Cases9 cases

6 to 9 of 9

1. IL-0139 Pods on GKE clusters with Calico network policy got stuck Terminating or Pending after upgrading to 1.22 or later because of a race condition in the Calico component

   Google Cloud2022

   Symptoms

   After upgrading to GKE 1.22 or later, clusters that use Calico network policy could have pods stuck in Terminating or Pending. Google Cloud posted the incident at 16:01 US/Pacific on 2022-09-15 as pods stuck terminating; from the 2022-09-21 update the description also named pods stuck Pending.

   Pods stuck in TerminatingPods Pending

   Trigger

   Upgrading a GKE cluster that uses Calico network policy to version 1.22 or later.

   Root cause

   Software defect

   Component

   CalicokubeletPod termination

   Contributing factor

   –

   Remediation

   • released a fix in GKE 1.24.4-gke.800 and made it available in 1.23 and 1.22
   • offered an internal patch through Google Cloud Support for affected customers
   • advised customers to restart the kubelet and calico-node to get stuck pods unstuck
   • advised customers without a strong need for Calico to disable Calico network policy

   Action items

   • Upgrade clusters on the Rapid, Regular and Stable release channels running 1.22 or 1.23 to the fixed version automatically

   Lessons

   A race in the Calico component can leave pods stuck Terminating, with FailedKillPod and KillPodSandbox errors that point at the network plugin, not at the workload. Restarting the kubelet and calico-node frees the stuck pods but does not remove the race; the fixed GKE version or Google's internal patch does. Clusters that use the cluster autoscaler are more likely to hit it.

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2. IL-0140 The GKE API in us-central1 returned errors and high latency for 3 hours 19 minutes after an unexpected rise in backend traffic that client retries made worse

   Google Cloud2022

   Symptoms

   From 03:03 US/Pacific on 14 June 2022, calls to the GKE API in us-central1 through gcloud and the Google Cloud Console were slow and returned errors. Retrying sometimes succeeded.

   High error rateHigh latencyControl plane lag

   Trigger

   An unexpected increase in backend traffic to the GKE API in us-central1.

   Root cause

   Capacity exhaustion

   Component

   GKE control plane

   Contributing factor

   Retry storm

   Remediation

   • scaled up the GKE control plane to handle the increased traffic

   Lessons

   Client retries against an overloaded management API add to the overload. When the cloud provider's control plane is degraded, running workloads may be fine while cluster operations fail, and the status page offered no workaround beyond advising customers to use another region if they could.

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3. IL-0132 A faulty resource cleanup process marked Vertex AI prediction endpoints as deleted worldwide, undeploying customers' models and failing predictions for 4 hours 20 minutes

   Google Cloud2022

   Symptoms

   From 10:10 US/Pacific on 2 June 2022, Vertex AI Online Prediction returned errors for 30% up to 100% of requests per region, depending on how each customer used the service.

   High error rateResources deletedService outage

   Trigger

   A faulty resource cleanup process marked Vertex Prediction Endpoints as deleted, globally.

   Root cause

   Software defect

   Component

   Model request routing

   Contributing factor

   Automation amplificationExcessive blast radius

   Detection

   The first public status update was posted at 13:58 US/Pacific and gave the start as 10:20 US/Pacific; the later mini incident report gives 10:10.

   Remediation

   • restored the Vertex Prediction Endpoints that had been marked as deleted

   View case
4. IL-0143 GKE nodes upgraded to certain 1.13 and 1.14 versions had an elevated number of kernel panics, fixed by releases that downgraded the Container-Optimized OS image

   Google Cloud2019

   Symptoms

   Some nodes in Google Kubernetes Engine clusters that had recently been upgraded showed an elevated number of kernel panics. The status page described it as kernel panics following an upgrade in GKE version.

   Node kernel panic

   Trigger

   Upgrading GKE node pools to one of the affected 1.13 or 1.14 versions.

   Root cause

   Software defect

   Component

   Node operating system imageLinux kernel

   Contributing factor

   –

   Remediation

   • advised affected users to downgrade to a patch release not on the affected list, with support helping Release Channel users
   • released GKE 1.13.11-gke.14, 1.13.12-gke.8, 1.14.7-gke.23 and 1.14.8-gke.12, which downgraded Container-Optimized OS to cos-73-11647-293-0
   • rolled the fixed versions out to node pools with auto-upgrade enabled; clusters on manual upgrades had to upgrade their nodes themselves

   Action items

   • Ship a GKE release with an upgraded kernel that fixes the panics

   Lessons

   Downgrading the node operating system image was used as a temporary mitigation until a kernel fix was available.

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