Cases2 cases

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1. IL-0136 A deployment that cleared an internal service's caches at peak traffic sent 20 times the normal load to the Azure OpenAI metadata backend in Sweden Central, and health checks that depended on its database restarted instances in a loop for almost six hours

   Microsoft Azure2026

   Symptoms

   From 10:03 UTC on 29 September 2026, requests to Azure OpenAI Service, Foundry Agent Service, Foundry Models and Azure AI services in Sweden Central failed intermittently, took longer, or returned HTTP 500 and 503 errors. At 10:17 UTC service monitoring raised an alert on elevated failure rates for Azure OpenAI Service requests in the region.

   High error rateHigh latencyIntermittent connection failuresIntermittent timeoutsProbe failuresOutbound connection exhaustion

   Trigger

   At about 09:38 UTC a planned deployment of an internal service in Sweden Central cleared that service's caches during the region's peak traffic hours.

   Root cause

   Faulty deployment

   Component

   In-memory cacheApplication database

   Contributing factor

   Change during peak hoursRetry stormAutomation amplificationMonitoring blind spotNo runbookHealth check tied to a dependency

   Detection

   Service monitoring detected elevated failure rates for Azure OpenAI Service requests in the region at 10:17 UTC and alerted the engineering teams; engineers were engaged at 10:28 UTC.

   Remediation

   • reconfigured the backend service's automated health check so it no longer depended on remote connectivity, which stopped the restart loop

   Action items

   • Reconfigure the backend service's automated health check so it no longer depends on connectivity to its database layer
   • Add monitoring and alerting on the load and latency of the caching layer used by the backend service
   • Update troubleshooting and procedural guidance for connection exhaustion scenarios for on-call engineers
   • Add throttling and rate limiting to protect the backend service from sudden traffic increases (estimated October 2026)
   • Increase the resource capacity of the caching layer used by the backend service across regions (estimated October 2026)
   • Improve diagnostic metrics and automated detection to identify such issues more quickly (estimated October 2026)
   • Partition the caching layer used by the backend service to increase its throughput (estimated December 2026)

   Lessons

   A health check that depends on a remote dependency turns a dependency problem into a restart loop: instances that cannot reach the database are killed, the remaining ones take more load and retries, and adding instances or resources does not help until the check itself is changed. Clearing a cache during peak traffic can multiply load on the backend behind it, especially when cache-read defects let lookups bypass the cache. Connection exhaustion was hard to see because the existing network and diagnostic metrics did not show it.

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2. IL-0110 A Grafana Agent pod's high memory use and crash-looping Hangfire pods drove a Kubernetes node into system OOM and NotReady, twice in one day

   DFDS2024

   Symptoms

   At 10:01 UTC on 2024-03-13 a Kubernetes node entered NotReady state, with CPU requests at 78% and memory requests at 82%. An alert fired in Slack at 10:03 and was picked up at 10:05. An attempt to SSH into the node at 10:13 failed because it was unresponsive; it started responding again at 10:19.

   Node NotReadyContainer crash loopOOMKilledMonitoring outage

   Trigger

   Pods of a dev container that used Hangfire to trigger jobs failed with OOM and crash-looped on a node where a Grafana Agent pod was already using a lot of memory.

   Root cause

   Capacity exhaustion

   Component

   Telemetry agentContainer memory limitApplication database

   Contributing factor

   Excessive blast radiusNo resource requests or limitsHarmful manual actionMonitoring blind spot

   Detection

   A Slack alert fired two minutes after the node went NotReady and was picked up two minutes later; the second node's NotReady at 15:03 also raised a Slack alert.

   Remediation

   • marked the EC2 instance unhealthy so a new node started
   • cordoned and force-drained the NotReady node and terminated its EC2 instance
   • removed the new LimitRange from the Grafana Agent namespace and restarted the Grafana Agent pod
   • scaled the dev container to 0 replicas, after which EC2 network traffic returned to normal
   • after the second NotReady, scaled the dev container to 0 replicas again and added a new node
   • emailed the team that owns the dev container, which confirmed it was working on a fix

   Action items

   • Move grafana-agent to the monitoring node group
   • Add alerts for when bandwidth across the cluster reaches a certain threshold
   • Investigate whether memory limits can be set on the grafana-agent Helm chart
   • Investigate grafana-agent memory usage
   • Ask Grafana support whether grafana-agent should use a PVC
   • Consider pushing node logs to Loki with limited retention
   • Create a wiki page with common metrics and log queries to check when investigating issues with Kubernetes nodes

   Lessons

   Dev workloads mixed with production let one crash-looping dev container take down nodes that production also used, and a monitoring agent outside its dedicated node group added to the memory pressure. Scaling the suspect container back up before its owners had fixed it brought the failure back on the replacement node. The team also noted that action items from earlier post-mortems need follow-up, and that knowledge of useful CloudWatch metrics had not been shared, which made the diagnosis harder.

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