Cases14 cases

1 to 5 of 14

1. IL-0136 A deployment that cleared an internal service's caches at peak traffic sent 20 times the normal load to the Azure OpenAI metadata backend in Sweden Central, and health checks that depended on its database restarted instances in a loop for almost six hours

   Microsoft Azure2026

   Symptoms

   From 10:03 UTC on 29 September 2026, requests to Azure OpenAI Service, Foundry Agent Service, Foundry Models and Azure AI services in Sweden Central failed intermittently, took longer, or returned HTTP 500 and 503 errors. At 10:17 UTC service monitoring raised an alert on elevated failure rates for Azure OpenAI Service requests in the region.

   High error rateHigh latencyIntermittent connection failuresIntermittent timeoutsProbe failuresOutbound connection exhaustion

   Trigger

   At about 09:38 UTC a planned deployment of an internal service in Sweden Central cleared that service's caches during the region's peak traffic hours.

   Root cause

   Faulty deployment

   Component

   In-memory cacheApplication database

   Contributing factor

   Change during peak hoursRetry stormAutomation amplificationMonitoring blind spotNo runbookHealth check tied to a dependency

   Detection

   Service monitoring detected elevated failure rates for Azure OpenAI Service requests in the region at 10:17 UTC and alerted the engineering teams; engineers were engaged at 10:28 UTC.

   Remediation

   • reconfigured the backend service's automated health check so it no longer depended on remote connectivity, which stopped the restart loop

   Action items

   • Reconfigure the backend service's automated health check so it no longer depends on connectivity to its database layer
   • Add monitoring and alerting on the load and latency of the caching layer used by the backend service
   • Update troubleshooting and procedural guidance for connection exhaustion scenarios for on-call engineers
   • Add throttling and rate limiting to protect the backend service from sudden traffic increases (estimated October 2026)
   • Increase the resource capacity of the caching layer used by the backend service across regions (estimated October 2026)
   • Improve diagnostic metrics and automated detection to identify such issues more quickly (estimated October 2026)
   • Partition the caching layer used by the backend service to increase its throughput (estimated December 2026)

   Lessons

   A health check that depends on a remote dependency turns a dependency problem into a restart loop: instances that cannot reach the database are killed, the remaining ones take more load and retries, and adding instances or resources does not help until the check itself is changed. Clearing a cache during peak traffic can multiply load on the backend behind it, especially when cache-read defects let lookups bypass the cache. Connection exhaustion was hard to see because the existing network and diagnostic metrics did not show it.

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2. 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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3. IL-0167 A phased rollout of an LKE software release in Osaka stalled LKE-E cluster creation, version upgrades and control plane ACL changes

   Linode2026

   Symptoms

   From about 19:15 UTC on 12 March 2026, customers in the Osaka data center (JP-OSA) could not create Linode Kubernetes Engine for Enterprise (LKE-E) clusters. Cluster creation stalled indefinitely.

   Cluster or node provisioning stallUpgrade stall

   Trigger

   A phased rollout of the LKE software release reached the Osaka data center.

   Root cause

   Faulty deployment

   Component

   Managed Kubernetes control plane and provider API

   Contributing factor

   –

   Detection

   The problem began at about 19:15 UTC, and the first status post, about five and a half hours later at 00:45 UTC on 13 March, said the team was investigating an emerging issue with deploying new LKE Enterprise clusters in Osaka.

   Remediation

   • deployed a software fix to mitigate the impact

   Action items

   • Investigate the root cause further and take preventive actions

   Lessons

   The problem followed a phased rollout of an LKE software release in the Osaka data center. There, cluster creation and administrative tasks stalled for more than six hours, and the first status post came about five and a half hours after the start.

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4. 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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5. 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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