Cases8 cases

1 to 5 of 8

1. IL-0464 IONOS Managed Kubernetes control planes went unreachable for minutes at a time as their shared etcd grew and fragmented

   IONOS Cloud2026

   Symptoms

   From 14 to 16 March 2026 a subset of customer control planes on IONOS Managed Kubernetes (MK8s) became unavailable again and again, each time for several minutes. While a cluster was unreachable, operations that depend on the Kubernetes API, such as deployments, scaling and health checks, did not work reliably.

   Control plane unresponsiveImage pull failures

   Root cause

   Capacity exhaustion

   Component

   etcdKubernetes API serverManaged Kubernetes control plane and provider API

   Contributing factor

   Excessive blast radius

   Remediation

   • Defragmented the affected database instances, reclaiming about 3–4 GiB per instance
   • Shortened the database compaction intervals on the affected instances
   • Deployed a fix for the affected AI Model Hub database services
   • Applied final changes to all affected clusters

   Action items

   • Expand monitoring dashboards to show control plane health per database instance
   • Move the highest-volume clusters to dedicated database backends
   • Make incidents of this kind trigger immediate paging
   • Roll the updated compaction interval out to all shared control plane database instances
   • Run automated, scheduled defragmentation jobs on all instances
   • Rebalance clusters across database instances to reduce concentration
   • Contact customers whose clusters write disproportionate data, for example to store large security scan reports outside the control plane database
   • Introduce per-tenant resource quotas and key-count limits on the shared control plane database
   • Re-architect the shared control plane for stronger isolation, with dedicated database instances and better vertical scaling
   • Evaluate full database sharding for customers with consistently high data volumes
   • Hold regular drills on database resource exhaustion and recovery

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2. IL-0204 An etcd memory spike overloaded the control plane of a Render Frankfurt cluster, and DNS then failed because CoreDNS ran on the rebooted control plane nodes and could not start without the API server

   Render2024

   Symptoms

   In January 2022 an etcd memory spike overloaded the Kubernetes control plane of one of Render's clusters in Frankfurt. At first only operations that need the control plane failed: new deploys and cron job runs.

   Control plane unresponsiveDeploys failingDNS resolution failuresHigh error rate

   Trigger

   A memory spike in etcd overloaded the cluster's control plane.

   Root cause

   Capacity exhaustion

   Component

   etcdCoreDNSKubernetes API server

   Contributing factor

   Unreviewed defaultsHarmful manual action

   Action items

   • Run CoreDNS on data plane nodes instead of control plane nodes
   • Run etcd on dedicated nodes

   Lessons

   CoreDNS can keep answering from its in-memory cache while the API server is down, but it cannot initialise without it. With CoreDNS on the control plane nodes, a reboot or restart during a control plane outage turned a failure of deploys and cron jobs into a failure of requests to services that were already running. The core problem was that the data plane was not isolated enough from the control plane: even without the reboot, the etcd memory spike alone could have forced CoreDNS to restart.

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3. IL-0195 The kube-apiservers of a cluster with more than 200 nodes ran out of memory and crashed one after another under mass LIST requests from Cilium agents

   Palark2023

   Symptoms

   A tech support engineer called Palark to say that a customer's cluster, production environment included, had practically stopped working. On the control plane nodes the kube-apiserver processes had used up all the memory and were crashing and restarting over and over, so the Kubernetes API was unreachable and completely non-functional.

   Control plane unresponsiveOOMKilled

   Trigger

   Cilium agent pods on more than 200 nodes sent LIST requests to the Kubernetes API en masse at the same time.

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverCilium

   Contributing factor

   Unreviewed defaults

   Detection

   A tech support engineer phoned the team to report that the customer's cluster was practically non-operational.

