Policies

• 1: Limit Ranges
• 2: Resource Quotas
• 3: Process ID Limits And Reservations
• 4: Node Resource Managers

1 - Limit Ranges

By default, containers run with unbounded compute resources on a Kubernetes cluster. Using Kubernetes resource quotas, administrators (also termed cluster operators) can restrict consumption and creation of cluster resources (such as CPU time, memory, and persistent storage) within a specified namespace. Within a namespace, a Pod can consume as much CPU and memory as is allowed by the ResourceQuotas that apply to that namespace. As a cluster operator, or as a namespace-level administrator, you might also be concerned about making sure that a single object cannot monopolize all available resources within a namespace.

A LimitRange is a policy to constrain the resource allocations (limits and requests) that you can specify for each applicable object kind (such as Pod or PersistentVolumeClaim) in a namespace.

A LimitRange provides constraints that can:

• Enforce minimum and maximum compute resources usage per Pod or Container in a namespace.
• Enforce minimum and maximum storage request per PersistentVolumeClaim in a namespace.
• Enforce a ratio between request and limit for a resource in a namespace.
• Set default request/limit for compute resources in a namespace and automatically inject them to Containers at runtime.

Kubernetes constrains resource allocations to Pods in a particular namespace whenever there is at least one LimitRange object in that namespace.

The name of a LimitRange object must be a valid DNS subdomain name.

Constraints on resource limits and requests

• The administrator creates a LimitRange in a namespace.
• Users create (or try to create) objects in that namespace, such as Pods or PersistentVolumeClaims.
• First, the LimitRange admission controller applies default request and limit values for all Pods (and their containers) that do not set compute resource requirements.
• Second, the LimitRange tracks usage to ensure it does not exceed resource minimum, maximum and ratio defined in any LimitRange present in the namespace.
• If you attempt to create or update an object (Pod or PersistentVolumeClaim) that violates a LimitRange constraint, your request to the API server will fail with anHTTP status code 403 Forbidden and a message explaining the constraint that has been violated.
• If you add a LimitRange in a namespace that applies to compute-related resources such as cpu and memory, you must specify requests or limits for those values. Otherwise, the system may reject Pod creation.
• LimitRange validations occur only at Pod admission stage, not on running Pods. If you add or modify a LimitRange, the Pods that already exist in that namespace continue unchanged.
• If two or more LimitRange objects exist in the namespace, it is not deterministic which default value will be applied.

LimitRange and admission checks for Pods

A LimitRange does not check the consistency of the default values it applies. This means that a default value for the limit that is set by LimitRange may be less than the request value specified for the container in the spec that a client submits to the API server. If that happens, the final Pod will not be schedulable.

For example, you define a LimitRange with below manifest:

concepts/policy/limit-range/problematic-limit-range.yaml

apiVersion: v1
kind: LimitRange
metadata:
  name: cpu-resource-constraint
spec:
  limits:
  - default: # this section defines default limits
      cpu: 500m
    defaultRequest: # this section defines default requests
      cpu: 500m
    max: # max and min define the limit range
      cpu: "1"
    min:
      cpu: 100m
    type: Container

along with a Pod that declares a CPU resource request of 700m, but not a limit:

concepts/policy/limit-range/example-conflict-with-limitrange-cpu.yaml

apiVersion: v1
kind: Pod
metadata:
  name: example-conflict-with-limitrange-cpu
spec:
  containers:
  - name: demo
    image: registry.k8s.io/pause:3.8
    resources:
      requests:
        cpu: 700m

then that Pod will not be scheduled, failing with an error similar to:

Pod "example-conflict-with-limitrange-cpu" is invalid: spec.containers[0].resources.requests: Invalid value: "700m": must be less than or equal to cpu limit

If you set both request and limit, then that new Pod will be scheduled successfully even with the same LimitRange in place:

concepts/policy/limit-range/example-no-conflict-with-limitrange-cpu.yaml

apiVersion: v1
kind: Pod
metadata:
  name: example-no-conflict-with-limitrange-cpu
spec:
  containers:
  - name: demo
    image: registry.k8s.io/pause:3.8
    resources:
      requests:
        cpu: 700m
      limits:
        cpu: 700m

Example resource constraints

Examples of policies that could be created using LimitRange are:

• In a 2 node cluster with a capacity of 8 GiB RAM and 16 cores, constrain Pods in a namespace to request 100m of CPU with a max limit of 500m for CPU and request 200Mi for Memory with a max limit of 600Mi for Memory.
• Define default CPU limit and request to 150m and memory default request to 300Mi for Containers started with no cpu and memory requests in their specs.

