---
id: "INC-circleci-jn756xtsy4xg"
kind: "incident"
aliases: []
title: "CircleCI: Elevated wait times for machine jobs"
severity: "medium"
cvss: null
epss: null
exploited: false
status: "resolved"
category: "vendor"
products: [{"name": "CircleCI", "fixed": [], "affected": "", "ecosystem": "vendor"}]
ecosystems: ["vendor"]
tags: ["ci", "circleci", "incident", "security", "vendor"]
not_affected_if: ["jobs-not-in-incident-window", "no-circleci-jobs"]
published_at: "2026-10-08T13:00:30+00:00"
first_seen_at: "2026-10-23T19:12:22+00:00"
updated_at: "2026-10-23T20:20:09+00:00"
sources: [{"source": "statuspage", "url": "https://stspg.io/vcsrgfwdsy4z", "first_seen_at": "2026-10-23T19:12:22+00:00"}]
---

# CircleCI: Elevated wait times for machine jobs

CircleCI had a series of incidents from October 1 to 8, 2026 where jobs waited longer than normal or failed to start, mostly due to cloud instance shortages and a scheduler disk configuration issue. No security compromise is described; jobs were delayed, not lost, and the incidents are resolved.

_Summary, check and fix written by a model from the sources; labelled as such._

## Affected

- vendor:CircleCI affected ? fixed ?

## Fix

No customer action is required per the source; customers whose jobs failed with an infrastructure error during October 7 14:24 to 15:21 UTC can rerun them.

## Timeline

- 2026-10-23T19:12:22+00:00 statuspage https://stspg.io/vcsrgfwdsy4z

## From the sources

## **Summary**

Between October 1 and October 8, 2026, CircleCI customers experienced five separate incidents in which jobs waited longer than normal to start, or failed to start. Three of the incidents were driven by limited instance availability from our cloud provider. One was caused by a configuration issue in our scheduling infrastructure, and one by memory limits on an internal service, which we raised during the incident.

* **October 1, 07:57 to 10:24 UTC:** Docker Gen 2 jobs waited up to 30 minutes to start. A configuration issue prevented the Gen 2 scheduling servers from using their high-performance local disks. During a burst in traffic, the fallback disks saturated and job placement slowed.
* **October 7, 13:27 to 14:06 UTC:** Machine jobs waited longer than normal because we could not acquire enough virtual machines from our cloud provider.
* **October 7, 14:24 to 15:21 UTC:** Some jobs failed with an infrastructure error, and workflows were delayed. The service that gives each job its configuration at startup ran out of memory and restarted.
* **October 7, 18:49 to 20:05 UTC:** Docker Gen 2 jobs waited longer than normal because we could not acquire enough instances from our cloud provider.
* **October 8, 12:48 to 16:00 UTC:** Linux machine jobs and remote Docker jobs waited up to 50 minutes to start. Our cloud provider did not have enough of the instance type we request, and a defect in the logic that picks a different instance type kept us from using alternatives.

Jobs that waited during these incidents were delayed, not lost. Customers whose jobs failed with an infrastructure error during the October 7 incident from 14:24 to 15:21 UTC can rerun them.

The original status pages can be found below:

* [October 1 - Delays starting Gen 2 Docker Jobs](https://status.circleci.com/incidents/22mrv46g6n7g)
* [October 7 - Delay on starting Machine Job Tasks](https://status.circleci.com/incidents/tj6wdnfjwr64)
* [October 7 - Elevated level of infra fails on customer jobs](https://status.circleci.com/incidents/0z31ldjd8m5v)
* [October 7 - Increased task wait times for Docker Gen2](https://status.circleci.com/incidents/39rfrjmk5pvd)
* [October 8 - Elevated wait times for machine jobs](https://status.circleci.com/incidents/jn756xtsy4xg)

## **Background**

Every job that runs on CircleCI needs compute. Where that compute comes from depends on the executor.

Docker jobs run on clusters of servers that schedule work onto a fleet of instances. The scheduler is the software on those servers that decides which instance runs each job. Gen 2 Docker jobs ran on one such cluster at the time of the October 1 incident. Gen 1 Docker jobs are spread across several clusters.

