Cases2 cases

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1. IL-0248 Azure OpenAI inference requests slowed and failed in several regions as a retry storm overloaded the routing layer

   Microsoft Azure2026

   Symptoms

   From 09:39 to 17:05 UTC on 29 May 2026, customers sending inference requests to Azure OpenAI Service saw higher latency, intermittent failures, timeouts and HTTP 5XX errors. Automated monitoring detected falling request success rates at 09:39 UTC, and regions in Europe and Australia East, which processed the most affected traffic, were hit hardest.

   High latencyHigh error rateIntermittent timeoutsOOMKilled

   Trigger

   A change in an upstream API layer used by Microsoft 365 made some capacity-related failures surface as transient server failures that callers retry. It rolled out first in Australia East and later in Sweden Central.

   Root cause

   Faulty deployment

   Component

   Model request routing

   Contributing factor

   Retry stormExcessive blast radiusUnverified assumption

   Detection

   Automated monitoring detected a drop in request success rates at 09:39 UTC and started the incident investigation.

   Remediation

   • Isolated the internal workload that sent the amplified retries onto dedicated infrastructure
   • Rolled back the triggering change in the upstream service and reduced retry-driven traffic
   • Cleared the backlogged requests

   Action items

   • Move large first-party generative AI workloads onto dedicated routing infrastructure instead of the shared inference load balancing stamp
   • Improve diagnostic guidance, runbooks and alerting to tell secondary crash symptoms from the real source of overload
   • Work with upstream teams on retry policy design, dependency contracts and telemetry to catch retry amplification early
   • Add stronger overload prevention and throttling so no single internal workload can overwhelm shared components
   • Find and remove single points of failure in the inference routing layer

   Lessons

   Internal workloads were not protected the way external traffic was: their traffic skipped some rate limiting and overload controls, so the amplified retries reached shared infrastructure. The apparent recovery after disabling the suspected feature hid the real cause until the problem came back at larger scale in Sweden Central.

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2. IL-0530 Virtual router controllers in us-central1 ran out of memory in a loop, cutting networking for new and migrated VMs

   Google Cloud2023

   Symptoms

   On 5 October 2023 in us-central1, newly created VMs waited a long time before their networking worked, and VMs that had been live migrated lost connectivity for a long time after the migration. Networking telemetry showed cross-region packet loss to and from us-central1 from 03:08 US/Pacific. Health check state of VMs reached load balancers late.

   Packet lossNetwork unreachableOOMKilledStale endpoint routingCluster or node provisioning stall

   Trigger

   A management plane behaviour change, meant to decouple the processing of API updates to Instance Groups and Network Endpoint Groups used as load balancer backends, was deployed in us-central1 after rolling out to several other regions without incident.

   Root cause

   Faulty deployment

   Component

   Network control plane (SDN)Cloud load balancer

   Contributing factor

   Excessive blast radiusNo alert on limit

   Detection

   Engineers were alerted to slowness in the virtual network control plane in us-central1 at 21:45 US/Pacific on 4 October, before the packet loss started, and to controller failures from memory exhaustion at 02:11 on 5 October. Networking telemetry showed cross-region packet loss from 03:08.

   Remediation

   • Increased the memory allocated to the virtual network router controllers
   • Rolled back the management plane change in us-central1
   • Restarted by hand the routers that were still not accepting updates

   Action items

   • Alert proactively on memory risks and unexpected memory increases
   • Refactor the deployment configuration so engineers can reallocate memory much faster
   • Re-evaluate existing practices and add safety mechanisms for API reconciliation
   • Make management plane changes more visible across teams so the changes can be correlated faster
   • Adjust the deployment footprint so a regional API change is less likely to exhaust memory across the region at once
   • Optimise memory use in the traffic routers and their controllers

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