How Does the Kubernetes Scheduler Make Decisions?

The Kubernetes scheduler is a main part of Kubernetes. It helps to decide where to place a new pod. This choice is very important. It helps to use resources better, keep things running well, and make sure everything is available in the Kubernetes cluster. The scheduler looks at different things. It checks the needs for resources, rules, and policies. This way, it can make good choices that fit the goals of the system.

In this article, we will talk about how the Kubernetes scheduler makes its decisions. We will look closely at its main parts and the methods it uses. We will also talk about quality of service classes, node affinity, anti-affinity, and how taints and tolerations work in scheduling. Furthermore, we will check the importance of resource requests and limits. We will give some real-life examples of scheduler decisions. Lastly, we will show how to change the Kubernetes scheduler with scheduling plugins.

How Does the Kubernetes Scheduler Make Decisions?
What Are the Main Parts of the Kubernetes Scheduler?
How Does the Scheduler Use Quality of Service Classes?
What Methods Does the Kubernetes Scheduler Use?
How Does the Scheduler Work with Node Affinity and Anti-Affinity?
How Are Taints and Tolerations Used in Decisions?
What Are Resource Requests and Limits in Scheduling?
Can You Share Real-Life Examples of Scheduler Choices?
How to Change the Kubernetes Scheduler with Scheduling Plugins?
Frequently Asked Questions

For more information about Kubernetes and what it can do, you can check these articles: What is Kubernetes and How Does it Simplify Container Management, How Does Kubernetes Scheduling Work, and What Are the Key Components of a Kubernetes Cluster.

What Are the Key Components of the Kubernetes Scheduler?

The Kubernetes Scheduler is an important part of the Kubernetes control plane. It helps to choose the right nodes for new pods. The main parts of the Scheduler are:

Scheduling Framework: This part lets the Scheduler use different scheduling methods and plugins. It allows us to customize based on what we need.
PodSpec: The Pod specification shows what we want the pod to be like. It includes requests for resources like CPU and memory. It also has node selectors, rules for affinity and anti-affinity, and tolerations. This information helps the Scheduler decide where to place the pods.
Node Information: The Scheduler gets details about available nodes. This includes their resource limits, current usage, and labels. This data helps us see which nodes can handle new pods.
Scheduling Queue: This queue keeps unscheduled pods while the Scheduler looks for suitable nodes. Pods in this queue get priority based on their importance and what they need.
Filter and Score Algorithms: The Scheduler uses a two-step process:
- Filtering: In this step, it removes nodes that do not fit the pod’s needs. For example, if there are not enough resources or if the affinity rules do not match.
- Scoring: The remaining nodes get scores based on how well they meet the pod’s needs. The node with the highest score gets picked for scheduling.
Admission Control: After picking a node, the Scheduler works with the Kubernetes API server. It updates the pod’s status and connects it to the chosen node.
Extenders: These are extra tools that let us add outside filtering and scoring rules into the scheduling process. They give us more options for customization.

Here is a simple example of a pod specification with resource requests and node affinity:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  containers:
  - name: my-container
    image: my-image
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: disktype
            operator: In
            values:
            - ssd

In this setup, the Kubernetes Scheduler only looks at nodes with the label disktype=ssd. It makes sure the selected node has at least 64Mi of memory and 250m of CPU available.

If we want to learn more about the Kubernetes Scheduler and its parts, we can check this article on Kubernetes scheduling.

How Does the Scheduler Utilize Quality of Service Classes?

We use Quality of Service (QoS) classes in the Kubernetes Scheduler to help decide which pods to run first. QoS helps us by sorting pods into three groups based on their resource needs.

