Cases6 cases

1 to 5 of 6

1. IL-0006 VPC subnets ran out of IP addresses because the AWS VPC CNI kept IPs on full nodes, and WARM_IP_TARGET changes then blocked pod starts after every pod deletion

   Neon2025

   Symptoms

   On 16 May 2025, in Neon's Kubernetes cluster in AWS us-east-1, new compute pods were scheduled but could not start and failed with IP allocation errors. Each Neon compute is a Postgres database running in a lightweight virtual machine in its own pod, so without new pods, idle customer databases could not be woken up.

   IP address exhaustionPods Pending

   Trigger

   On 16 May the Neon control plane's periodic job that terminates idle computes started failing. Compute creations continued, and the number of active computes rose from about 5k to about 8.1k within minutes. On 19 May, a follow-up change that set WARM_IP_TARGET back to 1 started the second round of failures.

   Root cause

   Capacity exhaustion

   Component

   AWS VPC CNIVPC subnet or pod address rangeCluster autoscaler

   Contributing factor

   Unreviewed defaultsNo capacity planningUnverified assumptionHarmful manual actionIneffective rollbackRetry storm

   Detection

   As cluster-autoscaler added nodes, the team saw IP allocation errors on new pods that were scheduled but could not start. The rate of successful pod starts and VPC subnet metrics were then used to follow the problem.

   Remediation

   • Set WARM_IP_TARGET=1 to release IPs held by nodes without free CPU or memory
   • Fixed the control plane's periodic job so idle computes were terminated again
   • Restarted the aws-node DaemonSet several times, each with a temporary improvement
   • Set WARM_IP_TARGET=0, MINIMUM_IP_TARGET=0 and WARM_ENI_TARGET=0, which let nodes allocate enough IPs and stabilised the cluster
   • On 19 May, set WARM_IP_TARGET back to 0 after setting it to 1 failed again
   • Enlarged the pool of pre-created warm compute pods to absorb failed pod starts

   Action items

   • Open a pull request to AWS CNI to improve how ipamd releases IPs in this situation
   • Follow up with AWS Support about the roughly 100 subnet IPs that were never allocated, for reasons Neon could not explain
   • Change the architecture to support horizontal scaling

   Lessons

   With default settings AWS CNI can keep many IPs on nodes that cannot use them, so the share of subnet IPs assigned to pods does not tell how close the subnet is to running out. WARM_IP_TARGET and WARM_ENI_TARGET follow different logic, a warm IP target of zero switches to the ENI logic, and IPs in cooldown count toward the target, which behaves badly under high pod churn. Changing these settings during an incident without knowing that logic prolonged the outage, and reverting a setting did not bring back the earlier healthy state.

   View case
2. IL-0007 The AWS VPC CNI could not assign IP addresses to pods as an EKS cluster ran out of IPv4 space

   Adevinta2024

   Symptoms

   One evening the platform team that runs SCHIP, Adevinta's internal runtime platform on Kubernetes, was paged because some DaemonSets were failing. Checking the pod status showed this error from the network plugin: name="aws-cni" failed (add): add cmd: failed to assign an IP address to container

   IP address exhaustionPods Pending

   Trigger

   Not stated for the first page. It came in June, during the migration from SCHIP's kube-aws-based clusters to EKS-based clusters; the write-up mentions spikes of pods, such as a job that goes rogue or failing jobs that keep their IPs, as ways a cluster can run out.

   Root cause

   Capacity exhaustion

   Component

   AWS VPC CNIVPC subnet or pod address rangeKarpenter

   Contributing factor

   Unreviewed defaultsNo capacity planning

   Detection

   A pager alert that some DaemonSets were failing. The on-call engineer checked the pod status and found the aws-cni error about failing to assign an IP address.

   Remediation

   • Rolled out VPC CNI custom networking with a secondary CIDR to the smaller clusters from late August
   • Paused the EKS migration in September while available IPs were very low
   • Rolled out custom networking to the rest of the fleet
   • Added the --reserved-enis parameter to the Karpenter deployment

   Action items

   • Use VPC CNI custom networking with a secondary CIDR across the whole fleet
   • Make Karpenter account for custom networking with the --reserved-enis parameter

   Lessons

   For a platform team, the VPC CNI is not ready to scale in production without time spent on capacity planning, and every option has its pros and cons. For large clusters started today, IPv6 is the cheaper and more future-proof option. Do not assume that what works in small clusters will keep working when they grow.

   View case
3. IL-0070 Critical pods on core nodes evicted when Prometheus began scraping about 200 nodes after a dask-gateway scale-up

   2i2c2023

   Symptoms

   The hub pod of the prod JupyterHub on the leap cluster (leap.2i2c.org) restarted unexpectedly during a large node scale-up, and a user opened a support ticket. The ingress-nginx and proxy pods kept running.

