Managed (Tenant) Kubernetes

Managed Kubernetes in Cozystack

Whenever you want to deploy a custom containerized application in Cozystack, it’s best to deploy it to a managed Kubernetes cluster.

Cozystack deploys and manages Kubernetes-as-a-service as standalone applications within each tenant’s isolated environment. In Cozystack, such clusters are named tenant Kubernetes clusters, while the base Cozystack cluster is called a management or root cluster. Tenant clusters are fully separated from the management cluster and are intended for deploying tenant-specific or customer-developed applications.

Within a tenant cluster, users can take advantage of LoadBalancer services and easily provision physical volumes as needed.
The control-plane operates within containers, while the worker nodes are deployed as virtual machines, all seamlessly managed by the application.

Kubernetes version in tenant clusters is independent of Kubernetes in the management cluster. Users can select the supported patch versions from 1.31 to 1.35.

Why Use a Managed Kubernetes Cluster?

Kubernetes has emerged as the industry standard, providing a unified and accessible API, primarily utilizing YAML for configuration. This means that teams can easily understand and work with Kubernetes, streamlining infrastructure management.

Kubernetes leverages robust software design patterns, enabling continuous recovery in any scenario through the reconciliation method. Additionally, it ensures seamless scaling across a multitude of servers, addressing the challenges posed by complex and outdated APIs found in traditional virtualization platforms. This managed service eliminates the need for developing custom solutions or modifying source code, saving valuable time and effort.

The Managed Kubernetes Service in Cozystack offers a streamlined solution for efficiently managing server workloads.

Starting Work

Once the tenant Kubernetes cluster is ready, you can get a kubeconfig file to work with it. It can be done via UI or a kubectl request:

• Open the Cozystack dashboard, switch to your tenant, find and open the application page. Copy one of the config files from the Secrets section.

• Run the following command (using the management cluster kubeconfig):

  kubectl get secret -n tenant-<name> kubernetes-<clusterName>-admin-kubeconfig -o go-template='{{ printf "%s\n" (index .data "admin.conf" | base64decode) }}' > admin.conf
  

There are several kubeconfig options available:

• admin.conf — The standard kubeconfig for accessing your new cluster. You can create additional Kubernetes users using this configuration.
• admin.svc — Same token as admin.conf, but with the API server address set to the internal service name. Use it for applications running inside the cluster that need API access.
• super-admin.conf — Similar to admin.conf, but with extended administrative permissions. Intended for troubleshooting and cluster maintenance tasks.
• super-admin.svc — Same as super-admin.conf, but pointing to the internal API server address.

Implementation Details

A tenant Kubernetes cluster in Cozystack is essentially Kubernetes-in-Kubernetes. Deploying it involves the following components:

• Kamaji Control Plane: Kamaji is an open-source project that facilitates the deployment of Kubernetes control planes as pods within a root cluster. Each control plane pod includes essential components like kube-apiserver, controller-manager, and scheduler, allowing for efficient multi-tenancy and resource utilization.

• Etcd Cluster: A dedicated etcd cluster is deployed using the cozystack etcd-operator (etcd-operator.cozystack.io/v1alpha2). It provides reliable and scalable key-value storage for the Kubernetes control plane.

• Worker Nodes: Virtual Machines are provisioned to serve as worker nodes using KubeVirt. These nodes are configured to join the tenant Kubernetes cluster, enabling the deployment and management of workloads. Worker node disks automatically detect and match the underlying volume’s block size (blockSize.matchVolume) to ensure compatibility with storage backends that use non-512-byte sectors, such as LINSTOR DRBD with 4Kn drives.

• Cluster API: Cozystack is using the Kubernetes Cluster API to provision the components of a cluster.

This architecture ensures isolated, scalable, and efficient tenant Kubernetes environments.

See the reference for components utilized in this service:

• Kamaji Control Plane
• Kamaji — Cluster API
• github.com/clastix/kamaji
• KubeVirt
• github.com/kubevirt/kubevirt
• github.com/cozystack/etcd-operator
• Kubernetes Cluster API
• github.com/kubernetes-sigs/cluster-api-provider-kubevirt
• github.com/kubevirt/csi-driver

Breaking Changes

• ephemeralStorage renamed to diskSize (v1.4): The nodeGroups[name].ephemeralStorage field has been renamed to nodeGroups[name].diskSize. Existing clusters are migrated transparently by platform migration 41 during the pre-upgrade hook — no manual action is required. Newly written values should use diskSize. Existing VMs are rolling-updated via CAPI when the new values are applied. With the Talos worker rollover in this release the field now sizes the single system disk (Talos OS image streamed from factory.talos.dev, kubelet state, containerd image cache, local-path PVCs) — the pre-Talos disk-kubelet PVC layout was removed. State on that single disk persists across same-VM reboots (virt-launcher restart, guest reboot, node failure); VM replacement by CAPI (e.g. nodeGroup field change, MachineHealthCheck remediation) provisions a fresh disk.

