Cases4 cases

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1. IL-0101 After Google forced a GKE control plane upgrade to 1.33.2 at a deprecation deadline, no new node could initialise, so autoscaling and spot node replacement stopped for 15 hours

   Wealthy2025sev1

   Symptoms

   Nodes that already existed kept running, but every new node, spot or standard, on either v1.32 or v1.33, failed to initialise and could not join the cluster. Spot nodes that were preempted and recreated never came back, so services degraded progressively: background workers on spot instances, reporting services and order allocation stopped completely, and the APIs on existing nodes kept answering but could not scale for load spikes.

   Scale-up failureContainer crash loopService outageDNS resolution failures

   Trigger

   Google upgraded the GKE control plane from v1.32.x to v1.33.2 at the version deprecation deadline, although auto-upgrade was disabled on the node pools.

   Root cause

   Breaking change in an upgrade

   Component

   GKE control planeNode poolClient-side DNS resolver and cachePersistent volumes and stateful setsConfigMaps and Secrets

   Contributing factor

   Deferred upgradesSingle point of failureIneffective rollbackNo headroom

   Remediation

   • Created a new GKE cluster at v1.33, the version of the forced upgrade
   • Upgraded Istio to 1.27.1 for Kubernetes 1.33 compatibility
   • Updated the cert-manager configuration for Kubernetes 1.33
   • Migrated all production workloads to the new cluster and verified pod scheduling, DNS resolution, PVC mounting and external integrations
   • Decommissioned the broken cluster

   Action items

   • Configure all clusters to use the GKE stable release channel
   • Implement a separate management cluster for Argo CD, monitoring and other control components (Q4 2025)
   • Review spot instance usage for critical workloads and add on-demand fallback pools
   • Document the SRE upgrade procedure: management cluster first, production after a monitoring period of several days
   • Document a Kubernetes, Istio and cert-manager compatibility matrix
   • Create an automated upgrade testing pipeline
   • Reserve static IPs for quick migrations
   • Pin versions of Istio, cert-manager, CSI and DNS addons
   • Stay current with stable releases and regularly check GKE alerts and announcements to avoid deprecation deadlines

   Lessons

   GKE upgrades the control plane at a version's deprecation deadline even when auto-upgrade is disabled, so staying inside the supported version window is how to avoid a forced upgrade. A control plane upgrade can leave existing nodes healthy while every new node fails, and spot nodes that cannot rejoin after preemption turn this into cascading failures. Relying heavily on spot instances for critical workers turned a node join failure into a progressive outage. Kubernetes upgrades have to be planned together with dependent components such as Istio and cert-manager, and without a standby cluster on the same provider there was no immediate failover; the only recovery left was building a new cluster.

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2. IL-0078 Enabling EKS secrets encryption with KMS through Terraform removed worker node IAM permissions and took down all ZenML Pro workspaces

   ZenML2025

   Symptoms

   Between 12 AM and 8 AM CEST on 11 May 2025, every ZenML Pro workspace server was unreachable, so customers could not use workspace UIs or pipelines. The ZenML Pro dashboard was not affected.

   Service outage

   Trigger

   At 23:38 on 10 May, secrets encryption with AWS KMS was enabled through Terraform as part of the EKS module upgrade.

   Root cause

   Software defect

   Component

   TerraformNode pool

   Contributing factor

   Unrepresentative stagingDeferred upgrades

   Detection

   Engineers found the cluster degraded when they resumed the upgrade at 06:00.

   Remediation

   • deleted and recreated the EKS node group with the correct IAM permissions and configuration
   • restored service access across all affected ZenML Pro environments

   Action items

   • Upgrade all Terraform modules (EKS, VPC) to current supported versions
   • Complete the EKS cluster upgrades as planned
   • Review the strategy for periodic Kubernetes and infrastructure upgrades to prevent a recurrence

   Lessons

   A successful upgrade on a much smaller staging cluster understated the risk of the same change in production. Skipping Kubernetes and module versions let upgrade complexity build up until one upgrade carried many changes at once.