Guaranteed: These pods have both requests and limits for all containers. This means they get exactly what they ask for.

apiVersion: v1
kind: Pod
metadata:
  name: guaranteed-pod
spec:
  containers:
  - name: app-container
    image: myapp:latest
    resources:
      requests:
        memory: "512Mi"
        cpu: "500m"
      limits:
        memory: "512Mi"
        cpu: "500m"

Burstable: These pods have requests and limits set, but the requests are lower than the limits. They can use more resources if there is enough, but they still have a basic guarantee.

apiVersion: v1
kind: Pod
metadata:
  name: burstable-pod
spec:
  containers:
  - name: app-container
    image: myapp:latest
    resources:
      requests:
        memory: "256Mi"
        cpu: "250m"
      limits:
        memory: "512Mi"
        cpu: "1"

BestEffort: These pods do not have any requests or limits. They get the lowest priority and will only run if there are free resources.

apiVersion: v1
kind: Pod
metadata:
  name: besteffort-pod
spec:
  containers:
  - name: app-container
    image: myapp:latest

When scheduling, the Kubernetes Scheduler checks the resource needs of each pod. Then it puts them on nodes based on their QoS class. Pods in the Guaranteed class get priority over Burstable and BestEffort pods. This helps keep the cluster running well, especially when resources are tight.

For more details on managing Kubernetes pods, we can check out this article.

What Algorithms Does the Kubernetes Scheduler Use?

The Kubernetes Scheduler uses different algorithms to make good scheduling choices. It looks at workload needs, resource availability, and any rules set by users. Here are the main algorithms we use:

Predicates: These functions help us filter nodes based on certain rules before we think about where to place pods. Some common predicates are:
- NodeCondition: This checks if the node is healthy (Ready, NotReady).
- PodFitsHostPorts: This makes sure that the host ports needed by the pod are free.
- PodFitsResources: This checks if the node has enough resources (CPU, memory) for the pod.
Priorities: After we filter nodes with predicates, we rank the remaining nodes using priority functions. Some common priority functions are:
- LeastRequestedPriority: This picks nodes that ask for the least resources.
- BalancedResourceAllocation: This tries to balance resource use across all nodes.
- NodeAffinityPriority: This chooses nodes that match the pod’s node affinity.
Scheduling Cycle: The scheduling process has these steps:
- Filtering: We filter nodes using predicates.
- Scoring: We score the remaining nodes with priority functions.
- Binding: The scheduler binds the pod to the chosen node.
Extender: For advanced needs, we can add external scheduling rules using Scheduler Extenders. This lets us apply custom filtering and scoring in the scheduling process.
Scheduling Framework: Added in Kubernetes 1.18, this framework lets developers add new features to the scheduling process with custom plugins. It supports:
- Filter Plugins: These are used in the filtering step.
- Score Plugins: These change how we score nodes.
- Bind Plugins: We can use this for custom binding rules.

Here is a sample setup to use a custom scheduling framework in the scheduler configuration file:

apiVersion: scheduling.k8s.io/v1
kind: Scheduler
metadata:
  name: custom-scheduler
spec:
  plugins:
    score:
      enabled:
        - name: MyCustomScorePlugin
    filter:
      enabled:
        - name: MyCustomFilterPlugin

By using these algorithms and frameworks, the Kubernetes Scheduler can place pods better, use resources well, and follow the rules set by users. For more details on how Kubernetes scheduling works, we can check out this article on how does Kubernetes scheduling work.

How Does the Scheduler Handle Node Affinity and Anti-Affinity?

We use the Kubernetes Scheduler to manage where pods run on nodes. It has rules called node affinity and anti-affinity. These rules help us decide how to place pods on nodes. This makes sure we use resources well and our applications work better.

Node Affinity

Node affinity means we have rules about which nodes a pod can run on. These rules depend on labels we give to the nodes. We write these rules in the pod definition under affinity. There are two main types of node affinity:

RequiredDuringSchedulingIgnoredDuringExecution: This rule says a pod can only run on nodes that match certain criteria. If there are no matching nodes, the pod will not run.
PreferredDuringSchedulingIgnoredDuringExecution: This rule is more about preference. The scheduler will try to place the pod on a matching node. But if there are no matches, it can go on other nodes.

