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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A queued GitHub Actions job is not automatically a runner outage. First determine whether the job is waiting for an environment approval, an eligible runner, workflow-concurrency capacity, or GitHub scheduling capacity. Open the run and job details, identify the runner model, then follow the matching diagnostic branch below.
The quickest fixes are different for each case: approve or repair an environment gate, free or add hosted capacity, bring an eligible self-hosted runner online, correct labels or groups, change an intentional concurrency policy, or move a scheduled workflow away from the top of the hour.
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Start by identifying what is actually waiting
Open the repository on GitHub, select Actions, open the affected workflow run, and select the job that shows a queued or pending state. Read the job details before changing runner settings. The state usually falls into one of two broad categories: GitHub has not assigned execution capacity, or the workflow is blocked by a policy that must be satisfied first.
- Check for an environment reference. A deployment job that names an environment can wait for required reviewers, wait timers, or other deployment protection rules. It will not start merely because a runner is idle. Review the environment’s rules using GitHub’s deployment protection guidance, then approve the deployment or fix the rule that is preventing approval.
- Read the job’s runner requirement. Inspect
runs-onin the workflow and note whether it selects a GitHub-hosted standard image, a larger hosted runner, a self-hosted label set, or an Actions Runner Controller (ARC) scale set. - Record the scope. Note the repository, organization, enterprise, runner group, and environment involved. Capacity and access are applied at different scopes, so a healthy runner in another group may not be eligible.
- Check whether newer runs are replacing the pending run. A concurrency group can make a run appear stuck even when scheduling is working. Inspect the workflow YAML for a top-level or job-level
concurrencykey before treating the state as an infrastructure failure.
Use the GitHub runner concepts overview to confirm the terminology and execution model shown in the run details.
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Match the queue to its runner model
| Execution target | What the queue usually represents | First check | Typical corrective action |
|---|---|---|---|
| GitHub-hosted standard runner | The account or organization has reached its concurrent-job allowance, or GitHub is experiencing scheduling load. | Organization or enterprise Settings → Actions → Runners, then the hosted-runner usage view. | Wait for active jobs to finish, reduce parallel work, or request additional concurrency where available. |
| GitHub-hosted larger runner | Hosted capacity is unavailable, or repository access and billing requirements are not satisfied. | Larger-runner assignment, access scope, and billing configuration. | Correct access or billing, then reassess capacity for that runner class. |
| Self-hosted runner | No online, idle runner matches every requested label and runner group, or the runner cannot communicate with GitHub. | Runner status, labels, groups, service process, and application logs. | Restore the service and network, correct labels or group access, or add an eligible runner. |
| ARC scale set | The controller cannot create enough runner pods, or scaling limits intentionally prevent new pods. | Scale-set minRunners, maxRunners, controller health, and pod events. |
Repair the controller or adjust the scale range while preserving cluster capacity. |
Branch A: GitHub-hosted jobs are at the concurrency ceiling
GitHub-hosted jobs consume concurrent-job slots while they run. GitHub states that when the maximum is reached, new jobs enter a queue. In organization or enterprise settings, go to Settings → Actions → Runners, select the GitHub-hosted runner entry, and inspect both Active jobs and All jobs usage. Compare the number of active jobs with the allowance for the account and runner type.
Interpret the documented limits in context
The following figures are listed in GitHub Enterprise Cloud’s current Actions limits documentation, accessed October 13, 2026. They are plan-specific reference values, not a universal limit for every GitHub product, account, or runner class.
| Enterprise Cloud plan | Concurrent standard hosted jobs | Concurrent macOS jobs |
|---|---|---|
| Free | 20 | 5 |
| Pro | 40 | 5 |
| Team | 60 | 5 |
| Enterprise | 500 | 50 |
Verify the applicable plan and product documentation immediately before relying on those numbers; GitHub changes limits and account policies. If demand regularly reaches the ceiling, GitHub says support can increase concurrency limits in eligible situations. Before requesting an increase, measure whether the burst is caused by a workflow fan-out, a stuck job consuming a slot, or several repositories sharing the same allowance.
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- Let long-running jobs finish and cancel obsolete runs when the result is no longer useful.
- Split unrelated workloads across workflows only when doing so changes scheduling or ownership; creating more jobs does not create more hosted capacity.
