Cases2 cases

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1. IL-0124 Capacity changes queued while a circuit breaker was engaged ran all at once when it was released, taking multiple OpenAI inference engines out of service and raising API error rates

   OpenAI2026

   Symptoms

   Between about 4:26 PM and 4:56 PM PST on 4 March 2026, some OpenAI API requests to several models failed or took longer than usual, and some Codex traffic was affected too. The overall API success rate dropped temporarily.

   High error rateHigh latency

   Trigger

   A protective circuit breaker that had paused some scheduling operations was released, and the infrastructure actions queued during the pause were executed at the same time.

   Root cause

   Software defect

   Component

   Automated capacity manager

   Contributing factor

   Automation amplificationChange with a delayed effectNo safeguard on destructive changeMonitoring blind spot

   Remediation

   • manually reassigned infrastructure resources to the affected models
   • brought the removed inference engines back online and reassigned them until error rates returned to normal

   Action items

   • Improve safeguards in the automated capacity management systems so they detect and respond to state drift
   • Improve observability of circuit breakers and scheduling systems so their status and effects are easier to identify
   • Add safeguards so queued actions cannot run in large batches after long pauses
   • Improve monitoring and alerting around capacity reconciliation problems

   Lessons

   Pausing one automated system does not stop the systems that feed it. Changes queued during the pause reflect the state at the time they were queued, and releasing the queue all at once applies those stale decisions together. The fixes OpenAI names are detecting state drift, making circuit breaker status visible, and preventing large batches from executing after a long pause.

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2. IL-0012 CPU limits and an unpatched kernel CFS quota bug throttled multi-threaded services into stalls, failed health checks and timeouts

   Omio2020

   Symptoms

   For about six months, applications on Omio's Google Kubernetes Engine clusters stalled at random: they got stuck, stopped answering health check requests or lost network connections, and nothing the team looked at explained why.

   CPU throttlingProbe failuresHigh error rateHigh latencyIntermittent timeoutsIntermittent connection failures

   Trigger

   Multi-threaded workloads running with CPU limits on nodes whose kernel did not yet have the fix for a known CFS quota bug that causes unnecessary throttling.

   Root cause

   Software defect

   Component

   Container CPU limitLinux kernelkubelet

   Contributing factor

   Unknown limitMonitoring blind spotUnpatched known bug

   Detection

   Months of random stalls and failed health checks led the team to read the cgroup cpu.stat throttling counters inside the pods, which showed far more throttling than the limits should allow.

   Remediation

   • removed CPU limits until the kernel with the CFS bug fix was deployed across all clusters

   Action items

   • Deploy the kernel with the CFS quota bug fix across all clusters
   • Monitor utilisation, saturation and error metrics, API latency and error rates to confirm the change behaves as expected

   Lessons

   A CPU limit is a time quota per 100 ms period, not a share of cores, so a multi-threaded container can be stopped for most of each period long before it uses the cores it was given, and a kernel bug can make that worse. Throttling is invisible in standard Kubernetes metrics and has to be read from the cgroup counters. Removing limits trades away protection against a container that takes a whole node, which is only acceptable when runtimes are tuned.

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