Example of Node Affinity

Here is an example of how we can write node affinity in a pod definition:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: disktype
            operator: In
            values:
            - ssd
      preferredDuringSchedulingIgnoredDuringExecution:
      - weight: 1
        preference:
          matchExpressions:
          - key: region
            operator: In
            values:
            - us-west
  containers:
  - name: my-container
    image: my-image

Node Anti-Affinity

Node anti-affinity is different. It helps us set rules to stop pods from running on certain nodes. This is helpful for keeping high availability and fault tolerance. We also define this in the pod specification under affinity.

RequiredDuringSchedulingIgnoredDuringExecution: Pods cannot run on nodes that match certain criteria.
PreferredDuringSchedulingIgnoredDuringExecution: The scheduler will try not to place the pod on matching nodes but can do it if needed.

Example of Node Anti-Affinity

Here is an example of how we can write node anti-affinity:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: zone
            operator: NotIn
            values:
            - zone1
  containers:
  - name: my-container
    image: my-image

Summary of Key Features

Flexibility: Node affinity and anti-affinity give us strong tools to manage where pods go based on node labels.
High Availability: With anti-affinity, we can make sure that copies of a service run on different nodes. This lowers the chance of downtime.
Resource Optimization: Node affinity helps use resources better by making sure pods run on nodes that fit certain hardware needs.

Node affinity and anti-affinity are key parts of the Kubernetes scheduler. They help us control where pods go. This makes sure we use resources well and keep our applications reliable. For more details on Kubernetes scheduling, we can check this guide on how Kubernetes scheduling works.

How Are Taints and Tolerations Used in Scheduling Decisions?

Taints and tolerations are important tools in Kubernetes. They help us control which pods can be placed on which nodes. This way, we make sure that workloads go to the right environments.

Taints

A taint is a mark we put on a Kubernetes node. It stops pods from being placed on that node unless those pods have a matching toleration. Taints have three parts: key, value, and effect. The effect can be one of these:

NoSchedule: Pods that do not tolerate the taint will not be placed on the node.
PreferNoSchedule: Kubernetes will try not to place pods that do not tolerate the taint. But it can still do that if there are no other choices.
NoExecute: Pods that do not tolerate the taint will be removed from the node if they are already running.

Here is how we add a taint to a node:

kubectl taint nodes <node-name> key=value:NoSchedule

Tolerations

Tolerations go on pods. They let pods be placed on nodes that have matching taints. A toleration has a key, value, and effect that match the taint.

Here is an example of a pod with a toleration:

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
spec:
  tolerations:
  - key: "key"
    operator: "Equal"
    value: "value"
    effect: "NoSchedule"
  containers:
  - name: my-container
    image: my-image

How Taints and Tolerations Work Together

Preventing Scheduling: When a node has a taint, only the pods with a matching toleration can be placed on that node.
Eviction: If we add a taint with the NoExecute effect to a node, any existing pods without the matching toleration will be removed.
Multiple Taints: A node can have many taints, and a pod can have many tolerations. This gives us flexible ways to schedule.

By using taints and tolerations, we can manage node workloads better. We can make sure that certain applications run only in the right environments. This helps us manage our cluster and use resources well. If you want to learn more about Kubernetes scheduling, check out How Does Kubernetes Scheduling Work?.

What Role Do Resource Requests and Limits Play in Scheduling?

In Kubernetes, resource requests and limits play a big part in how the Kubernetes Scheduler makes decisions. They give us important details about what resources Pods need. This helps us make sure workloads go to the right nodes in the cluster.

Resource Requests

Definition: Resource requests tell us the least amount of CPU and memory a Pod needs to run.
Impact on Scheduling: When we create a Pod, the Scheduler looks at the resource requests. It finds a node that can meet or be better than these needs. If a node can’t meet the resource requests, the Pod won’t be scheduled there.

