Build Queue Calculator
Estimate how long a build waits in the Jenkins queue based on queue length, available executors, and average build time.
Inputs
Number of builds waiting ahead of (and including) this one.
Total executor slots across all agents able to run this build.
Average time a single build occupies an executor.
Estimated Queue Wait Time
18.00min
Estimated Queue Wait Time
0.300hr
Queue Rounds (Batches of Executors)
3.00
Step by step
Queue rounds = queue length ÷ executors
12 ÷ 4
= 3.00 rounds
Wait time = queue rounds × avg build time
3.00 × 6min
= 18.00 min
How it works
When more builds are queued than there are free executors, Jenkins processes them in batches roughly the size of the executor pool, so a build's expected wait time is proportional to how many 'rounds' of builds must complete ahead of it. Dividing queue length by executor count gives the number of rounds, and multiplying by average build time converts that into an estimated wall-clock wait — a simplified model that assumes builds are homogeneous and executors are fungible across them.
Formula
waitTime = (queueLength / executors) × avgBuildTime
- Q
- Builds currently queued
- E
- Available executors
- t_{avg}
- Average build time in minutes
- W
- Estimated queue wait time in minutes
Frequently Asked Questions
Why might real wait times differ from this estimate?
Real queues have heterogeneous build durations, executor labels/restrictions that prevent some builds from using all executors, and priority/quiet-period settings — all of which this simplified average-based model doesn't capture.
What's the fastest way to reduce queue wait time?
Add more executors (more agents or higher per-agent executor count), reduce average build time itself (caching, parallelism), or use build throttling/priority plugins to ensure critical builds aren't stuck behind low-priority ones.
Does this account for builds that need a specific label?
No — it assumes any of the available executors can run the queued builds; in practice, label-restricted builds (e.g. requiring a GPU or specific OS) effectively queue against a smaller executor pool than the cluster-wide total.
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