In the case where the total limits of the namespace is less than the sum of the limits of the Pods/Containers, there may be contention for resources. In this case, the Containers or Pods will not be created.

Neither contention nor changes to a LimitRange will affect already created resources.

What's next

For examples on using limits, see:

• how to configure minimum and maximum CPU constraints per namespace.
• how to configure minimum and maximum Memory constraints per namespace.
• how to configure default CPU Requests and Limits per namespace.
• how to configure default Memory Requests and Limits per namespace.
• how to configure minimum and maximum Storage consumption per namespace.
• a detailed example on configuring quota per namespace.

Refer to the LimitRanger design document for context and historical information.

2 - Resource Quotas

When several users or teams share a cluster with a fixed number of nodes, there is a concern that one team could use more than its fair share of resources.

Resource quotas are a tool for administrators to address this concern.

A resource quota, defined by a ResourceQuota object, provides constraints that limit aggregate resource consumption per namespace. It can limit the quantity of objects that can be created in a namespace by type, as well as the total amount of compute resources that may be consumed by resources in that namespace.

Resource quotas work like this:

• Different teams work in different namespaces. This can be enforced with RBAC.

• The administrator creates one ResourceQuota for each namespace.

• Users create resources (pods, services, etc.) in the namespace, and the quota system tracks usage to ensure it does not exceed hard resource limits defined in a ResourceQuota.

• If creating or updating a resource violates a quota constraint, the request will fail with HTTP status code 403 FORBIDDEN with a message explaining the constraint that would have been violated.

• If quota is enabled in a namespace for compute resources like cpu and memory, users must specify requests or limits for those values; otherwise, the quota system may reject pod creation. Hint: Use the LimitRanger admission controller to force defaults for pods that make no compute resource requirements.

  See the walkthrough for an example of how to avoid this problem.

The name of a ResourceQuota object must be a valid DNS subdomain name.

Examples of policies that could be created using namespaces and quotas are:

• In a cluster with a capacity of 32 GiB RAM, and 16 cores, let team A use 20 GiB and 10 cores, let B use 10GiB and 4 cores, and hold 2GiB and 2 cores in reserve for future allocation.
• Limit the "testing" namespace to using 1 core and 1GiB RAM. Let the "production" namespace use any amount.

In the case where the total capacity of the cluster is less than the sum of the quotas of the namespaces, there may be contention for resources. This is handled on a first-come-first-served basis.

Neither contention nor changes to quota will affect already created resources.

Enabling Resource Quota

ResourceQuota support is enabled by default for many Kubernetes distributions. It is enabled when the API server --enable-admission-plugins= flag has ResourceQuota as one of its arguments.

A resource quota is enforced in a particular namespace when there is a ResourceQuota in that namespace.

Compute Resource Quota

You can limit the total sum of compute resources that can be requested in a given namespace.

The following resource types are supported:

Resource Quota For Extended Resources

In addition to the resources mentioned above, in release 1.10, quota support for extended resources is added.

As overcommit is not allowed for extended resources, it makes no sense to specify both requests and limits for the same extended resource in a quota. So for extended resources, only quota items with prefix requests. are allowed.

Take the GPU resource as an example, if the resource name is nvidia.com/gpu, and you want to limit the total number of GPUs requested in a namespace to 4, you can define a quota as follows:

• requests.nvidia.com/gpu: 4

See Viewing and Setting Quotas for more details.

Storage Resource Quota

You can limit the total sum of storage resources that can be requested in a given namespace.

In addition, you can limit consumption of storage resources based on associated storage-class.