Here is an example of how to define resource requests in a Pod specification:

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  containers:
  - name: example-container
    image: nginx
    resources:
      requests:
        memory: "64Mi"
        cpu: "250m"

Resource Limits

Definition: Resource limits tell us the most CPU and memory a Pod can use.
Impact on Scheduling: Limits help stop a Pod from using all the resources on a node. If a Pod uses more than its limits, Kubernetes may reduce its resource usage. This can affect performance but not the scheduling directly.

Here is an example of how to define resource limits in a Pod specification:

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  containers:
  - name: example-container
    image: nginx
    resources:
      limits:
        memory: "128Mi"
        cpu: "500m"

Scheduling Decisions

The Scheduler looks at both requests and limits when it makes choices:
- It checks if the total resource requests of all Pods on a node are too much for that node.
- It makes sure that the Pod’s limits are not more than the resources the node can give after looking at other Pods.

This helps us to share resources fairly among Pods. It stops resource fights and makes sure that Pods have what they need to run well.

For more info about how Kubernetes scheduling works, you can check this article on how does Kubernetes scheduling work.

Can You Provide Real Life Examples of Scheduler Decisions?

Real life examples of Kubernetes Scheduler decisions show how it manages workloads well based on what resources are available. Here are some simple situations that explain how the Kubernetes Scheduler makes choices:

Pod Scheduling Based on Resource Requests:
When a Pod asks for certain resources like CPU and memory, the Scheduler checks the available nodes. For example, if a Pod needs 500m CPU and 256Mi memory, the Scheduler will only put it on nodes that have enough resources.

apiVersion: v1
kind: Pod
metadata:
  name: resource-request-pod
spec:
  containers:
    - name: my-app
      image: my-app-image
      resources:
        requests:
          cpu: "500m"
          memory: "256Mi"

Node Affinity Example:
If we need to put a workload on certain nodes, the Scheduler will look at this when scheduling. For example, if we want to put a Pod only on nodes that have the label zone=us-west1, we can set it up like this:

apiVersion: v1
kind: Pod
metadata:
  name: node-affinity-pod
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
          - matchExpressions:
              - key: zone
                operator: In
                values:
                  - us-west1
  containers:
    - name: my-app
      image: my-app-image

Handling Taints and Tolerations:
When we have nodes that are tainted, like to stop non-critical Pods from being scheduled, we can use tolerations in our Pods. For example, a node might be tainted with key=special, effect=NoSchedule. A Pod that can handle this would look like this:

apiVersion: v1
kind: Pod
metadata:
  name: toleration-pod
spec:
  tolerations:
    - key: "special"
      operator: "Equal"
      value: "true"
      effect: "NoSchedule"
  containers:
    - name: my-app
      image: my-app-image

Quality of Service (QoS) Classes:
The Scheduler uses QoS classes to give priority to Pods based on their resource requests and limits. For example, if we have important applications that need guaranteed resources, we can set them with specific resource requests and limits so they get scheduled first.

apiVersion: v1
kind: Pod
metadata:
  name: qos-guaranteed-pod
spec:
  containers:
    - name: my-app
      image: my-app-image
      resources:
        requests:
          cpu: "100m"
          memory: "128Mi"
        limits:
          cpu: "200m"
          memory: "256Mi"

Real-time Load Balancing:
In a production setup, the Scheduler can balance workloads based on current metrics. If a node is almost full, the Scheduler can place new Pods on nodes that have less load. This helps to use resources well across the cluster.

By using these methods, the Kubernetes Scheduler manages workloads effectively. It adjusts to the needs and limits of our applications. For more information about how Kubernetes scheduling works, you can check this article.

How to Customize the Kubernetes Scheduler with Scheduling Plugins?

Kubernetes lets us change how the scheduler works using scheduling plugins. We can use these plugins to meet our needs for where to place pods. Scheduling plugins help us change or add to the scheduling process. This way, we can fit our specific application needs.