- Limit matrix expansion for pull requests and reserve broad matrices for protected branches or releases.
- Check whether macOS or another specialized image is the constrained class even when standard-runner capacity remains available.
- For larger runners, verify that the repository is authorized to use the runner and that the required billing configuration is active. A general hosted-job allowance does not prove that a larger runner is available.
If active-job counts are below the applicable ceiling and the queue is still growing, move to the service-load and scheduled-workflow checks rather than repeatedly changing workflow YAML.
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Branch B: a self-hosted runner is not eligible or healthy
GitHub assigns a self-hosted job only to a runner that is online, idle, permitted for the repository, and matches every label and runner-group requirement. One matching label is not enough: a job requesting [self-hosted, linux, x64] needs a runner carrying all three labels and access to the referenced group.
Verify eligibility in the runner settings
- Open the repository, organization, or enterprise Settings → Actions → Runners page at the scope where the runner is registered.
- Confirm that at least one matching runner shows Online and is not already executing another job.
- Compare the job’s
runs-onlabels with the runner’s labels character by character. Labels are not interchangeable aliases, and a typo leaves the job waiting indefinitely. - Check runner-group policy. A runner can be online yet unavailable because the repository is not included in the group’s permitted repositories.
- Confirm that the runner application service is running under the intended account and that the host has not been suspended, rebooted into maintenance, or disconnected from the network.
Use the queue timing clues
GitHub’s self-hosted runner behavior provides two useful boundaries. When an online matching runner is assigned a job but does not accept it within 60 seconds, GitHub re-queues the job so another runner can try. A self-hosted job that remains queued for more than 24 hours fails. These are scheduling safeguards, not retry settings you can extend; treat repeated re-queues as evidence of a runner-side fault.
Check connectivity and logs
Inspect the runner service log and the runner application’s diagnostic files for authentication failures, certificate errors, DNS failures, proxy rejection, disk exhaustion, or permission errors. GitHub documents a network test through the runner configuration script’s --check option; run that test on the runner host and resolve any endpoint or firewall failures before restarting the service. Organization firewalls, outbound proxies, DNS filtering, and third-party network controls can all leave a process apparently running while preventing job pickup.
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Keep the runner application current
GitHub stops queueing jobs to a self-hosted runner that has not performed a software update within 30 days. A required critical security update can also block queueing before that interval. Build runner updates into host maintenance, monitor the installed version, and read the service log after an update to confirm that the runner reconnects. An online indicator alone does not prove that the application is within the supported update window.
Branch C: ARC scale-set capacity is intentionally or accidentally zero
With Actions Runner Controller, the controller creates ephemeral runner pods for a scale set. Check both the controller and the scale-set configuration when jobs wait without a corresponding self-hosted machine.
Inspect minimum and maximum runners
minRunners establishes the baseline number of runners and maxRunners caps scale-out. If both are set to zero, no new pods are created; this is an intentional maintenance configuration that drains existing work while leaving new jobs queued. Restore a positive minimum or allow scale-out only after confirming that the cluster can supply CPU, memory, and network capacity.
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Check the controller path
- Confirm that the controller deployment is healthy and can authenticate to GitHub.
- Inspect controller logs for token, webhook, API-rate, or reconciliation errors.
- Review pending pod events for image-pull failures, insufficient cluster resources, admission-policy denials, or node-selector mismatches.
- Verify that the scale-set labels and runner group still match the workflow’s
runs-onexpression.
Once a pod registers, the job should leave the queue only if the newly created runner is eligible for that exact job. Scaling a different scale set does not help a label or group mismatch.
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Branch D: workflow concurrency is deliberately holding runs
A concurrency group controls workflow semantics independently of runner availability. By default, a group permits one running run and one pending run. When a newer run becomes pending, GitHub cancels the older pending run rather than retaining an unbounded backlog.
concurrency:
group: ci-${{ github.ref }}
cancel-in-progress: trueThis pattern is useful for pull-request validation when only the newest commit matters. It is the wrong policy for migrations, releases, or audit jobs where every run must complete. In those cases, configure queueing behavior that retains runs in order, and make sure the resulting workload fits the available runner capacity. A pending run replaced by a newer run is a concurrency outcome, not proof that a runner is offline.