For example, if you want to quota storage with gold StorageClass separate from a bronze StorageClass, you can define a quota as follows:

• gold.storageclass.storage.k8s.io/requests.storage: 500Gi
• bronze.storageclass.storage.k8s.io/requests.storage: 100Gi

In release 1.8, quota support for local ephemeral storage is added as an alpha feature:

Object Count Quota

You can set quota for the total number of one particular resource kind in the Kubernetes API, using the following syntax:

• count/<resource>.<group> for resources from non-core groups
• count/<resource> for resources from the core group

Here is an example set of resources users may want to put under object count quota:

• count/persistentvolumeclaims
• count/services
• count/secrets
• count/configmaps
• count/replicationcontrollers
• count/deployments.apps
• count/replicasets.apps
• count/statefulsets.apps
• count/jobs.batch
• count/cronjobs.batch

If you define a quota this way, it applies to Kubernetes' APIs that are part of the API server, and to any custom resources backed by a CustomResourceDefinition. If you use API aggregation to add additional, custom APIs that are not defined as CustomResourceDefinitions, the core Kubernetes control plane does not enforce quota for the aggregated API. The extension API server is expected to provide quota enforcement if that's appropriate for the custom API. For example, to create a quota on a widgets custom resource in the example.com API group, use count/widgets.example.com.

When using such a resource quota (nearly for all object kinds), an object is charged against the quota if the object kind exists (is defined) in the control plane. These types of quotas are useful to protect against exhaustion of storage resources. For example, you may want to limit the number of Secrets in a server given their large size. Too many Secrets in a cluster can actually prevent servers and controllers from starting. You can set a quota for Jobs to protect against a poorly configured CronJob. CronJobs that create too many Jobs in a namespace can lead to a denial of service.

There is another syntax only to set the same type of quota for certain resources. The following types are supported:

For example, pods quota counts and enforces a maximum on the number of pods created in a single namespace that are not terminal. You might want to set a pods quota on a namespace to avoid the case where a user creates many small pods and exhausts the cluster's supply of Pod IPs.

You can find more examples on Viewing and Setting Quotas.

Quota Scopes

Each quota can have an associated set of scopes. A quota will only measure usage for a resource if it matches the intersection of enumerated scopes.

When a scope is added to the quota, it limits the number of resources it supports to those that pertain to the scope. Resources specified on the quota outside of the allowed set results in a validation error.

The BestEffort scope restricts a quota to tracking the following resource:

• pods

The Terminating, NotTerminating, NotBestEffort and PriorityClass scopes restrict a quota to tracking the following resources:

• pods
• cpu
• memory
• requests.cpu
• requests.memory
• limits.cpu
• limits.memory

Note that you cannot specify both the Terminating and the NotTerminating scopes in the same quota, and you cannot specify both the BestEffort and NotBestEffort scopes in the same quota either.

The scopeSelector supports the following values in the operator field:

• In
• NotIn
• Exists
• DoesNotExist

When using one of the following values as the scopeName when defining the scopeSelector, the operator must be Exists.

• Terminating
• NotTerminating
• BestEffort
• NotBestEffort

If the operator is In or NotIn, the values field must have at least one value. For example:

  scopeSelector:
    matchExpressions:
      - scopeName: PriorityClass
        operator: In
        values:
          - middle

If the operator is Exists or DoesNotExist, the values field must NOT be specified.

Resource Quota Per PriorityClass

Pods can be created at a specific priority. You can control a pod's consumption of system resources based on a pod's priority, by using the scopeSelector field in the quota spec.

A quota is matched and consumed only if scopeSelector in the quota spec selects the pod.

When quota is scoped for priority class using scopeSelector field, quota object is restricted to track only following resources:

• pods
• cpu
• memory
• ephemeral-storage
• limits.cpu
• limits.memory
• limits.ephemeral-storage
• requests.cpu
• requests.memory
• requests.ephemeral-storage

This example creates a quota object and matches it with pods at specific priorities. The example works as follows:

• Pods in the cluster have one of the three priority classes, "low", "medium", "high".
• One quota object is created for each priority.