• Air-gapped tenant workers (Phase 1 Talos rollover): tenant worker bootstrap moves from Ubuntu containerDisk + kubeadm to a Talos OS disk image fetched over HTTP by CDI and a .../installer/... installer reference. Both sources are now overridable — set talos.imageFactoryURL (default https://factory.talos.dev) to a self-hosted Image Factory, a caching mirror, or an internal HTTP file server, and talos.installerRepository (default factory.talos.dev/installer) to a mirrored registry. The Helm-rendered *-patch-containerd Secret that previously plumbed the platform-wide registries.mirrors config to tenant workers (via the kubeadm template’s containerd certs.d mount) has no consumer in the Talos machineconfig and was removed in this release; in-guest container-image pulls (pods pulling from registries inside the tenant cluster) still honour no per-tenant registries.mirrors override until Phase 2 restores it via machine.registries.mirrors knobs. So a strict-egress tenant can now mirror the Talos OS image and installer, but rate-limited in-guest registry pulls remain a Phase 2 follow-up — file an issue if you depend on this and it is not yet landed.

• Worker MachineHealthCheck remediation is now enabled by default (Phase 1 Talos rollover): the worker MHC maxUnhealthy moved from a hard-coded 0 (remediation effectively off) to a configurable nodeHealthCheck.maxUnhealthy defaulting to "50%". CAPI now auto-remediates (deletes and replaces) unhealthy worker Machines while at least 50% of a group stays healthy. "50%" deliberately leaves headroom for transient unhealthy nodes during the kubeadm→Talos rollover and slow first boots from factory.talos.dev; set nodeHealthCheck.maxUnhealthy: "0%" to keep the previous remediation-off behaviour until your fleet is stable on Talos workers.

• GitOps-managed tenant Kubernetes CRs must bump spec.version in Git (Phase 1 Talos rollover): platform migration 46 patches live kuberneteses.apps.cozystack.io objects from v1.30 to v1.31 (v1.30 left the Talos↔Kubernetes support matrix). When the tenant Kubernetes CR is reconciled from Git (Flux/Argo), the next source reconcile re-applies version: v1.30 over migration 46’s patch, which then trips the chart’s _versions.tpl guard and the HelmRelease fails. Update spec.version to v1.31 (or newer) in your Git source before/with the platform upgrade. Tenants managed via the API or dashboard need no action — migration 46 handles them.

• Remote-accessible LINSTOR StorageClasses are auto-propagated to tenants (v1.5): infra-cluster LINSTOR StorageClasses whose linstor.csi.linbit.com/allowRemoteVolumeAccess is not "false" are created inside each tenant under the same name (provisioned by csi.kubevirt.io). Upgrade caveat: delete any manually created tenant StorageClass whose name collides with a propagated infra class (e.g. a hand-made replicated) before upgrading, or the propagated class cannot be created and the tenant CSI release will not converge. Infra classes that must stay node-local need allowRemoteVolumeAccess: "false" set explicitly — an absent annotation is treated as remote-accessible. Propagation is evaluated at HelmRelease render time, so classes added or removed on the infra cluster after a tenant exists propagate only on that tenant’s next reconcile.

The top-level storageClass field is annotated as immutable in the chart schema — see docs/storage-immutability.md for the contract and which consumers enforce it. The per-node-group nodeGroups[name].storageClass field is intentionally not annotated immutable: it is optional and undefaulted, so a strict self == oldSelf rule would block any future attempt to set it on an existing node group.

Parameters

Common Parameters

Application-specific Parameters

Cluster Addons

Kubernetes Control Plane Configuration

Talos Worker Image

Node Health Check

OIDC

Parameter examples and reference

resources and resourcesPreset

resources sets explicit CPU and memory configurations for each replica. When left empty, the preset defined in resourcesPreset is applied.

resources:
  cpu: 4000m
  memory: 4Gi

resourcesPreset sets named CPU and memory configurations for each replica. This setting is ignored if the corresponding resources value is set.

instanceType Resources

The following instanceType resources are provided by Cozystack:

Kubelet Resource Reservations

Each node group supports a kubelet object that configures how much memory and CPU the kubelet reserves for the host OS and the Kubernetes/system components running on the worker node.

When systemReservedMemory or kubeReservedMemory are left empty, they are auto-computed using the following formula:

1. Determine the effective memory of the node:
   • If resources.memory is explicitly set, use that value.
   • Otherwise, look up the instanceType and use its memory.guest value.
   • If neither is available, the reservation falls back to the minimum (256Mi).
2. Calculate 5% of the effective memory (in MiB, rounded down).
3. Clamp the result to the range [256Mi, 1Gi]:
   • Nodes with 5 GiB or less get the minimum 256Mi reservation.
   • Nodes with 20 GiB or more get the maximum 1Gi reservation.

Both systemReservedMemory and kubeReservedMemory receive the same auto-computed value by default.