Diagnose a concurrency group
- Search the workflow and any reusable workflow it calls for
concurrency. - Determine the group key after expressions resolve. A key based on branch, environment, or repository can serialize more work than intended.
- Check whether
cancel-in-progressis enabled and whether cancellation is acceptable for that workload. - Change the policy only after deciding whether freshness or completion of every run is the requirement.
Branch E: scheduled workflows are colliding with platform load
GitHub reports that platform load is high near the start of every hour. Under sufficiently high load, some scheduled jobs can be delayed or dropped. This warning applies specifically to scheduled workflows; it is not a general explanation for a continuously queued pull-request job.
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Use metrics and logs to prove the bottleneck
Actions metrics can show average queue time, runtime, and failure rates by workflow, job, repository, and runner type. Compare those categories instead of relying on one run: a high queue time only for macOS jobs points to specialized capacity, while high queue time for one self-hosted label points to fleet eligibility or health.
Read the run timeline
- Record when the job entered the queue, when a runner was assigned, and when execution began.
- Compare queued jobs with active jobs to see whether the backlog follows a capacity ceiling.
- Look for a repeating branch, label, environment, or schedule pattern.
- Enable debug logging when standard logs do not show runner registration, network, or action-resolution details. Protect tokens and secrets when sharing diagnostic output.
Metrics turn a one-off delay into an operational decision: add capacity for a recurring, measurable bottleneck; repair labels or hosts for a narrow class of failures; or change workflow policy when obsolete runs are consuming slots.
Check billing, permissions, networking, and GitHub service status
GitHub’s troubleshooting guidance treats runner minutes and artifact storage as usage that can be affected by budgets and billing controls. Review organization or enterprise spending limits, payment status, and storage usage when jobs stop across multiple repositories. A billing or budget restriction can look like a capacity problem even though runners are healthy.
For self-hosted fleets, inspect outbound firewall rules, DNS resolution, proxy settings, certificate trust, and any third-party security service between the runner and GitHub. Confirm repository and runner-group permissions after ownership or organization changes. If several unrelated repositories show the same new delay and local checks are clean, consult GitHub Status and contact GitHub Support with run URLs, timestamps, runner type, and diagnostic logs.
Quick Recap
A practical decision tree for the next action
| Observed evidence | Next action |
|---|---|
| The job names an environment and shows a protection or approval wait. | Review the environment rule, obtain the required approval, or correct the protection configuration. |
| Hosted active jobs equal the account’s applicable concurrency allowance. | Wait for completion, reduce unnecessary parallelism, or pursue an approved concurrency increase. |
| No self-hosted runner is both online and idle with all requested labels and group access. | Bring up or repair an eligible runner, or correct the workflow’s labels and group. |
| An eligible self-hosted runner repeatedly receives and then loses assignments. | Inspect the runner service, network test, permissions, and application logs for acceptance or connectivity failures. |
| ARC shows no pods while minimum and maximum capacity are zero. | Restore an intentional maintenance setting only after validating cluster and controller health. |
| Only the newest run remains pending in a shared group. | Review concurrency cancellation and serialization rules; decide whether stale runs should be discarded. |
| Delays cluster around scheduled jobs near the hour. | Move cron times away from the top of the hour and monitor for missed schedules. |
| Queues affect unrelated repositories at the same time. | Check billing, organization policy, network controls, GitHub Status, and support channels. |
Prevent the queue from returning
- Document runner intent. Record each label, runner group, repository scope, operating system, architecture, and maintenance owner.
- Automate health checks. Alert when a runner is offline, has no recent heartbeat, loses network access, or approaches the update-age policy.
- Reserve capacity for critical work. Separate release or deployment runners from bursty pull-request matrices when the organization’s access model permits it.
- Choose concurrency deliberately. Cancel obsolete validation runs; retain every run for compliance, migration, and release workflows.
- Measure queue time by runner class. A single organization-wide average can hide a saturated specialized runner or a broken label.
- Stagger schedules. Avoid placing many cron expressions at the same minute, especially at the beginning of an hour.
- Review budgets and artifacts. Runner minutes, artifact retention, and spending controls should be part of capacity planning, not an after-the-fact investigation.
- Test recovery. Periodically run a small job through each important label, group, environment, and scale set so an access or update failure is found before a release is waiting.
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