Runner Fleet Dashboard - AI recommendations: recommend the optimal runner for job execution

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Problem(s)

A customer wants to optimize their runner resource utilization.

Scenarios

GitLab SaaS Runners:

• Customer is on GitLab SaaS but is looking to be able to track when a user tags their job saas-linux-large-amd64 but they only use resources that would happily sit inside saas-linux-small-amd64 One solution would be to extend the the jobs API to include resources allocated / resources consumed.

Note - we will likely end up moving the GitLab SaaS scenario into its own issue.

Self-Managed Runner Fleet

• Customers that self-manage a runner fleet and offer different compute types are interested in quickly determining the optimal compute resource on which to run a CI job efficiently and with minimal impact on time-to-result

Proposal

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Target Persona(s)

• Priyanka - platform engineer

• Sasha - software developer

Note: While Priyanka is the primary target persona for the Fleet Dashboard, we are also thinking about how to surface runner resource optimization insights to Sasha in line with daily software development workflows.