Cases7 cases

4 to 7 of 7

1. IL-0128 Network interfaces that dropped for a few seconds made NCCL time out and stalled or slowed LLM training on a cluster of more than 10,000 GPUs

   ByteDance2024

   Symptoms

   ByteDance's large language model training jobs on its MegaScale system, which runs on more than 10,000 GPUs, occasionally stalled or slowed down. The cause was network interfaces that flapped frequently.

   Link flappingNCCL timeoutThroughput degradation

   Trigger

   Network interfaces went down for several seconds and came back up, dropping every packet in transit.

   Root cause

   DNS or network fault

   Component

   NCCLNIC, cable and switch port link

   Contributing factor

   Unreviewed defaults

   Detection

   MegaScale's monitoring system uses second-level monitoring to track link flapping and other NIC issues. When the RDMA traffic metrics carried in the training daemons' heartbeats drop sharply or fluctuate abnormally, the driver raises an alert for manual investigation, and if the traffic stops entirely it starts the fault recovery procedure automatically.

   Remediation

   • set NCCL's timeout threshold explicitly to a larger value than the default
   • tuned NCCL's retransmit timer and retry count for fast recovery under link flapping
   • enabled the adap_retrans feature on the NICs
   • applied lower-level quality control to NIC signal strength, AOC cable quality and switch-side signal strength

   Lessons

   A default NCCL timeout can be shorter than the few seconds an interface needs to come back from a flap, so a brief link loss becomes a failed collective and a stalled job. A larger timeout and faster retransmission give the link time to return, but the flapping itself came from physical link quality between NIC, AOC cable and switch, and quality control at that level reduced it.

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2. IL-0198 An automatic Ubuntu security update restarted systemd-networkd, which flushed the IP rules Cilium relied on and cut more than 60 percent of Datadog's Kubernetes nodes off the network across regions and cloud providers

   Datadog2023

   Symptoms

   Between 06:00 and 07:00 UTC on 8 March 2023, Kubernetes nodes in several Datadog regions lost network connectivity at the same time, on AWS, Google Cloud and Azure. All services across multiple regions were affected, including the regions that host Datadog's CI/CD and automation tools. Datadog's regions are not connected to each other and changes are rolled out one region at a time, so a simultaneous failure on several cloud providers looked implausible at first.

   Node NotReadyService outageThroughput degradation

   Trigger

   On 7 March 2023 Ubuntu published a systemd patch for a CVE (249.11-0ubuntu3.7). Ubuntu's default unattended upgrades installed it on the nodes between 06:00 and 07:00 UTC on 8 March, and installing it restarted systemd-networkd.

   Root cause

   Breaking change in an upgrade

   Component

   systemd-networkdCiliumNode operating system image

   Contributing factor

   Unreviewed defaultsChange with a delayed effectNo staged rolloutAutomation amplification

   Remediation

   • restarted thousands of disconnected instances on Google Cloud and Azure through the cloud APIs, keeping their local disks
   • rebuilt the data lost with terminated AWS instances' local disks through replication, reprocessing and restores from backup

   Lessons

   A change in a base operating system component can stay dormant for months until something restarts that component. Here the trigger was a security patch, and the change reached every region at once because automatic OS updates ran outside the progressive, region-by-region process used for every other change. Cloud providers react differently to an instance that loses its network. Google Cloud and Azure left the instances running, so a restart recovered them with their disks; AWS replaced them quickly, which brought stateless services back fast but destroyed the local-disk data the stateful services needed.

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3. IL-0116 A CNI configuration change incompatible with a hardware variant that exists only in production cut network connectivity in an OpenAI GPU cluster and took text-davinci-003 fully down

   OpenAI2023

   Symptoms

   At 4:53 pm Pacific on 25 January 2023 one of OpenAI's GPU clusters had a major outage, and the text-davinci-003 model went fully down.

   Service outageThroughput degradation

   Trigger

   A configuration change to the CNI (Container Network Interface) plugins, which provide network connectivity to containers, was deployed to a GPU cluster that contained a hardware variant the staging environment did not have.

   Root cause

   Configuration change

   Component

   CNI plugin

   Contributing factor

   Unrepresentative stagingMonitoring blind spot

   Detection

   Engineers immediately identified the problem as network connectivity, but it took nearly an hour to identify CNI as the cause.

   Remediation

   • put a fix for the CNI configuration change in place, which restored 80% of traffic immediately
   • deployed models back onto GPUs over about three more hours until capacity was fully stable

   Action items

   • Make staging environments contain every type of hardware configuration so they better represent production
   • Monitor and alert on the health of the CNI system directly
   • Fix the network and storage bottlenecks that limit how quickly many replicas of a model can be deployed

   Lessons

   A change that had been deployed to other clusters over 24 hours and deemed safe still broke a cluster with different hardware, so staging needs every hardware configuration that production has.

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4. IL-0032 Mutating admission controller generated broken JSON patches, silently blocking deploys for a week until a production service ran down to two pods

   Airbnb2020

   Symptoms

   A production service at Airbnb showed a high error rate and could not be deployed. The deploy failed with a cryptic Kubernetes error about being unable to access invalid index 5.

   High error rateThroughput degradationDeploys failing

   Trigger

   A change to the mutating admission controller, which had been rolled out within the previous month, had gone in seven days before the incident surfaced; when it had to delete two items from an array in a spec, it generated an invalid JSON patch that removed index 2 and then index 3.

   Root cause

   Software defect

   Component

   Admission webhookHorizontal pod autoscaler

   Contributing factor

   Change with a delayed effectMonitoring blind spotIgnored early signal

   Detection

   The affected service reported a high error rate and its deploy failed; the slow degradation of other services over the previous seven days had gone unnoticed.

   Remediation

   • deleted the mutating admission controller from the affected cluster

   Action items

   • Create alerts from the existing metrics that would have shown the problem earlier

   Lessons

   Keep in mind that a mutating admission controller exists in the cluster; a recently added one is easy to forget during an incident. Search for the exact error message early instead of working on the visible symptoms. Page a teammate when progress stalls.

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