Machine jobs, including remote Docker jobs, get a dedicated virtual machine. A remote Docker job is handled as a Linux machine job from submission through provisioning. Our machine provisioning service requests each machine from our cloud provider in one of the two regions we normally use for machine jobs. When the provider cannot supply the instance type we request, the service falls back: it requests a different instance type, or a different region, from a list we maintain. A job waits until a machine is available.

When any job starts, it asks a job configuration service for its configuration. That service runs as a set of pods, which are small copies of the same program. If too many pods stop, the remaining pods carry more load, and jobs cannot start until the service recovers.

These paths depend on three kinds of capacity: the throughput of our scheduling systems, the supply of instances from our cloud provider, and the memory and number of copies of the services that start jobs. A shortfall in any of them makes jobs wait or fail.

## **What Happened**

_\(All times UTC\)_

### **October 1: Docker Gen 2 jobs delayed up to 30 minutes**

At 07:44, disk activity on the servers that schedule Gen 2 Docker jobs rose to 90% of capacity and stayed there. At 07:57, the number of Docker jobs waiting to start began to grow, and wait times rose with it. An automated alert paged the on-call engineer at 08:08. We declared an incident at 08:42.

At 08:43, engineers identified high disk latency on the scheduling servers. They first believed the disks were too slow for the load, so they prepared a change to a faster disk type. At 09:01, the team agreed on two workstreams: move the servers to faster disks, and add a second Gen 2 cluster so that jobs would split across two clusters.

At 09:41, engineers found the root cause. Each scheduling server has fast disks attached directly to it, and the scheduler is meant to keep its working data there. These servers run on a security-hardened operating system image, and on that image the fast disks were not being used. The scheduler wrote its data to slower network-attached disks instead. The script that sets up the fast disks failed without an error, and a gap in logging on these servers hid the failure. Gen 2 traffic had also become increasingly bursty. The slower disks saturated during those bursts, and the scheduler fell behind.

Wait times peaked at 09:54, when jobs on the Gen 2 medium resource class waited up to 30 minutes. At 09:55, engineers applied a fix that set up the fast disks correctly. At 10:11, the scheduler began placing jobs faster, which confirmed the servers could keep up with new jobs. Wait times for Gen 2 medium fell to 13 minutes at 10:14, 7 minutes at 10:17, and 1 minute 30 seconds at 10:20. We moved the status page to monitoring at 10:24 and resolved the incident at 10:45.

At 11:20, the second Gen 2 cluster began running jobs.

### **October 7, 13:27 to 14:06: Elevated wait times for machine jobs**

We declared an incident at 13:27 after seeing elevated queueing and wait times. We posted to the status page at 13:31. At 13:31, engineers saw that we were struggling to acquire virtual machines for machine jobs, and that the number of jobs waiting to start was high mainly for machine jobs. At 13:34, wait times were elevated across executors.

At 13:48, wait times began to recover as more machines became available, and we resolved the incident at 14:06.

### **October 7, 14:24 to 15:21: Job failures and workflow delays**

Shortly after the previous incident was marked as resolved, we declared a new incident at 14:24 due to a sharp drop in the number of jobs submitted to our execution systems. At 14:26, engineers saw that pods of the job configuration service were restarting. Pods that restart leave fewer copies of the service to answer requests, and jobs that could not retrieve their configuration after repeated attempts failed with an infrastructure error. Workflows that were already running waited for updates from jobs that had not started.

At 14:35, engineers increased the amount of available memory to the pods and increased the number of pods, which gave the service more copies to share the load. We also increased the amount of memory available Job submissions began recovering, and the number of messages flowing through our workflow system returned to normal levels. At 14:44, we moved the status page to monitoring.

Pods in the job configuration service reached their memory limits and restarted. We increased memory allocations and pod counts, and the service recovered. At 15:13, we increased the number of instances in our Gen 2 Docker clusters so we could work through the backlog of waiting jobs quickly. We resolved the incident at 15:21.

### **October 7, 18:49 to 20:05: Elevated wait times for Docker Gen 2 jobs**

We declared a new incident at 18:49 after Docker Gen 2 wait times rose. At 18:51, engineers confirmed that a shortage of available instances kept us from acquiring enough of them, which raised wait times. Wait times differed by resource class size.

At 19:52, average wait times were lower than an hour earlier as more capacity became available, though brief spikes continued, and we moved the status page to monitoring. W

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