Cases7 cases

1 to 5 of 7

1. IL-0166 A compute host update removed the credential parser used to decode StackScripts, so new Linodes and every new LKE and LKE-E node failed to boot and autoscaling and node recycling failed in all regions

   Linode2026

   Symptoms

   Linodes deployed with StackScripts failed to deploy and could not boot. Every newly created Linode Kubernetes Engine (LKE) and LKE-E node relies on StackScripts, so LKE autoscaling and node recycling failed as well, in all regions.

   Scale-up failure

   Trigger

   A recent update on the compute hosts removed a credential parser that is used when decoding StackScripts.

   Root cause

   Faulty deployment

   Component

   Cluster autoscalerNode pool

   Contributing factor

   –

   Detection

   The issue was identified at 18:18 UTC on 2026-05-15. The first status post went up at 00:42 UTC on 2026-05-16.

   Remediation

   • tested and applied a rollback starting at 01:10 UTC on 2026-05-16, and failures dropped from 01:20 UTC

   Lessons

   Removing a credential parser from the compute hosts broke the boot of every Linode deployed with StackScripts, and every new LKE and LKE-E node is one of them, so node creation, autoscaling and node recycling stopped in all regions. The rollback reduced failures ten minutes after testing began.

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2. IL-0097 A cluster autoscaler version bump was incompatible with the one cluster still on Kubernetes 1.33, so no scale-up events fired and new nodes were not created

   2i2c2026

   Symptoms

   At 15:10 (Bucharest time) on 2026-03-31 an alert fired for the prod hub on the berkeley-geojupyter cluster on AWS: "[FIRING:1] At least two servers failed to start in the last 30m cluster=berkeley-geojupyter hub=prod (provider=aws immediate action needed)". It opened PagerDuty incident #2057.

   Scale-up failure

   Trigger

   An upgrade merged on 2026-03-30 bumped the cluster autoscaler version, and the new version was incompatible with Kubernetes 1.33, which the berkeley-geojupyter cluster still ran.

   Root cause

   Breaking change in an upgrade

   Component

   Cluster autoscaler

   Contributing factor

   Deferred upgradesUnverified assumption

   Detection

   An alert that at least two servers failed to start in the last 30 minutes fired at 15:10 and opened PagerDuty incident #2057. The report notes it was triggered by an automatic health check run, not by users being unable to start servers.

   Remediation

   • upgraded the cluster's Kubernetes version from 1.33 to 1.34
   • downgraded the cluster autoscaler by one patch version to 9.55.0, the version meant for Kubernetes 1.34

   Action items

   • The change in https://github.com/2i2c-org/infrastructure/pull/8008 (the report gives only the link)
   • Use recurrent issues, such as the recurrent-k8s-aws-upgrades workflow, to standardize Kubernetes versions across clusters

   Lessons

   Because the context of the autoscaler bump was fresh, the team went down the right path from the start. The upgrade batches had been designed with only 1.32 clusters in mind, so the one 1.33 cluster was missed and got an incompatible autoscaler. 2i2c notes it was lucky that an automatic health check, not real users, hit the failure first.

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3. IL-0157 After a release, new nodes could not join Linode Kubernetes Engine clusters with IP ACL enabled, so autoscaling failed on about 20 to 25 clusters

   Linode2025

   Symptoms

   From about 01:00 UTC on 2025-03-05, Linode Kubernetes Engine (LKE) clusters with IP ACL enabled failed to autoscale: new nodes could not join the cluster, and some customers had service disruptions. About 20 to 25 clusters in several regions around the world were affected; the status page lists the US-IAD (Washington), US-ORD (Chicago), US-SEA (Seattle) and IN-MAA (Chennai) LKE components.

   Scale-up failureService outage

   Trigger

   Akamai deployed the latest LKE software release.

   Root cause

   Software defect

   Component

   Cluster autoscaler

   Contributing factor

   –

   Detection

   Customers began having problems at about 01:00 UTC, and the first status post, at 06:49 UTC, said the team was investigating an LKE autoscaling problem when IP ACL is turned on.

   Remediation

   • restarted the gateway pods, which gave only short-term relief
   • rolled back the release on all affected clusters

   Action items

   • Continue investigating the root cause and take preventive actions

   Lessons

   The defect hit only clusters with IP ACL enabled, and it showed when the autoscaler tried to add nodes. Restarting the gateway pods did not hold; rolling back the release ended the incident.

