Cases2 cases

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1. IL-0237 A Kubernetes patch upgrade evicted every Ray Serve head and worker pod at once, and Lucidworks hosted LLMs went down

   Lucidworks2026sev1

   Symptoms

   From 16:18 UTC on 28 May 2026 the Lucidworks AI hosted LLMs in the us-southcarolina region were unavailable: queries to llama-3-8b-instruct, llama-3v2-3b-instruct and phi-4-multimodal-instruct returned 500 or 429 errors. Search, embedding models, the Lucidworks Platform UI and passthrough LLM calls kept working.

   Service outagePod evictionImage pull failures

   Trigger

   A routine Kubernetes patch upgrade on a cluster in us-southcarolina cycled its node pools one after another in quick succession.

   Root cause

   Faulty deployment

   Component

   Node poolModel inference server

   Contributing factor

   No staged rolloutOverlapping failures

   Remediation

   • Deleted the degraded replacement head node that could not pull container images
   • Waited for the LLM image pulls to finish on the replacement nodes
   • Launched new nodes with the required LLM images and redirected Lucidworks AI traffic to them

   Action items

   • Upgrade clusters that host LLM workloads in sequence, with each node pool fully healthy before the next one is upgraded
   • Investigate image pre-loading, such as pre-baked disks or image streaming, for large ML model images
   • Open a support ticket with the cloud provider about the abnormal Docker pull times
   • Coordinate Kubernetes maintenance windows with LLM service owners so they do not clash with ongoing deployments
   • Build tooling to force a Ray cluster failover to the blue or green state instead of waiting for Ray to settle it

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2. 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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