CPU reservations (systemReservedCpu, kubeReservedCpu) follow the same pattern: 5% of effective CPU, clamped to [50m, 500m]. Both are auto-computed when left empty.

Kubelet Defaults

Eviction thresholds can be specified as percentages (e.g., 7%) or absolute values (e.g., 200Mi). Both thresholds must use the same unit type. The hard threshold must be strictly less than the soft threshold.

The evictionSoftGracePeriod and evictionMinimumReclaim parameters are currently hardcoded in the chart template and cannot be overridden through values.

Capacity Annotation

When kubelet resource reservations are configured, both the capacity.cluster-autoscaler.kubernetes.io/memory and capacity.cluster-autoscaler.kubernetes.io/cpu annotations on MachineDeployments report allocatable values instead of total node resources. Memory allocatable subtracts system-reserved, kube-reserved, and eviction-hard. CPU allocatable subtracts system-reserved and kube-reserved. This aligns the annotations with the values the cluster autoscaler uses in its scaling calculations.

Upgrading from a version without this feature may cause the autoscaler to see reduced per-node capacity after the annotations are updated, which can trigger additional scale-up operations. No action is typically required — the new values reflect the actual memory available for workload scheduling.

Note: When neither resources.memory nor instanceType is set, eviction thresholds (default 7% hard / 10% soft) are still enforced by the kubelet at runtime, but the capacity annotation is not rendered. Without this annotation, the cluster-autoscaler has no visibility into these reservations and may over-schedule the node until evictions fire.

Example: Override kubelet reservations

nodeGroups:
  md0:
    instanceType: "u1.large"
    kubelet:
      systemReservedMemory: "256Mi"
      kubeReservedMemory: "256Mi"
      evictionHardMemory: "500Mi"
      evictionSoftMemory: "1Gi"

U Series: Universal

The U Series is quite neutral and provides resources for general purpose applications.

U is the abbreviation for “Universal”, hinting at the universal attitude towards workloads.

VMs of instance types will share physical CPU cores on a time-slice basis with other VMs.

U Series Characteristics

Specific characteristics of this series are:

• Burstable CPU performance - The workload has a baseline compute performance but is permitted to burst beyond this baseline, if excess compute resources are available.
• vCPU-To-Memory Ratio (1:4) - A vCPU-to-Memory ratio of 1:4, for less noise per node.

O Series: Overcommitted

The O Series is based on the U Series, with the only difference being that memory is overcommitted.

O is the abbreviation for “Overcommitted”.

O Series Characteristics

Specific characteristics of this series are:

• Burstable CPU performance - The workload has a baseline compute performance but is permitted to burst beyond this baseline, if excess compute resources are available.
• Overcommitted Memory - Memory is over-committed in order to achieve a higher workload density.
• vCPU-To-Memory Ratio (1:4) - A vCPU-to-Memory ratio of 1:4, for less noise per node.

CX Series: Compute Exclusive

The CX Series provides exclusive compute resources for compute intensive applications.

CX is the abbreviation of “Compute Exclusive”.

The exclusive resources are given to the compute threads of the VM. In order to ensure this, some additional cores (depending on the number of disks and NICs) will be requested to offload the IO threading from cores dedicated to the workload. In addition, in this series, the NUMA topology of the used cores is provided to the VM.

CX Series Characteristics

Specific characteristics of this series are:

• Hugepages - Hugepages are used in order to improve memory performance.
• Dedicated CPU - Physical cores are exclusively assigned to every vCPU in order to provide fixed and high compute guarantees to the workload.
• Isolated emulator threads - Hypervisor emulator threads are isolated from the vCPUs in order to reduce emaulation related impact on the workload.
• vNUMA - Physical NUMA topology is reflected in the guest in order to optimize guest sided cache utilization.
• vCPU-To-Memory Ratio (1:2) - A vCPU-to-Memory ratio of 1:2.

M Series: Memory

The M Series provides resources for memory intensive applications.

M is the abbreviation of “Memory”.

M Series Characteristics

Specific characteristics of this series are:

• Hugepages - Hugepages are used in order to improve memory performance.
• Burstable CPU performance - The workload has a baseline compute performance but is permitted to burst beyond this baseline, if excess compute resources are available.
• vCPU-To-Memory Ratio (1:8) - A vCPU-to-Memory ratio of 1:8, for much less noise per node.

RT Series: RealTime

The RT Series provides resources for realtime applications, like Oslat.

RT is the abbreviation for “realtime”.

This series of instance types requires nodes capable of running realtime applications.

RT Series Characteristics

Specific characteristics of this series are:

• Hugepages - Hugepages are used in order to improve memory performance.
• Dedicated CPU - Physical cores are exclusively assigned to every vCPU in order to provide fixed and high compute guarantees to the workload.
• Isolated emulator threads - Hypervisor emulator threads are isolated from the vCPUs in order to reduce emaulation related impact on the workload.
• vCPU-To-Memory Ratio (1:4) - A vCPU-to-Memory ratio of 1:4 starting from the medium size.