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4. IL-0096 A GKE node pool pinned to the europe-west2-b zone could not scale for 6.5 hours because Google ran out of resources in that zone (ZONE_RESOURCE_POOL_EXHAUSTED)

   2i2c2022

   Symptoms

   A user of the LIS JupyterHub reported a "backoff after failed scale-up" error, and user servers would not start. An engineer opened incident #36, "LIS hub cannot scale", at 5:40 AM Pacific on 14 November 2022.

   Scale-up failurePods PendingService outage

   Trigger

   At 10 AM UK time on 14 November 2022, the B zone of Google Cloud's europe-west2 region (London) ran out of resources to allocate. The LIS cluster's nodes were restricted to that zone.

   Root cause

   Cloud provider outage

   Component

   Node poolCluster autoscaler

   Contributing factor

   Single point of failurePages not delivered

   Detection

   A user reported the "backoff after failed scale-up" error; an engineer opened the incident at 5:40 AM Pacific, about three hours and forty minutes after the zone ran out of resources.

   Remediation

   • edited the node pool in the GCP console to allow node creation in the A and C zones of europe-west2, which were not exhausted

   Action items

   • Either restrict the node pool back to the B zone after the Black Friday event, or move the cluster and the NFS Filestore to the A zone (infrastructure issue #1944)
   • Investigate an enterprise plan that allows a regional NFS, at higher cost (infrastructure issue #1945)
   • Make sure PagerDuty notifications always get through to engineers (team-compass issue #574)

   Lessons

   A node pool pinned to one zone for storage performance cannot scale when the cloud provider runs out of capacity in that zone. The autoscaler's "backoff after failed scale-up" message usually points to quotas, and raising quotas does nothing here; the ZONE_RESOURCE_POOL_EXHAUSTED error in the logs is what identifies a zone without capacity. The review found that one engineer wearing many hats (incident commander, comms and debugging), with pages not reaching the other responders, made the response harder; a team response would have given more support.

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5. IL-0205 The GKE cluster autoscaler could not add nodes to GitLab.com's production cluster because Google Cloud ran out of SSD persistent disks in us-east1

   GitLab2021

   Symptoms

   At 08:05 UTC on 2021-06-21 the API service's Apdex (the share of requests that finish within a target time) and Rails request queuing began trending the wrong way. At 09:01 the team found the API deployment unhealthy: it needed more pods for the day's growing traffic, but no new nodes could be added to give it pod capacity. Users saw slow responses; the review found no sign that error rates rose.

   Scale-up failureHigh latencyPods PendingDeploys failing

   Trigger

   Google Cloud ran out of SSD persistent disk stock in us-east1, the region GitLab.com runs in, while the API service needed more nodes for rising user traffic.

   Root cause

   Cloud provider outage

   Component

   Cluster autoscalerNode pool

   Contributing factor

   Monitoring blind spotLimited debug accessOverlapping failures

   Detection

   There was no alert on resource availability. Apdex and Rails queuing began trending the wrong way at 08:05 UTC and an engineer declared the incident in Slack at 08:43; finding the cause took a lot of log digging: cluster autoscaler errors about node pools in backoff, then ZONE_RESOURCE_POOL_EXHAUSTED at the instance-group level, and finally Google Cloud support naming SSD as the exhausted resource.

   Remediation

   • added a node pool that uses pd-standard disks
   • scaled the new node pool by hand across all three zones to relieve the API
   • brought more pd-standard node pools online for the other blocked workloads
   • stopped production deployments and opened a Google Cloud support case about the cluster autoscaler not working with the new node pools
   • changed node pools and nodeSelectors so the remaining workloads ran on pd-standard nodes
   • removed the SSD-backed node pools after a day without problems

   Action items

   • Move Kubernetes services to pd-standard disks (delivery#1837)
   • Capture resource exhaustion errors as a metric

   Lessons

   A cloud region can run out of one specific resource, here SSD persistent disks, and the only error may be a generic ZONE_RESOURCE_POOL_EXHAUSTED that does not name it. GitLab could neither monitor nor reserve resource availability, so detection took 22 hours and the cause came from Google Cloud support. The workloads ran fine on pd-standard disks. A second problem found on the way, a cluster autoscaler that does not fall back to another matching node pool when one cannot scale, slowed the move to the new node pools.

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