• 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
• Off Peak time mode configuration
• 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.

Thanks to Runners being able to autoscale, your infrastructure contains only as much build instances as necessary at anytime. If you configure the Runner to only use autoscale, the system on which the Runner is installed acts as a bastion for all the machines it creates.

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 runners 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

At this point you should have installed all the requirements. If not, make sure to do it before going over the configuration.

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

In this section we will describe only the significant parameters from the autoscale feature point of view. 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 three main parameters: IdleCount, IdleTime and 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.

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]
    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 2 new machines. Then, the next 2 jobs from the queue are sent to those newly created machines. Again, the number of Idle machines is less than IdleCount, so GitLab Runner starts 2 new machines and the last queued job is sent to one of the Idle machines.

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 will still work in the same way; GitLab Runner will create a new Idle machine for each machine used for the job execution until IdleCount is satisfied. Those machines will be created up to the number defined by limit parameter. If GitLab Runner notices that there is a limit number of total created machines, it will stop autoscaling, and new jobs will need to wait in the job queue until machines start returning to Idle state.

In the above example we will 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 will be removed. In our example, after 30 minutes, all machines will be removed (each machine after 30 minutes from when last job execution ended) and GitLab Runner will start to keep an IdleCount of Idle machines running, just like at the beginning of the example.

So, to sum up:

1. We start the Runner
2. Runner creates 2 idle machines
3. Runner picks one job
4. 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 will be removed
7. The Runner will always have 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

There doesn’t exist a magic equation that will 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 will run the maximum count of machines you are willing to pay for. As for IdleCount, it should be set to a value that will generate 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 we will have at most 20 concurrent jobs, and at most 25 machines created. In the worst case scenario regarding idle machines, we will not be able to have 10 idle machines, but only 5, because the limit is 25.

Off Peak time mode configuration

Autoscale can be configured with the support for Off Peak time mode periods.

What is Off Peak time mode period?

Some organizations can select a regular time periods when no work is done. For example most of commercial companies are working from Monday to Friday in a fixed hours, eg. from 10am to 6pm. In the rest of the week - from Monday to Friday at 12am-9am and 6pm-11pm and whole Saturday and Sunday - no one is working. These time periods we’re naming here as Off Peak.

Organizations where Off Peak time periods occurs probably don’t want to pay for the Idle machines when it’s certain that no jobs will be executed in this time. Especially when IdleCount is set to a big number.

In the v1.7 version of the Runner we’ve added the support for Off Peak configuration. With parameters described in configuration file you can now change the IdleCount and IdleTime values for the Off Peak time mode periods.

How it is working?

Configuration of Off Peak is done by four parameters: OffPeakPeriods, OffPeakTimezone, OffPeakIdleCount and OffPeakIdleTime. The OffPeakPeriods setting contains an array of cron-style patterns defining when the Off Peak time mode should be set on. For example:

[runners.machine]
  OffPeakPeriods = [
    "* * 0-9,18-23 * * mon-fri *",
    "* * * * * sat,sun *"
  ]

will enable the Off Peak periods described above, so the working days from 12am to 9am and from 6pm to 11pm and whole weekend days. Machines scheduler is checking all patterns from the array and if at least one of them describes current time, then the Off Peak time mode is enabled.

You can specify the OffPeakTimezone e.g. "Australia/Sydney". If you don’t, the system setting of the host machine of every runner will be used. This default can be stated as OffPeakTimezone = "Local" explicitly if you wish.

When the Off Peak time mode is enabled machines scheduler use OffPeakIdleCount instead of IdleCount setting and OffPeakIdleTime instead of IdleTime setting. The autoscaling algorithm is not changed, only the parameters. When machines scheduler discovers that none from the OffPeakPeriods pattern is fulfilled then it switches back to IdleCount and IdleTime settings.

More information about syntax of OffPeakPeriods patterns can be found in GitLab Runner - Advanced Configuration - The [runners.machine] section.

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 Runners autoscale feature, most of your jobs will be running on a new (or almost new) host, which will execute each job in a new Docker container. In that case, you will not be able to 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 will query the server and will download or upload 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. That will remove the runner token from the URL (runner/<runner-id>) and all configured Runners will share the same cache. Remember that 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 will provide a proxy between your Docker machines and all used registries. Images will be downloaded once by the registry mirror. On each new host, or on an existing host where the image is not available, it will be downloaded from the configured registry mirror.

Provided that the mirror will exist 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.

A complete example of config.toml

The config.toml below uses the digitalocean 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:2.1"               # The default image used for jobs is 'ruby:2.1'
  [runners.machine]
    OffPeakPeriods = [               # Set the Off Peak time mode on for:
      "* * 0-9,18-23 * * mon-fri *", # - Monday to Friday for 12am to 9am and 6pm to 11pm
      "* * * * * sat,sun *"          # - whole Saturday and Sunday
    ]
    OffPeakIdleCount = 1             # There must be 1 machine in Idle state - when Off Peak time mode is on
    OffPeakIdleTime = 1200           # Each machine can be in Idle state up to 1200 seconds (after this it will be removed) - when Off Peak time mode is on
    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 = "digitalocean"   # Docker Machine is using the 'digitalocean' driver
    MachineOptions = [
        "digitalocean-image=coreos-stable",
        "digitalocean-ssh-user=core",
        "digitalocean-access-token=DO_ACCESS_TOKEN",
        "digitalocean-region=nyc2",
        "digitalocean-size=4gb",
        "digitalocean-private-networking",
        "engine-registry-mirror=http://10.11.12.13:12345"   # Docker Machine is using registry mirroring
    ]
  [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 digitalocean driver which is used by Docker Machine to spawn machines hosted on Digital Ocean, and one option for Docker Machine itself (engine-registry-mirror).