   Remediation

   • added CPU and memory to the control plane nodes in two steps, from 8 CPUs and 16 GB of RAM to 16 CPUs and 64 GB, until the API stabilised
   • restarted the Cilium agents in a test window agreed with the customer to reproduce the memory growth
   • deployed a FlowSchema and a PriorityLevelConfiguration named cilium-pods that limit and queue LIST requests from the Cilium service accounts to cilium.io resources
   • reduced the control plane nodes back to their original resources

   Action items

   • Make an API Priority and Fairness configuration a standard part of Palark's Kubernetes platform

   Lessons

   In a large cluster, many agents sending LIST requests at once can exhaust kube-apiserver memory, and with several API servers the crash moves from one to the next. APF can hold back such a client: a FlowSchema matches its requests and a PriorityLevelConfiguration gives them a small concurrency share and a queue, so the requests run more slowly instead of all at once. With APF enabled, the total concurrency it divides is the sum of --max-requests-inflight (default 400) and --max-mutating-requests-inflight (default 200), shared among priority levels in proportion to their assured concurrency shares, so raising one level's share lowers every other level's limit. Long-running requests such as logs and exec, and WATCH requests, are not subject to APF. The response headers X-Kubernetes-PF-FlowSchema-UID and X-Kubernetes-PF-PriorityLevel-UID show where a request landed, and the metrics Apiserver_flowcontrol_rejected_requests_total, Apiserver_current_inqueue_requests and Apiserver_flowcontrol_request_execution_seconds, with the debug endpoints /debug/api_priority_and_fairness/dump_priority_levels and /debug/api_priority_and_fairness/dump_queues, show how the queues behave.

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4. IL-0165 Some LKE users in Frankfurt could not reach their kube-apiservers with kubectl after backend services failed under memory constraints

   Linode2023

   Symptoms

   At 14:11 UTC on May 25, 2023, Linode learned that part of its LKE users in the Frankfurt data center could not reach their kube-apiservers through kubectl. The status page opened at 14:27 UTC with "We are investigating reports of connectivity issues with the LKE service within the Frankfurt Datacenter".

   Control plane unresponsive

   Root cause

   Capacity exhaustion

   Component

   Kubernetes API serverContainer memory limit

   Contributing factor

   –

   Detection

   Linode was made aware of the problem at 14:11 UTC; the status page refers to reports of connectivity issues.

   Remediation

   • increased memory limits

   Action items

   • Increase resilience, redundancy and monitoring for the LKE components affected by the incident

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5. IL-0194 After a kube-apiserver restart, pod LIST requests from every pod of a Vector DaemonSet nearly tripled etcd memory and restarted all master nodes

   Palark2023

   Symptoms

   One evening, right after an ordinary restart of kube-apiserver in a customer's production cluster, etcd memory consumption rose almost threefold. The sudden growth led to a cascading restart of all the master nodes.

   Control plane unresponsiveDatabase overload

   Trigger

   An ordinary restart of kube-apiserver, after which every pod of the log collector DaemonSet sent its list requests at the same time.

   Root cause

   Capacity exhaustion

   Component

   etcdKubernetes API serverTelemetry agent

   Contributing factor

   Unknown limitUnreviewed defaults

   Detection

   Memory graphs showed etcd memory on the master nodes almost tripling after a kube-apiserver restart; the audit logs then pointed to one DaemonSet's list requests.

   Remediation

   • created a PriorityLevelConfiguration named limit-list-custom that queues requests (assuredConcurrencyShares 5, 16 queues, handSize 4, queueLengthLimit 50)
   • created a FlowSchema that routes list and get requests on pods from the log collector's service account to that priority level

   Action items

   • Submit a pull request adding resource version support to the Kubernetes Rust library
   • Open a pull request to the Vector log collector to address the startup list requests
   • Apply the same flow-control approach to a similar problem found with the Cilium CNI

   Lessons

   A field selector reduces what kube-apiserver returns, not what it reads from etcd: listing the pods of one node still reads every pod in the cluster. A DaemonSet whose pods all list at startup multiplies that by the node count each time kube-apiserver restarts. Where exact freshness is not needed, resourceVersion=0 lets kube-apiserver answer from its cache; where the client cannot set it, API Priority and Fairness can queue the requests so they reach etcd gradually.

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