Controlling pod placement using the scheduler
- About the default scheduler
  - Understanding default scheduling
- Scheduler use cases

Controlling pod placement using the scheduler

Pod scheduling is an internal process that determines placement of new pods onto nodes within the cluster.

The scheduler code has a clean separation that watches new pods as they get created and identifies the most suitable node to host them. It then creates bindings (pod to node bindings) for the pods using the master API.

Default pod scheduling

OKD comes with a default scheduler that serves the needs of most users. The default scheduler uses both inherent and customization tools to determine the best fit for a pod.

Advanced pod scheduling

In situations where you might want more control over where new pods are placed, the OKD advanced scheduling features allow you to configure a pod so that the pod is required or has a preference to run on a particular node or alongside a specific pod.

You can control pod placement by using the following scheduling features:

About the default scheduler

The default OKD pod scheduler is responsible for determining the placement of new pods onto nodes within the cluster. It reads data from the pod and finds a node that is a good fit based on configured profiles. It is completely independent and exists as a standalone solution. It does not modify the pod; it creates a binding for the pod that ties the pod to the particular node.

Understanding default scheduling

The existing generic scheduler is the default platform-provided scheduler engine that selects a node to host the pod in a three-step operation:

Filters the nodes

The available nodes are filtered based on the constraints or requirements specified. This is done by running each node through the list of filter functions called predicates, or filters.

Prioritizes the filtered list of nodes

This is achieved by passing each node through a series of priority, or scoring, functions that assign it a score between 0 - 10, with 0 indicating a bad fit and 10 indicating a good fit to host the pod. The scheduler configuration can also take in a simple weight (positive numeric value) for each scoring function. The node score provided by each scoring function is multiplied by the weight (default weight for most scores is 1) and then combined by adding the scores for each node provided by all the scores. This weight attribute can be used by administrators to give higher importance to some scores.

Selects the best fit node

The nodes are sorted based on their scores and the node with the highest score is selected to host the pod. If multiple nodes have the same high score, then one of them is selected at random.

Scheduler use cases

One of the important use cases for scheduling within OKD is to support flexible affinity and anti-affinity policies.

Infrastructure topological levels

Administrators can define multiple topological levels for their infrastructure (nodes) by specifying labels on nodes. For example: region=r1, zone=z1, rack=s1.

These label names have no particular meaning and administrators are free to name their infrastructure levels anything, such as city/building/room. Also, administrators can define any number of levels for their infrastructure topology, with three levels usually being adequate (such as: regions → zones → racks). Administrators can specify affinity and anti-affinity rules at each of these levels in any combination.

Affinity

Administrators should be able to configure the scheduler to specify affinity at any topological level, or even at multiple levels. Affinity at a particular level indicates that all pods that belong to the same service are scheduled onto nodes that belong to the same level. This handles any latency requirements of applications by allowing administrators to ensure that peer pods do not end up being too geographically separated. If no node is available within the same affinity group to host the pod, then the pod is not scheduled.

If you need greater control over where the pods are scheduled, see Controlling pod placement on nodes using node affinity rules and Placing pods relative to other pods using affinity and anti-affinity rules.

These advanced scheduling features allow administrators to specify which node a pod can be scheduled on and to force or reject scheduling relative to other pods.

Anti-affinity

Administrators should be able to configure the scheduler to specify anti-affinity at any topological level, or even at multiple levels. Anti-affinity (or ‘spread’) at a particular level indicates that all pods that belong to the same service are spread across nodes that belong to that level. This ensures that the application is well spread for high availability purposes. The scheduler tries to balance the service pods across all applicable nodes as evenly as possible.