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The Gardener Scheduler is in essence a controller that watches newly created shoots and assigns a seed cluster to them. Conceptually, the task of the Gardener Scheduler is very similar to the task of the Kubernetes Scheduler: finding a seed for a shoot instead of a node for a pod.

Either the scheduling strategy or the shoot cluster purpose hereby determines how the scheduler is operating. The following sections explain the configuration and flow in greater detail.

Why Is the Gardener Scheduler Needed?

1. Decoupling

Previously, an admission plugin in the Gardener API server conducted the scheduling decisions. This implies changes to the API server whenever adjustments of the scheduling are needed. Decoupling the API server and the scheduler comes with greater flexibility to develop these components independently.

2. Extensibility

It should be possible to easily extend and tweak the scheduler in the future. Possibly, similar to the Kubernetes scheduler, hooks could be provided which influence the scheduling decisions. It should be also possible to completely replace the standard Gardener Scheduler with a custom implementation.

Algorithm Overview

The following sequence describes the steps involved to determine a seed candidate:

  1. Determine usable seeds with “usable” defined as follows:
    • no .metadata.deletionTimestamp
    • .spec.settings.scheduling.visible is true
    • .status.lastOperation is not nil
    • conditions GardenletReady, BackupBucketsReady (if available) are true
  2. Filter seeds:
    • matching .spec.seedSelector in CloudProfile used by the Shoot
    • matching .spec.seedSelector in Shoot
    • having no network intersection with the Shoot’s networks (due to the VPN connectivity between seeds and shoots their networks must be disjoint)
    • whose taints (.spec.taints) are tolerated by the Shoot (.spec.tolerations)
    • whose capacity for shoots would not be exceeded if the shoot is scheduled onto the seed, see Ensuring seeds capacity for shoots is not exceeded
    • which have at least three zones in .spec.provider.zones if shoot requests a high available control plane with failure tolerance type zone.
  3. Apply active strategy e.g., Minimal Distance strategy
  4. Choose least utilized seed, i.e., the one with the least number of shoot control planes, will be the winner and written to the .spec.seedName field of the Shoot.

In order to put the scheduling decision into effect, the scheduler sends an update request for the Shoot resource to the API server. After validation, the gardener-apiserver updates the Shoot to have the spec.seedName field set. Subsequently, the gardenlet picks up and starts to create the cluster on the specified seed.


The Gardener Scheduler configuration has to be supplied on startup. It is a mandatory and also the only available flag. This yaml file holds an example scheduler configuration.

Most of the configuration options are the same as in the Gardener Controller Manager (leader election, client connection, …). However, the Gardener Scheduler on the other hand does not need a TLS configuration, because there are currently no webhooks configurable.


The scheduling strategy is defined in the candidateDeterminationStrategy of the scheduler’s configuration and can have the possible values SameRegion and MinimalDistance. The SameRegion strategy is the default strategy.

Same Region strategy

The Gardener Scheduler reads the spec.provider.type and .spec.region fields from the Shoot resource. It tries to find a seed that has the identical .spec.provider.type and .spec.provider.region fields set. If it cannot find a suitable seed, it adds an event to the shoot stating that it is unschedulable.

Minimal Distance strategy

The Gardener Scheduler tries to find a valid seed with minimal distance to the shoot’s intended region. Distances are configured via ConfigMap(s), usually per cloud provider in a Gardener landscape. The configuration is structured like this:

  • It refers to one or multiple CloudProfiles via annotation scheduling.gardener.cloud/cloudprofiles.
  • It contains the declaration as region-config via label scheduling.gardener.cloud/purpose.
  • If a CloudProfile is referred by multiple ConfigMaps, only the first one is considered.
  • The data fields configure actual distances, where key relates to the Shoot region and value contains distances to Seed regions.
apiVersion: v1
kind: ConfigMap
  name: <name>
  namespace: garden
    scheduling.gardener.cloud/cloudprofiles: cloudprofile-name-1{,optional-cloudprofile-name-2,...}
    scheduling.gardener.cloud/purpose: region-config
  region-1: |
    region-2: 10
    region-3: 20
  region-2: |
    region-1: 10
    region-3: 10

Gardener provider extensions for public cloud providers usually have an example weight ConfigMap in their repositories. We suggest to check them out before defining your own data.

