Cases14 cases

4 to 8 of 14

1. IL-0160 A repaired legacy GPU monitoring script sent a firmware query that triggers a race condition in the GPU System Processor of NVIDIA RTX 4000 Ada GPUs, locking up GPU Linodes and LKE worker nodes in several regions

   Linode2026

   Symptoms

   From about 21:00 UTC on March 4, 2026, customers using Linodes backed by NVIDIA RTX 4000 Ada GPUs saw the GPUs lock up. At first the problem seemed limited to worker nodes of the Linode Kubernetes Engine (LKE); later Linode confirmed that every Linode with this hardware was affected, while Standard Compute and other GPU instances were not.

   GPU unavailableControl plane unresponsive

   Trigger

   Deployment of a repaired legacy GPU monitoring script, fixed during a routine improvement effort to restore a missing metric on internal observability dashboards.

   Root cause

   Faulty deployment

   Component

   Telemetry agentGPU driver

   Contributing factor

   Missed in reviewUnverified assumption

   Detection

   Customers saw the lockups from about 21:00 UTC; the first status post went up at 02:07 UTC on March 5, about 5 hours after the lockups began.

   Remediation

   • Disabled the monitoring script across the GPU fleet
   • Rebooted the affected nodes

   Action items

   • Take improvement and prevention measures to stop a recurrence; the postmortem does not list them

   Lessons

   A monitoring script can take down the hardware it watches. A fix meant only to restore a missing metric brought back a firmware query written for an earlier GPU generation, and on the RTX 4000 Ada architecture that query against an active GPU triggers a lockup. The script ran across the GPU fleet, so every Linode with this GPU was affected in several regions, and the investigation first suspected a regression in the host hypervisor or the GPU firmware.

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2. IL-0188 Rolling updates on EKS returned 502 errors for about 30 seconds because Kubernetes deleted old pods as soon as new pods passed readiness, before the ALB had marked the new pod targets healthy

   Yonghyun Kim2025

   Symptoms

   Every rolling update of the team's microservices on Amazon EKS, behind an Application Load Balancer, was followed by intermittent 502 and 503 errors for users. A Slack message at 3 a.m. read "배포는 성공했는데, 서비스는 죽었다" (the deployment succeeded, but the service died). Every pod was Ready and Datadog was green while users saw errors.

   High error rateService outage

   Trigger

   A rolling update of a Deployment served through an ALB that registers pod IPs as targets, with no pod readiness gates.

   Root cause

   Faulty deployment

   Component

   Cloud load balancerIngress controllerPod termination

   Contributing factor

   Unverified assumptionUnreviewed defaultsMonitoring blind spot

   Detection

   A Slack message at 3 a.m. reported that the deployment had succeeded but the service was down; Datadog dashboards were green at the time.

   Remediation

   • labelled the namespace with elbv2.k8s.aws/pod-readiness-gate-inject=enabled so the AWS Load Balancer Controller injects pod readiness gates and Kubernetes waits for the ALB target to be healthy before deleting old pods

   Lessons

   A pod being Ready in Kubernetes does not mean the load balancer will send it traffic. Without a readiness gate, a rolling update can delete the old pods while the ALB still considers the new targets unhealthy, and dashboards that track pod status stay green during the errors. Making pods wait longer on shutdown reduced the errors but did not fix the start-up gap and made every deployment much slower; the readiness gate ties the pod's Ready state to the ALB's view of the target.

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3. IL-0121 A faulty code change to a control plane cluster meant for low-priority workloads degraded the production inference workloads it also managed

   OpenAI2025

   Symptoms

   Between 2:10 PM and 3:05 PM PDT on July 9, 2025, success rates of the gpt-4.1-mini model in the API dropped for about 20 minutes, and ChatGPT search returned many more errors than usual for about 35 minutes.

   High error rate

   Trigger

   A faulty code change was deployed to a control plane cluster whose main job is managing low-priority workloads.

   Root cause

   Faulty deployment

   Component

   –

   Contributing factor

   Excessive blast radiusNo safeguard on destructive change

   Remediation

   • rolled back the faulty code change

   Action items

   • Add safeguards that prevent production inference workloads from being managed by the low-priority control plane cluster
   • Strengthen testing for deploying high-impact control plane changes
   • Move all existing production inference workloads under the production control plane cluster
   • Build a circuit breaker that stops large-scale unintended changes

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4. IL-0020 During an EKS 1.30 upgrade the descheduler tried to reschedule every pod on the cordoned nodes at once and production workloads went down

   UK Ministry of Justice2025

   Symptoms

   At 09:36 on 18 March 2025, during the upgrade of the live cluster to EKS 1.30, users reported that their pods were being terminated and were not restarting automatically, in both production and non-production environments. Automated infrastructure alerts fired as well, and production services had outages.

   Pod evictionPods PendingService outageUpgrade stall

   Trigger

   Upgrading the live EKS cluster from 1.29 to 1.30, during which the descheduler attempted to reschedule all pods on the cordoned nodes simultaneously.

   Root cause

   Faulty deployment

   Component

   DeschedulerNode poolCluster autoscaler

   Contributing factor

   No headroomNo alert on limitUnreviewed defaults

   Detection

   Users reported at 09:36 that pods were being terminated and not restarting, and automated infrastructure alerts fired.

   Remediation

   • Removed the cordon annotation so pods could be rescheduled
   • Deleted stuck 1.30 nodes to force their recreation
   • Added a new node group of 20 nodes running 1.30 by hand
   • Cordoned and drained all old 1.29 nodes onto the new 1.30 nodes

   Action items

   • Raise an AWS Production System Down support case (Case ID: 174229643100496)
   • Get guidance from AWS support on querying control-plane logs to find the root cause of similar issues
   • Document a runbook for emergency scaling of a new node group in case of unexpected capacity shortages
   • Alert on an imbalance between the autoscaling group and the Kubernetes nodes
   • Review the descheduler configuration to understand why it throws pods off cordoned nodes, and fix it where possible
   • Alert on spikes in the pending pod count

   Lessons

   The write-up lists review actions rather than lessons. They show the team had no documented way to add an emergency node group, no alert on a mismatch between the autoscaling group and the nodes Kubernetes sees, no alert on a spike in pending pods, and no clear understanding of how the descheduler treats cordoned nodes.

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5. IL-0142 GKE clusters on 1.24 and later wrote unexpected fluentbit error entries such as "Failed to get record: decoder: failed to decode payload: EOF" that used up the Logging API write quota for some customers

   Google Cloud2023

   Symptoms

   From 07:13 US/Pacific on 6 July 2023, GKE customers saw unexpected additional error and warning entries in their cluster logs, written to the Cloud Logging project that holds the clusters. Standard and Autopilot clusters at GKE 1.24.x and later were affected.

   Log flood

   Trigger

   Google rolled out a new version of the GKE component that processes logs (preliminary analysis).

   Root cause

   Faulty deployment

   Component

   Telemetry agent

   Contributing factor

   –

   Remediation

   • gave customers a workaround: request a Logging API quota increase, or apply a log exclusion for the fluentbit-gke messages
   • rolled out a fix gradually to the affected clusters between 10 and 18 July 2023

   Lessons

   Log messages written by a managed logging agent count against the customer's own Logging API write quota and logging bill, so a faulty agent release can drop a customer's other logs. A log exclusion on the agent's container name and the message patterns keeps the noise out of storage until a fix arrives.

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