Save the following YAML to a file quota.yml.

apiVersion: v1
kind: List
items:
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-high
  spec:
    hard:
      cpu: "1000"
      memory: 200Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["high"]
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-medium
  spec:
    hard:
      cpu: "10"
      memory: 20Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["medium"]
- apiVersion: v1
  kind: ResourceQuota
  metadata:
    name: pods-low
  spec:
    hard:
      cpu: "5"
      memory: 10Gi
      pods: "10"
    scopeSelector:
      matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["low"]

Apply the YAML using kubectl create.

kubectl create -f ./quota.yml

resourcequota/pods-high created
resourcequota/pods-medium created
resourcequota/pods-low created

Verify that Used quota is 0 using kubectl describe quota.

kubectl describe quota

Name:       pods-high
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     1k
memory      0     200Gi
pods        0     10


Name:       pods-low
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     5
memory      0     10Gi
pods        0     10


Name:       pods-medium
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     10
memory      0     20Gi
pods        0     10

Create a pod with priority "high". Save the following YAML to a file high-priority-pod.yml.

apiVersion: v1
kind: Pod
metadata:
  name: high-priority
spec:
  containers:
  - name: high-priority
    image: ubuntu
    command: ["/bin/sh"]
    args: ["-c", "while true; do echo hello; sleep 10;done"]
    resources:
      requests:
        memory: "10Gi"
        cpu: "500m"
      limits:
        memory: "10Gi"
        cpu: "500m"
  priorityClassName: high

Apply it with kubectl create.

kubectl create -f ./high-priority-pod.yml

Verify that "Used" stats for "high" priority quota, pods-high, has changed and that the other two quotas are unchanged.

kubectl describe quota

Name:       pods-high
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         500m  1k
memory      10Gi  200Gi
pods        1     10


Name:       pods-low
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     5
memory      0     10Gi
pods        0     10


Name:       pods-medium
Namespace:  default
Resource    Used  Hard
--------    ----  ----
cpu         0     10
memory      0     20Gi
pods        0     10

Cross-namespace Pod Affinity Quota

Operators can use CrossNamespacePodAffinity quota scope to limit which namespaces are allowed to have pods with affinity terms that cross namespaces. Specifically, it controls which pods are allowed to set namespaces or namespaceSelector fields in pod affinity terms.

Preventing users from using cross-namespace affinity terms might be desired since a pod with anti-affinity constraints can block pods from all other namespaces from getting scheduled in a failure domain.

Using this scope operators can prevent certain namespaces (foo-ns in the example below) from having pods that use cross-namespace pod affinity by creating a resource quota object in that namespace with CrossNamespacePodAffinity scope and hard limit of 0:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: disable-cross-namespace-affinity
  namespace: foo-ns
spec:
  hard:
    pods: "0"
  scopeSelector:
    matchExpressions:
    - scopeName: CrossNamespacePodAffinity
      operator: Exists

If operators want to disallow using namespaces and namespaceSelector by default, and only allow it for specific namespaces, they could configure CrossNamespacePodAffinity as a limited resource by setting the kube-apiserver flag --admission-control-config-file to the path of the following configuration file:

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: "ResourceQuota"
  configuration:
    apiVersion: apiserver.config.k8s.io/v1
    kind: ResourceQuotaConfiguration
    limitedResources:
    - resource: pods
      matchScopes:
      - scopeName: CrossNamespacePodAffinity
        operator: Exists

With the above configuration, pods can use namespaces and namespaceSelector in pod affinity only if the namespace where they are created have a resource quota object with CrossNamespacePodAffinity scope and a hard limit greater than or equal to the number of pods using those fields.

Requests compared to Limits

When allocating compute resources, each container may specify a request and a limit value for either CPU or memory. The quota can be configured to quota either value.

If the quota has a value specified for requests.cpu or requests.memory, then it requires that every incoming container makes an explicit request for those resources. If the quota has a value specified for limits.cpu or limits.memory, then it requires that every incoming container specifies an explicit limit for those resources.

