Scaling Self-hosted Runners
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Maintaining a fixed compute fleet for CircleCI self-hosted runners can incur unnecessary costs as the workload can fluctuate depending on the rate at which jobs are queued. To help reduce this cost, the compute fleet can be scaled according to the demand.
There are several API endpoints to help you set up a solution to scale data:
A scaling solution can use the above endpoints to calculate the total number of waiting tasks which can be run. The task data endpoints are scoped to a single resource class, so it’s important to query every available resource class to get the total number of running tasks.
The scaling solution is then free to decide which resource class to add new claiming agents for. The distribution of tasks across resource classes is dependent on the incoming workload and internal dispatching decisions made by CircleCI.
NOTE: You will also need to be aware of your plan’s concurrency limit to avoid starting compute which cannot be used. This can be found on the CircleCI Plans page. Scale plan customers will need to contact support to get this information.
There are some
launch-agent configuration settings which can be used by your scaling solution, particularly to assist resource cleanup when the demand drops:
single-taskmode will cause the
launch-agentto shut down after a single task. This is useful if using completely ephemeral compute as the resources can be automatically recycled upon
continuousmode will cause the
launch-agentto poll for new tasks after completing a task. Your scaling solution will need to monitor the task workload and actively shutdown unused
Setting a reasonable timeout can be used for automatic resource recycling during periods of lower demand.
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