Cases45 cases

11 to 15 of 45

1. 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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2. IL-0138 A failed Nvidia driver build left broken kernel packages on the only Kubernetes master node, which hit a kernel panic on reboot after a power outage and took the homelab cluster down for six days

   Barush Mendez2025

   Symptoms

   Every service in the homelab Kubernetes cluster stopped answering: the V-Rising server, Grafana, Jellyfin and Linkding were all unreachable, and the Kubernetes API was down, so the cluster could not be managed at all. The outage was found by hand, about 3 hours after it began, when someone tried to use the services.

   Control plane unresponsiveService outageMonitoring outage

   Trigger

   A power outage forced the Kubernetes master node to reboot.

   Root cause

   Software defect

   Component

   GPU driverLinux kernelKubernetes API server

   Contributing factor

   Ignored early signalChange with a delayed effectSingle point of failureMonitoring blind spot

   Detection

   Manually, when someone tried to access the services; there were no automated alerts.

   Remediation

   • Connected a monitor to the master node and read the boot logs, which showed the kernel panic
   • Booted the previous kernel version, which brought the system back
   • Removed the broken Nvidia packages with sudo apt-get remove --purge '^nvidia-.*' and ran sudo dpkg --configure -a
   • Found remaining broken packages with dpkg -l | grep -v ^ii and removed them by hand
   • Ran sudo apt-get install -f and reinstalled linux-headers-$(uname -r)
   • Rebooted and confirmed the kernel panic was gone

   Action items

   • Make sure GPU driver installations fully succeed, or revert immediately when they fail
   • Move the cluster to a high-availability control plane
   • Expose the Kubernetes API through a load balancer in the HA setup
   • Set up a ticketing system to record incidents and their resolution
   • Write predefined troubleshooting workflows to shorten response time
   • Add infrastructure monitoring such as Prometheus and Grafana
   • Add blackbox HTTP and TCP checks from outside the cluster for critical services

   Lessons

   A driver installation that fails half-way can leave kernel packages broken without any visible effect until the next reboot, which may come weeks later and at the worst time. A failed DKMS build should be fixed or reverted at once. With a single master node and no monitoring from outside the cluster, one unbootable machine meant a total outage that nobody was told about; the fix itself was simple once the boot logs were seen.

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3. IL-0148 A kube-scheduler regression in Kubernetes v1.27.14, v1.28.10 and v1.29.5 made the scheduler panic on a pod whose node affinity named a nonexistent node, so no pods were scheduled on affected AKS clusters

   Microsoft2024

   Symptoms

   On AKS clusters running Kubernetes v1.27.14, v1.28.10 or v1.29.5, kube-scheduler panicked whenever the cluster contained a pod with a bad node affinity, and from then on no pod in the cluster was scheduled.

   Pods Pending

   Trigger

   A pod in the cluster with a required node affinity whose matchFields term names a node that does not exist.

   Root cause

   Software defect

   Component

   Pod scheduling and placement

   Contributing factor

   –

   Detection

   The AKS team found the bug; the issue does not say how.

   Remediation

   • AKS engineers fixed the impacted clusters
   • Rolled out a fixed kube-scheduler to all regions, applied to clusters automatically

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4. IL-0131 A new kind of GenerateContent request made Vertex AI prediction API servers segfault, and once enough of them restarted at the same time the serving fleet could not carry the load

   Google Cloud2024

   Symptoms

   From 12:06 US/Pacific on 12 June 2024, services built on Vertex AI Prediction began reporting failed user requests in eight regions: us-central1, asia-southeast1, europe-west3, europe-west4, us-east1, us-west1, northamerica-northeast1 and us-east4.

   High error rateHigh latencyIntermittent timeouts

   Trigger

   Starting on 8 June 2024, a new form of user request to the GenerateContent API caused intermittent segmentation faults in the Vertex Serving Prediction API servers; on 12 June the rate of such requests increased.

   Root cause

   Software defect

   Component

   Model inference serverCloud load balancer

   Contributing factor

   Partial rolloutNo headroom

   Detection

   Internal production monitoring alerted engineers to the segfaults on 10 June 2024 at 07:35 US/Pacific, before any visible customer impact; on 12 June services built on Vertex Prediction reported failing user requests.

   Remediation

   • confirmed the new failures came from the same segfault found on 10 June
   • accelerated the rollout of the fix that was already in progress, completed at 15:59 US/Pacific

   Action items

   • Ensure early signals of server binary issues are captured in production health analysis and investigated in time
   • Ensure release validation of feature changes and updates in production

   Lessons

   A crash that only some requests trigger can stay invisible while load balancers route around the restarting servers. It becomes an outage when the share of such requests grows until too many servers are restarting at once. Here the cause was known and a fix was rolling out two days before customers were affected, but the progressive rollout had not finished when the triggering traffic increased; Google's follow-up is to investigate early signals of server binary issues in time.

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5. IL-0077 EKS cluster went read-only for over seven hours after Kyverno ephemeralreports filled the etcd database past its size limit

   Marcin Cuber2024sev1

   Symptoms

   A production EKS cluster on Kubernetes 1.30 went into read-only mode: the API server rejected every request that would change an object, such as creating pods or scaling deployments, and deleting objects to free space did not work either.

   Control plane unresponsiveControl plane lag

   Trigger

   Kyverno 1.12 kept creating ephemeralreports objects that piled up in etcd until the database passed EKS's size limit.

   Root cause

   Software defect

   Component

   etcdKubernetes API serverPolicy engine

   Contributing factor

   No alert on limitLimited debug access

   Detection

   The cluster rejected writes; the audit logs carried the "etcdserver: mvcc: database space exceeded" error.

   Remediation

   • AWS raised the etcd storage limit from 8/10GB to 12GB to unlock writes
   • an AWS engineer deleted the leaked Kyverno ephemeralreports objects directly from etcd

   Action items

   • Monitor the etcd database size with apiserver_storage_size_bytes and never let it reach 8GB
   • Upgrade Kyverno 1.12 to 1.12.4, which fixes the ephemeralreports pile-up, or disable reporting for admission events and tune --aggregationWorkers

   Lessons

   On EKS, once etcd hits its size limit the cluster becomes read-only and only AWS can clean it up, so cleanup scripts must run before the limit is reached. Watch the etcd database size and object counts per resource, especially for controllers that create report objects.

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