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Kubernetes control plane offers overview
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Kubernetes control plane offers overview

Reviewed on 31 August 2023Published on 31 August 2023

On a managed Kubernetes service such as Kapsule or Kosmos, Scaleway is responsible for overseeing the Kubernetes control plane. The control plane comprises multiple components that play a crucial role in cluster management, application scheduling, and cluster state maintenance.

These components encompass the control plane’s core elements, namely etcd, API server, scheduler, cloud controller, and controller manager.

Scaleway offers a range of Kubernetes control planes to cater to a wide range of use-cases and to provide you with a maximum of flexibility. Whether you are seeking a mutualized environment or a dedicated control plane, we provide a flexible offer to fit your infrastructure needs, which can be a differentiating advantage from other Kubernetes engine providers.

Available control plane offers

Control plane type /
Features
MutualizedDedicated 4Dedicated 8Dedicated 16
Memoryup to 4GB14GB RAM dedicated8GB RAM dedicated16GB RAM dedicated
Availability1 resilient replica2 replicas for HA2 replicas for HA2 replicas for HA
SLAN/A99.5% uptime99.5% uptime99.5% uptime
Max cluster size150 nodes250 nodes500 nodes500 nodes
Note:
  • 1 Each mutualized control plane can use up to 4GB memory (RAM and swap).
  • The resource quotas for each offer are subject to potential evolutions over time, and this table will be modified accordingly.

Dedicated control plane conditions

You have the option of choosing the most suitable control plane type for your workload when setting up a Kubernetes Kapsule or Kubernetes Kosmos cluster.

The cost of the control plane varies depending on whether it is a shared or dedicated environment, and by the specific dedicated resources that you choose. Although indicated that the control planes are billed on a monthly basis in the Scaleway console, the billing for each control plane option is actually calculated on an hourly basis. Scaleway chooses to display a monthly billing for simplicity purposes.

The control plane within the Kubernetes architecture goes beyond being just another component; it acts as the central command that orchestrates the cluster’s activities. Comprising elements like the API server, etcd, scheduler, and controller manager, the control plane oversees critical operations including resource distribution, scheduling, health checks, and scaling. Making frequent modifications to the control plane can result in unintended repercussions, compatibility conflicts, and potential downtimes, directly impacting the overall dependability of the whole cluster. Therefore it is imperative to handle the control plane with the utmost care and prudence in order to sustain a smoothly operating Kubernetes cluster.

When selecting a dedicated environment, you are committing to this plan for a period of 30 calendar days. While the cluster is active, you retain the flexibility to shift their control plane among different types to better align with their workloads. However, certain conditions govern this migration process:

  • Upgrading to a higher tier will reset the commitment period to 30 calendar days.
  • Downgrading to a lower tier will not extend the commitment period.
  • Downgrading a cluster type is not allowed during the commitment period.

Once a dedicated control plane is no longer under commitment, you can freely migrate to a lower tier using the Scaleway console or command-line interface (CLI).

Important:

Migrating to a lower tier will have some impact on key features. The allocated RAM resources will be reduced, the maximum number of nodes will be limited, and in the case of a mutualized control plane, the SLA (Service Level Agreement) will be revoked.

If a dedicated cluster is deleted, the commitment is automatically canceled, and there will be no further billing for this dedicated service.

Changing a control plane offer

During the lifecycle of a Kubernetes cluster, you have the flexibility to transition its control plane to a different type, whether it involves scaling up or scaling down, in order to align with their specific workloads.

Refer to our dedicated documentation for more information regarding the choice of control plane offers for clusters, as well as instructions on how to manage a control plane through the console or API.