Cases7 cases

1 to 5 of 7

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-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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3. IL-0150 A latent race condition in DynamoDB's DNS automation left an empty DNS record for dynamodb.us-east-1.amazonaws.com, and the DynamoDB and EC2 launch failures that followed caused EKS, ECS and Fargate container launch failures and cluster scaling delays in us-east-1

   Amazon Web Services2025

   Symptoms

   At 11:48 PM PDT on 19 October 2025, every system that reached DynamoDB in us-east-1 through the public regional endpoint started getting DNS failures and could not connect. This included customer traffic and internal AWS services that depend on DynamoDB. DynamoDB global table replicas in other regions still answered but fell behind in replication to and from us-east-1.

   DNS resolution failuresHigh error rateScale-up failureIntermittent connection failuresHigh latency

   Trigger

   One DNS Enactor, slowed by unusually high retry delays, applied a much older DNS plan to the regional DynamoDB endpoint just as another Enactor that had applied the newest plan ran its clean-up, which deleted that older plan and with it every IP address of the endpoint.

   Root cause

   Software defect

   Component

   Authoritative DNS servers and recordsApplication databaseCloud load balancer

   Contributing factor

   Automation amplificationRetry stormNo runbook

   Detection

   DNS failures to DynamoDB began immediately at 11:48 PM PDT and the engineering teams of affected AWS services were engaged at once; by 12:38 AM they had identified DynamoDB's DNS state as the source. The later NLB health check problem was detected by monitoring at 6:52 AM.

   Remediation

   • applied temporary mitigations by 1:15 AM that let some internal services reach DynamoDB and repaired key internal tooling
   • restored all DynamoDB DNS information by 2:25 AM through manual operator intervention
   • throttled incoming work and selectively restarted DWFM hosts from 4:14 AM so droplet leases could be re-established
   • reduced the load on Network Manager to bring network configuration propagation times back to normal by 10:36 AM
   • disabled automatic health check failover for NLB at 9:36 AM, and re-enabled it at 2:09 PM after EC2 recovered
   • relaxed the EC2 request throttles from 11:23 AM until all EC2 APIs and launches were normal at 1:50 PM

   Action items

   • Disable the DynamoDB DNS Planner and DNS Enactor automation worldwide
   • Fix the race condition and add protections against applying incorrect DNS plans before re-enabling the DNS automation
   • Add a velocity control to NLB that limits how much capacity a single load balancer can remove when health check failures cause availability zone failover
   • Build an additional EC2 test suite that exercises the DWFM recovery workflow at scale
   • Improve throttling in EC2 data propagation systems so incoming work is rate limited by the size of the waiting queue

   Lessons

   A single DNS record that automation can empty is enough to take a widely used regional endpoint off the network, and every service built on it fails in turn. A check made once at the start of a long operation can be stale by the time the operation writes its result. Recovery of the dependency is not recovery of the dependents: systems that lost state during the outage, such as EC2's droplet leases and network propagation backlog, can collapse under their own catch-up work, and container services that rely on new instance launches keep failing to launch and scale until that work is done.

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4. IL-0182 Too few CoreDNS replicas on EKS were overloaded during traffic spikes, raising latency and cutting the network for task pods, and the over-provisioned, tightly spread CoreDNS pods added later blocked Karpenter consolidation

   이언철2024

   Symptoms

   When traffic changed sharply, CoreDNS came under heavy load, and the cluster saw network problems and a sudden rise in latency. The horizontal pod autoscaler then added pods, database connections surged, and the impact spread to the whole service.

   High latencyIntermittent connection failuresAutoscaler misbehaviour

   Trigger

   Sharp changes in traffic loaded the small number of CoreDNS pods beyond what they could handle.

   Root cause

   Capacity exhaustion

   Component

   CoreDNSKarpenterHorizontal pod autoscalerPod scheduling and placement

   Contributing factor

   Automation amplificationHarmful manual action

   Remediation

   • Set up the Cluster Proportional Autoscaler for CoreDNS with nodesPerReplica and coresPerReplica chosen for the instance types in use
   • Spread CoreDNS with soft topologySpreadConstraints (ScheduleAnyway) instead of anti-affinity so Karpenter could consolidate nodes
   • Added alert thresholds on CoreDNS error rate, panics, request duration and request rate
   • Deployed NodeLocal DNSCache so each node answers DNS from a local cache and forwards misses to the main CoreDNS

   Lessons

   A fixed, small number of CoreDNS pods cannot follow a cluster whose nodes and pods change quickly with traffic, and the damage spreads: DNS latency triggers autoscaling, which in turn floods the database with connections. Simply adding many CoreDNS replicas created a different problem, because with tainted nodes and a maxSkew: 1 spread constraint the pods piled onto a few nodes and blocked Karpenter consolidation. Scaling CoreDNS in proportion to the cluster and putting a DNS cache on every node handled both.

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5. IL-0202 A burst of queries and database writes exhausted connection pools and memory on Honeycomb's ingest hosts, and Kubernetes crash-loop back-off kept failed hosts offline so ingest capacity stayed too low to recover

   Honeycomb2023sev1

   Symptoms

   Standard alerting paged Honeycomb's on-call engineers about the performance and stability of event ingestion, and the automated load-shedding mechanisms tripped and sent further notifications. The status page described a higher than normal error rate and response time on the ingestion service.

   High error rateHigh latencyContainer crash loopService outageDatabase overload

   Trigger

   An abnormally large number of queries arrived from many separate connections within a few minutes.

   Root cause

   Capacity exhaustion

   Component

   Application databaseIn-memory cache

   Contributing factor

   Retry stormAutomation amplification

   Detection

   Standard alerting notified the on-call engineers about ingestion performance and stability, and the automated load-shedding mechanisms added notifications when they tripped. The first status post, about alerts on a higher than normal error rate and response time, went up at 18:28 PST (02:28 UTC), 13 minutes after the 02:15 UTC start.

   Remediation

   • Scaled the ingest fleet up manually and aggressively
   • Cut off most ingest for a few minutes so the ingest hosts could come back with enough capacity
   • Temporarily lowered the rate limits for the most aggressive traffic sources until their traffic subsided

   Action items

   • Keep the ingest fleet scaled up to prevent a repeat while longer-term fixes are worked on
   • Decide on the projects that will address these failure paths

   Lessons

   Load shedding and auto-scaling were in place, yet a burst that arrived faster than the cache could spread its writes still took the ingest hosts down. During recovery, Kubernetes' crash loop back-off worked against the operators: the hosts that had failed stayed offline while the fleet needed every host. Clients that retry whole OTel batches after a partial drop can multiply traffic many times over, so per-source rate limits were what finally stabilised ingest.

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