   Pod evictionPods Pending

   Trigger

   A user's dask-gateway workload added about 100 dask-worker nodes within a minute, and Prometheus started scraping the metrics exporters on about 200 nodes.

   Root cause

   Capacity exhaustion

   Component

   PrometheusNode pool

   Contributing factor

   No headroomExcessive blast radiusMonitoring blind spot

   Detection

   A user support ticket (2i2c Freshdesk ticket 414) about the leap hub; the engineer then looked at Grafana and the cluster's nodes and events.

   Action items

   • Configure CPU and memory requests for the hub and proxy pods (#2127; at least 100m CPU was proposed)
   • Make the Grafana OOMKiller dashboard work (#2213)
   • Move LEAP cloud infrastructure to shared nodes (#2209)
   • Update resource requests and limits, mainly for ingress-nginx and Prometheus (#2324)
   • Decide whether Prometheus should scrape dask worker pods (#2366)

   Lessons

   Prometheus load grows with the number of nodes, so a burst of autoscaled nodes can push a nearly full core node over the edge and evict the pods that matter most. Core pods that request 10m of CPU get a tenth of the CPU share of busy neighbours that request 100m. Kubernetes events expire after an hour, so evidence of OOM kills needs a dashboard that keeps it.

   View case
4. IL-0015 A GKE cluster's /16 pod address range ran out at 256 nodes, leaving new pods Pending and node scale-ups failing

   loveholidays2020

   Symptoms

   A colleague wrote in Slack that his application was taking a long time to deploy. A deployment that normally finishes in minutes had been running for about an hour: about half of the new pods were running and serving traffic, and the other half were Pending.

   Pods PendingIP address exhaustionScale-up failure

   Trigger

   The cluster reaching 256 nodes, the most its /16 pod secondary range could hold at 256 addresses per node; every further node creation failed.

   Root cause

   Capacity exhaustion

   Component

   VPC subnet or pod address rangeCluster autoscalerNode pool

   Contributing factor

   Unreviewed defaultsUnknown limit

   Detection

   A colleague reported in Slack that a deployment was slow; the pending pods' events and the cluster autoscaler status ConfigMap showed the exhausted IP space.

   Remediation

   • lowered the maximum pods per node on selected node pools, cutting IP consumption by about 30%
   • moved some node pools to larger instance types

   Action items

   • Lower the maximum pods per node on node pools that do not run many pods
   • Use larger instance types in some node pools

   Lessons

   In a VPC-native GKE cluster the number of nodes, not the number of pods, uses up the pod range: by default each node reserves 256 addresses, so even a /16 is full at 256 nodes. Maximum pods per node and node size are the practical levers; larger nodes pack better but take more pods down when they fail.

   View case
5. IL-0040 Parallel Helm installs saturated small gp2 root volumes on a kops cluster and left all three workers NotReady with the kubelet or Docker down

   Saltside2017

   Symptoms

   While running the CI process on a local machine to test an application Helm chart, the SRE lead watched the pods with watch kubectl get pods. All pods reached Running, and then some went back to Pending. Two hours later the problem had grown and some pods showed the phase Unknown.

   Pods PendingImage pull failuresNode NotReadyService outage

   Trigger

   A CI run installed the application's Helm charts for all four markets (Efritin.com, Tonaton.com, Ikman.lk and Bikroy.com) on the nonproduction cluster at the same time.

   Root cause

   Capacity exhaustion

   Component

   Node root diskkubeletContainer runtimeHelm

   Contributing factor

   Unreviewed defaultsMonitoring blind spotIgnored early signalSingle point of failure

   Detection

   The SRE lead saw pods go from Running back to Pending while watching kubectl get pods during the CI run; no monitoring raised an alert, even when all three workers were not ready.

   Remediation

   • terminated the three worker nodes and let the auto scaling group replace them

   Action items

   • Fix the Datadog integration for the nonproduction AWS account and repeat the installs with telemetry
   • Increase the worker root EBS volumes from 20GB to 120GB
   • Keep common base images pulled on every node with a DaemonSet until kops supports user hooks
   • Configure different allocation limits for the different layers in Kubernetes
   • Discuss a highly available Tiller with the Helm team
   • Find a way to throttle the number of newly created pods

   Lessons

   An ordinary deploy of the application could collapse the cluster, because the default 20GB gp2 root volumes could not keep up with parallel image pulls and container starts. Without node metrics and pod phase metrics, three NotReady workers raised no alert. A single Tiller replica can block the deploy that would fix an outage.

   View case

By root cause1