Types of Scheduling Plugins

Filter Plugins: These plugins remove nodes that do not match specific rules.
Score Plugins: After filtering, these plugins give scores to the remaining nodes. This helps us find the best one.
Reserve Plugins: These plugins can hold resources on nodes for certain pods.
Bind Plugins: These plugins connect the pod to the chosen node.

Example of Custom Scheduler Configuration

To change the Kubernetes scheduler, we can make a custom configuration file. Here is an example that shows how to use scheduling plugins:

apiVersion: scheduling.k8s.io/v1
kind: Scheduler
metadata:
  name: custom-scheduler
spec:
  schedulerName: custom-scheduler
  plugins:
    score:
      enabled:
      - name: NodeResourcesLeastAllocated
      - name: NodePreferAvoidPods
    filter:
      enabled:
      - name: NodePorts
      - name: PodToleratesNodeTaints

Implementing a Custom Scheduler

Deploy a Custom Scheduler: We can deploy our custom scheduler using a Deployment manifest.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: custom-scheduler
spec:
  replicas: 1
  selector:
    matchLabels:
      app: custom-scheduler
  template:
    metadata:
      labels:
        app: custom-scheduler
    spec:
      containers:
      - name: kube-scheduler
        image: k8s.gcr.io/kube-scheduler:v1.23.0
        command:
        - kube-scheduler
        - --config=/etc/kubernetes/scheduler-config/scheduler-config.yaml
        volumeMounts:
        - name: config-volume
          mountPath: /etc/kubernetes/scheduler-config
      volumes:
      - name: config-volume
        configMap:
          name: scheduler-config

Bind Pods to the Custom Scheduler: We can make certain pods use our custom scheduler by setting the schedulerName field in the PodSpec.

apiVersion: v1
kind: Pod
metadata:
  name: my-app
spec:
  schedulerName: custom-scheduler
  containers:
  - name: my-app-container
    image: my-app-image

Testing and Verification

After we deploy the custom scheduler, we should watch the scheduling choices. We can check the logs of the scheduler pod.

kubectl logs -f deployment/custom-scheduler

References

For more information on Kubernetes scheduling and how to customize it, check the article How Does Kubernetes Scheduling Work.

Frequently Asked Questions

1. What is the role of the Kubernetes scheduler in resource allocation?

The Kubernetes scheduler decides which nodes in a cluster should run certain pods. It looks at what resources the pods need and any limits. The scheduler checks things like resource requests, node affinity, and some scheduling rules. This helps us use resources well and keep our applications running smoothly.

2. How does the Kubernetes scheduler handle pod scheduling conflicts?

When there are scheduling conflicts, the Kubernetes scheduler uses different methods to fix them. It can prioritize pods by their Quality of Service (QoS) classes. It also has preemption rules. This way, important applications get the resources they need while still keeping the system working well.

3. Can I customize the Kubernetes scheduler?

Yes, we can customize the Kubernetes scheduler. Kubernetes lets us change its scheduling logic with scheduling plugins. By using custom schedulers or the scheduling framework, we can change how scheduling works. This helps us meet specific needs for our applications or follow company rules.

4. How are taints and tolerations significant in Kubernetes scheduling?

Taints and tolerations are important in Kubernetes scheduling. They let nodes refuse certain pods but allow others to run. Taints stop pods from being scheduled on nodes unless the pods have the right tolerations. This gives us control over where pods go and how we manage resources in mixed environments.

5. What algorithms does the Kubernetes scheduler utilize for decision-making?

The Kubernetes scheduler uses different algorithms. Some examples are the “Least Requested” method and the “Node Affinity” algorithm. These help the scheduler look at nodes based on their resources and specific rules. This helps us make smart scheduling choices that use resources well while following our set limits.

For more info on how Kubernetes scheduling works and what it means for resource management, check our detailed article on how does Kubernetes scheduling work.