 9-17 * * mon-fri *"]
    IdleCount = 50
    IdleTime = 3600
    Timezone = "UTC"

Distributed runners caching

To speed up your jobs, GitLab Runner provides a cache mechanism where selected directories and/or files are saved and shared between subsequent jobs.

This is working fine when jobs are run on the same host, but when you start using the GitLab Runner autoscale feature, most of your jobs run on a new (or almost new) host, which executes each job in a new Docker container. In that case, you can’t take advantage of the cache feature.

To overcome this issue, together with the autoscale feature, the distributed runners cache feature was introduced.

It uses configured object storage server to share the cache between used Docker hosts. When restoring and archiving the cache, GitLab Runner queried the server and downloaded or uploaded the archive respectively.

To enable distributed caching, you have to define it in config.toml using the [runners.cache] directive:

[[runners]]
  limit = 10
  executor = "docker+machine"
  [runners.cache]
    Type = "s3"
    Path = "path/to/prefix"
    Shared = false
    [runners.cache.s3]
      ServerAddress = "s3.example.com"
      AccessKey = "access-key"
      SecretKey = "secret-key"
      BucketName = "runner"
      Insecure = false

In the example above, the S3 URLs follow the structure http(s)://<ServerAddress>/<BucketName>/<Path>/runner/<runner-id>/project/<id>/<cache-key>.

To share the cache between two or more runners, set the Shared flag to true. This flag removes the runner token from the URL (runner/<runner-id>) and all configured runners share the same cache. You can also set Path to separate caches between runners when cache sharing is enabled.

Distributed container registry mirroring

To speed up jobs executed inside of Docker containers, you can use the Docker registry mirroring service. This service provides a proxy between your Docker machines and all used registries. Images are downloaded one time by the registry mirror. On each new host, or on an existing host where the image is not available, the image is downloaded from the configured registry mirror.

Provided that the mirror exists in your Docker machines LAN, the image downloading step should be much faster on each host.

To configure the Docker registry mirroring, you have to add MachineOptions to the configuration in config.toml:

[[runners]]
  limit = 10
  executor = "docker+machine"
  [runners.machine]
    (...)
    MachineOptions = [
      (...)
      "engine-registry-mirror=http://10.11.12.13:12345"
    ]

Where 10.11.12.13:12345 is the IP address and port where your registry mirror is listening for connections from the Docker service. It must be accessible for each host created by Docker Machine.

Read more about how to use a proxy for containers.

A complete example of config.toml

The config.toml below uses the google Docker Machine driver:

concurrent = 50   # All registered runners can run up to 50 concurrent jobs

[[runners]]
  url = "https://gitlab.com"
  token = "RUNNER_TOKEN"             # Note this is different from the registration token used by `gitlab-runner register`
  name = "autoscale-runner"
  executor = "docker+machine"        # This runner is using the 'docker+machine' executor
  limit = 10                         # This runner can execute up to 10 jobs (created machines)
  [runners.docker]
    image = "ruby:2.6"               # The default image used for jobs is 'ruby:2.6'
  [runners.machine]
    IdleCount = 5                    # There must be 5 machines in Idle state - when Off Peak time mode is off
    IdleTime = 600                   # Each machine can be in Idle state up to 600 seconds (after this it will be removed) - when Off Peak time mode is off
    MaxBuilds = 100                  # Each machine can handle up to 100 jobs in a row (after this it will be removed)
    MachineName = "auto-scale-%s"    # Each machine will have a unique name ('%s' is required)
    MachineDriver = "google" # Refer to Docker Machine docs on how to authenticate: https://docs.docker.com/machine/drivers/gce/#credentials
    MachineOptions = [
      "google-project=GOOGLE-PROJECT-ID",
      "google-zone=GOOGLE-ZONE", # e.g. 'us-central-1'
      "google-machine-type=GOOGLE-MACHINE-TYPE", # e.g. 'n1-standard-8'
      "google-machine-image=ubuntu-os-cloud/global/images/family/ubuntu-1804-lts",
      "google-username=root",
      "google-use-internal-ip",
      "engine-registry-mirror=https://mirror.gcr.io"
    ]
    [[runners.machine.autoscaling]]  # Define periods with different settings
      Periods = ["* * 9-17 * * mon-fri *"] # Every workday between 9 and 17 UTC
      IdleCount = 50
      IdleTime = 3600
      Timezone = "UTC"
    [[runners.machine.autoscaling]]
      Periods = ["* * * * * sat,sun *"] # During the weekends
      IdleCount = 5
      IdleTime = 60
      Timezone = "UTC"
  [runners.cache]
    Type = "s3"
    [runners.cache.s3]
      ServerAddress = "s3-eu-west-1.amazonaws.com"
      AccessKey = "AMAZON_S3_ACCESS_KEY"
      SecretKey = "AMAZON_S3_SECRET_KEY"
      BucketName = "runner"
      Insecure = false

Note that the MachineOptions parameter contains options for the google driver which is used by Docker Machine to spawn machines hosted on Google Compute Engine, and one option for Docker Machine itself (engine-registry-mirror).