Viewing and Setting Quotas

kubectl supports creating, updating, and viewing quotas:

kubectl create namespace myspace

cat <<EOF > compute-resources.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: compute-resources
spec:
  hard:
    requests.cpu: "1"
    requests.memory: 1Gi
    limits.cpu: "2"
    limits.memory: 2Gi
    requests.nvidia.com/gpu: 4
EOF

kubectl create -f ./compute-resources.yaml --namespace=myspace

cat <<EOF > object-counts.yaml
apiVersion: v1
kind: ResourceQuota
metadata:
  name: object-counts
spec:
  hard:
    configmaps: "10"
    persistentvolumeclaims: "4"
    pods: "4"
    replicationcontrollers: "20"
    secrets: "10"
    services: "10"
    services.loadbalancers: "2"
EOF

kubectl create -f ./object-counts.yaml --namespace=myspace

kubectl get quota --namespace=myspace

NAME                    AGE
compute-resources       30s
object-counts           32s

kubectl describe quota compute-resources --namespace=myspace

Name:                    compute-resources
Namespace:               myspace
Resource                 Used  Hard
--------                 ----  ----
limits.cpu               0     2
limits.memory            0     2Gi
requests.cpu             0     1
requests.memory          0     1Gi
requests.nvidia.com/gpu  0     4

kubectl describe quota object-counts --namespace=myspace

Name:                   object-counts
Namespace:              myspace
Resource                Used    Hard
--------                ----    ----
configmaps              0       10
persistentvolumeclaims  0       4
pods                    0       4
replicationcontrollers  0       20
secrets                 1       10
services                0       10
services.loadbalancers  0       2

kubectl also supports object count quota for all standard namespaced resources using the syntax count/<resource>.<group>:

kubectl create namespace myspace

kubectl create quota test --hard=count/deployments.apps=2,count/replicasets.apps=4,count/pods=3,count/secrets=4 --namespace=myspace

kubectl create deployment nginx --image=nginx --namespace=myspace --replicas=2

kubectl describe quota --namespace=myspace

Name:                         test
Namespace:                    myspace
Resource                      Used  Hard
--------                      ----  ----
count/deployments.apps        1     2
count/pods                    2     3
count/replicasets.apps        1     4
count/secrets                 1     4

Quota and Cluster Capacity

ResourceQuotas are independent of the cluster capacity. They are expressed in absolute units. So, if you add nodes to your cluster, this does not automatically give each namespace the ability to consume more resources.

Sometimes more complex policies may be desired, such as:

• Proportionally divide total cluster resources among several teams.
• Allow each tenant to grow resource usage as needed, but have a generous limit to prevent accidental resource exhaustion.
• Detect demand from one namespace, add nodes, and increase quota.

Such policies could be implemented using ResourceQuotas as building blocks, by writing a "controller" that watches the quota usage and adjusts the quota hard limits of each namespace according to other signals.

Note that resource quota divides up aggregate cluster resources, but it creates no restrictions around nodes: pods from several namespaces may run on the same node.

Limit Priority Class consumption by default

It may be desired that pods at a particular priority, such as "cluster-services", should be allowed in a namespace, if and only if, a matching quota object exists.

With this mechanism, operators are able to restrict usage of certain high priority classes to a limited number of namespaces and not every namespace will be able to consume these priority classes by default.

To enforce this, kube-apiserver flag --admission-control-config-file should be used to pass path to the following configuration file:

apiVersion: apiserver.config.k8s.io/v1
kind: AdmissionConfiguration
plugins:
- name: "ResourceQuota"
  configuration:
    apiVersion: apiserver.config.k8s.io/v1
    kind: ResourceQuotaConfiguration
    limitedResources:
    - resource: pods
      matchScopes:
      - scopeName: PriorityClass
        operator: In
        values: ["cluster-services"]

Then, create a resource quota object in the kube-system namespace:

policy/priority-class-resourcequota.yaml

apiVersion: v1
kind: ResourceQuota
metadata:
  name: pods-cluster-services
spec:
  scopeSelector:
    matchExpressions:
      - operator : In
        scopeName: PriorityClass
        values: ["cluster-services"]

kubectl apply -f https://k8s.io/examples/policy/priority-class-resourcequota.yaml -n kube-system

resourcequota/pods-cluster-services created

In this case, a pod creation will be allowed if:

1. the Pod's priorityClassName is not specified.
2. the Pod's priorityClassName is specified to a value other than cluster-services.
3. the Pod's priorityClassName is set to cluster-services, it is to be created in the kube-system namespace, and it has passed the resource quota check.