If a valid seed candidate cannot be found after consulting the distance configuration, the scheduler will fall back to the Levenshtein distance to find the closest region. Therefore, the region name is split into a base name and an orientation. Possible orientations are north, south, east, west and central. The distance then is twice the Levenshtein distance of the region’s base name plus a correction value based on the orientation and the provider.

If the orientations of shoot and seed candidate match, the correction value is 0, if they differ it is 2 and if either the seed’s or the shoot’s region does not have an orientation it is 1. If the provider differs, the correction value is additionally incremented by 2.

Because of this, a matching region with a matching provider is always preferred.

Special handling based on shoot cluster purpose

Every shoot cluster can have a purpose that describes what the cluster is used for, and also influences how the cluster is setup (see Shoot Cluster Purpose for more information).

In case the shoot has the testing purpose, then the scheduler only reads the .spec.provider.type from the Shoot resource and tries to find a Seed that has the identical .spec.provider.type. The region does not matter, i.e., testing shoots may also be scheduled on a seed in a complete different region if it is better for balancing the whole Gardener system.

shoots/binding Subresource

The shoots/binding subresource is used to bind a Shoot to a Seed. On creation of a shoot cluster/s, the scheduler updates the binding automatically if an appropriate seed cluster is available. Only an operator with the necessary RBAC can update this binding manually. This can be done by changing the .spec.seedName of the shoot. However, if a different seed is already assigned to the shoot, this will trigger a control-plane migration. For required steps, please see Triggering the Migration.

spec.schedulerName Field in the Shoot Specification

Similar to the spec.schedulerName field in Pods, the Shoot specification has an optional .spec.schedulerName field. If this field is set on creation, only the scheduler which relates to the configured name is responsible for scheduling the shoot. The default-scheduler name is reserved for the default scheduler of Gardener. Affected Shoots will remain in Pending state if the mentioned scheduler is not present in the landscape.

spec.seedName Field in the Shoot Specification

Similar to the .spec.nodeName field in Pods, the Shoot specification has an optional .spec.seedName field. If this field is set on creation, the shoot will be scheduled to this seed. However, this field can only be set by users having RBAC for the shoots/binding subresource. If this field is not set, the scheduler will assign a suitable seed automatically and populate this field with the seed name.

seedSelector Field in the Shoot Specification

Similar to the .spec.nodeSelector field in Pods, the Shoot specification has an optional .spec.seedSelector field. It allows the user to provide a label selector that must match the labels of the Seeds in order to be scheduled to one of them. The labels on the Seeds are usually controlled by Gardener administrators/operators - end users cannot add arbitrary labels themselves. If provided, the Gardener Scheduler will only consider as “suitable” those seeds whose labels match those provided in the .spec.seedSelector of the Shoot.

By default, only seeds with the same provider as the shoot are selected. By adding a providerTypes field to the seedSelector, a dedicated set of possible providers (* means all provider types) can be selected.

Ensuring a Seed’s Capacity for Shoots Is Not Exceeded

Seeds have a practical limit of how many shoots they can accommodate. Exceeding this limit is undesirable, as the system performance will be noticeably impacted. Therefore, the scheduler ensures that a seed’s capacity for shoots is not exceeded by taking into account a maximum number of shoots that can be scheduled onto a seed.

This mechanism works as follows:

  • The gardenlet is configured with certain resources and their total capacity (and, for certain resources, the amount reserved for Gardener), see /example/20-componentconfig-gardenlet.yaml. Currently, the only such resource is the maximum number of shoots that can be scheduled onto a seed.
  • The gardenlet seed controller updates the capacity and allocatable fields in the Seed status with the capacity of each resource and how much of it is actually available to be consumed by shoots. The allocatable value of a resource is equal to capacity minus reserved.
  • When scheduling shoots, the scheduler filters out all candidate seeds whose allocatable capacity for shoots would be exceeded if the shoot is scheduled onto the seed.

Failure to Determine a Suitable Seed

In case the scheduler fails to find a suitable seed, the operation is being retried with exponential backoff. The reason for the failure will be reported in the Shoot’s .status.lastOperation field as well as a Kubernetes event (which can be retrieved via kubectl -n <namespace> describe shoot <shoot-name>).

Current Limitation / Future Plans

  • Azure unfortunately has a geographically non-hierarchical naming pattern and does not start with the continent. This is the reason why we will exchange the implementation of the MinimalDistance strategy with a more suitable one in the future.