Incident Library
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Cases2 cases

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  1. Symptoms

    From 08:00 US/Pacific on 12 March 2024, GKE GPU nodes running Container-Optimized OS (COS) that were newly created or recreated came up with GPU drivers that did not work, so workloads that used the GPUs could not run. GKE showed elevated errors.

    Root cause
    Component
    Contributing factor
    Detection

    The report does not say how Google detected the issue. Affected users saw "Failed to download GPU driver installer, status: 403 Forbidden" on the GPU node when installing the driver.

    Remediation
    • Halted automatic node recreations, which attempt GPU driver downloads, until the issue was mitigated
    • Worked with the driver partner to resolve access to the driver download bucket
    Lessons

    The status page report states no lessons or follow-up actions.

  2. Symptoms

    An upgrade of the network subsystem under Target's datacenter OpenStack, meant to cause only a brief disruption, ran into problems and left network connectivity disrupted for several hours. Kafka, which runs on that OpenStack, became intermittently available.

    Trigger

    A network subsystem upgrade in the datacenter OpenStack infrastructure disrupted connectivity for several hours and made Kafka intermittently available.

    Root cause
    Component
    Contributing factor
    Remediation
    • enabled gossip encryption on Consul so messages from the poisoned mesh were rejected
    • stabilized the deployment engine and redeployed the development environment with encryption, terminating instances without it
    • upgraded Consul in place to 1.2.3 and then to 1.3.0
    Action items
    • Reduce how much of an outage a single Docker daemon can cause, together with running smaller and more numerous clusters
    Lessons

    Smaller and more numerous clusters limited the damage: the smaller development clusters and production were not hit the same way as the one large cluster, and dev and prod on one cluster would have been far worse. A Docker daemon shared by many workloads is a brittle failure point. Per-workload logging and metrics sidecars were judged the right design; a shared cluster-level agent would almost certainly have made the problem worse and harder to fix.

Check your cluster for this

These 2 cases involve Container runtime and Credential broker for pods. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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