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3. IL-0180 A GKE control plane auto-upgrade from 1.22 to 1.23 removed the v1beta1 Ingress and ValidatingWebhookConfiguration APIs that Istio 1.7.4 still used, and Istio stopped working in production

   Sunwoo Park2023

   Symptoms

   On 2023-05-18 a Slack alert arrived at 20:01 and Istio stopped working properly in the production GKE cluster. The istiod error logs said that the v1beta1.Ingress and v1beta1.ValidatingWebhookConfiguration resources could not be found.

   Service outage

   Trigger

   GKE automatically upgraded the production cluster's control plane from 1.22.17-gke.7500 to 1.23.17-gke.1700 at 19:52 on 2023-05-18.

   Root cause

   Breaking change in an upgrade

   Component

   IstioGKE control planeAdmission webhook

   Contributing factor

   Deferred upgrades

   Detection

   A Slack alert at 20:01 on 2023-05-18, nine minutes after the control plane upgrade. The istiod error logs showed that the v1beta1 Ingress and ValidatingWebhookConfiguration resources could not be found.

   Remediation

   • upgraded Istio from 1.7.4 to 1.13.3

   Lessons

   The first diagnosis, end of support for Istio, was wrong; the real cause was found by matching the alert time against the cluster's UPGRADE_MASTER operations in Logs Explorer. The author recommends checking the version compatibility matrices of Kubernetes, Istio and other packages, keeping a production version for at least six months and upgrading three months before end of support, keeping the test environment on the latest version, backing up before upgrades, managing deployments and versions with a tool such as Argo CD, and testing upgrades in a test environment before production.

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4. IL-0115 One of OpenAI's three production AKS clusters went offline through a human error, and rebuilding it took 10½ hours because an AKS Kubernetes upgrade broke the ingress and AKS cannot be downgraded

   OpenAI2022

   Symptoms

   At 18:56 PDT on 23 June 2022 (01:56 UTC on 24 June) several models went offline at once: some fine-tuned curie and davinci models, the Codex beta models code-davinci-001 and code-cushman-001, the legacy GPT models curie, babbage and ada, and all Embeddings models.

   Service outageHigh error rateIntermittent timeouts

   Trigger

   A destructive operation on one of the three production clusters, caused by human error that the existing safeguards did not catch, took the whole cluster offline.

   Root cause

   Operator error

   Component

   Ingress controllerModel hub

   Contributing factor

   No safeguard on destructive changeNo headroomDeferred upgradesOverlapping failures

   Remediation

   • re-allocated the models from the lost cluster to the remaining two clusters
   • rebuilt the VPN link with Azure support after losing the connection to AKS during cluster setup
   • built workarounds with Azure support after losing the connection to Azure Private Link
   • tore down the cluster and rebuilt it from scratch after the upgraded Kubernetes version broke the ingress
   • moved traffic back into the rebuilt cluster between 05:30 and 06:30 PDT

   Action items

   • Add checks and locks so that this and similar production-critical infrastructure cannot be modified by accident
   • Add better visibility into the operations being performed and make parts of the system more lexicographically distinct
   • Keep working with Azure on the AKS network connectivity issues so they do not disrupt new cluster operations
   • Update playbooks with the new cluster failure modes and change the setup steps for new clusters and infrastructure
   • Upgrade Kubernetes systematically and set aside time so the clusters do not fall behind again
   • Shard fine-tuned models across multiple clusters and work towards more automatic cross-cluster replication
   • Distribute and replicate Azure Blob Storage so fine-tuned models load faster and more reliably
   • Finish rolling out node-to-node caching to relieve Azure Blob Storage bottlenecks
   • Change how model weights are stored so they are more efficient and faster to load

   Lessons

   Losing one of three clusters exhausted the GPU headroom, and GPUs could not be added on short notice, so full recovery depended on bringing the same cluster back. Rebuilding it exposed a deferred Kubernetes upgrade: the newer version broke the ingress, and because AKS cannot downgrade there was no way back except a full rebuild. Recovering many large models at once is limited by the bandwidth of the storage they load from, and the caches that normally hide this do not help when everything moves to a different cluster.

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