A Pod creation request is rejected if its priorityClassName is set to cluster-services and it is to be created in a namespace other than kube-system.

What's next

• See ResourceQuota design document for more information.
• See a detailed example for how to use resource quota.
• Read Quota support for priority class design document.
• See LimitedResources.

3 - Process ID Limits And Reservations

Kubernetes allow you to limit the number of process IDs (PIDs) that a Pod can use. You can also reserve a number of allocatable PIDs for each node for use by the operating system and daemons (rather than by Pods).

Process IDs (PIDs) are a fundamental resource on nodes. It is trivial to hit the task limit without hitting any other resource limits, which can then cause instability to a host machine.

Cluster administrators require mechanisms to ensure that Pods running in the cluster cannot induce PID exhaustion that prevents host daemons (such as the kubelet or kube-proxy, and potentially also the container runtime) from running. In addition, it is important to ensure that PIDs are limited among Pods in order to ensure they have limited impact on other workloads on the same node.

You can configure a kubelet to limit the number of PIDs a given Pod can consume. For example, if your node's host OS is set to use a maximum of 262144 PIDs and expect to host less than 250 Pods, one can give each Pod a budget of 1000 PIDs to prevent using up that node's overall number of available PIDs. If the admin wants to overcommit PIDs similar to CPU or memory, they may do so as well with some additional risks. Either way, a single Pod will not be able to bring the whole machine down. This kind of resource limiting helps to prevent simple fork bombs from affecting operation of an entire cluster.

Per-Pod PID limiting allows administrators to protect one Pod from another, but does not ensure that all Pods scheduled onto that host are unable to impact the node overall. Per-Pod limiting also does not protect the node agents themselves from PID exhaustion.

You can also reserve an amount of PIDs for node overhead, separate from the allocation to Pods. This is similar to how you can reserve CPU, memory, or other resources for use by the operating system and other facilities outside of Pods and their containers.

PID limiting is an important sibling to compute resource requests and limits. However, you specify it in a different way: rather than defining a Pod's resource limit in the .spec for a Pod, you configure the limit as a setting on the kubelet. Pod-defined PID limits are not currently supported.

Node PID limits

Kubernetes allows you to reserve a number of process IDs for the system use. To configure the reservation, use the parameter pid=<number> in the --system-reserved and --kube-reserved command line options to the kubelet. The value you specified declares that the specified number of process IDs will be reserved for the system as a whole and for Kubernetes system daemons respectively.

Pod PID limits

Kubernetes allows you to limit the number of processes running in a Pod. You specify this limit at the node level, rather than configuring it as a resource limit for a particular Pod. Each Node can have a different PID limit.
To configure the limit, you can specify the command line parameter --pod-max-pids to the kubelet, or set PodPidsLimit in the kubelet configuration file.

PID based eviction

You can configure kubelet to start terminating a Pod when it is misbehaving and consuming abnormal amount of resources. This feature is called eviction. You can Configure Out of Resource Handling for various eviction signals. Use pid.available eviction signal to configure the threshold for number of PIDs used by Pod. You can set soft and hard eviction policies. However, even with the hard eviction policy, if the number of PIDs growing very fast, node can still get into unstable state by hitting the node PIDs limit. Eviction signal value is calculated periodically and does NOT enforce the limit.

PID limiting - per Pod and per Node sets the hard limit. Once the limit is hit, workload will start experiencing failures when trying to get a new PID. It may or may not lead to rescheduling of a Pod, depending on how workload reacts on these failures and how liveness and readiness probes are configured for the Pod. However, if limits were set correctly, you can guarantee that other Pods workload and system processes will not run out of PIDs when one Pod is misbehaving.

What's next

• Refer to the PID Limiting enhancement document for more information.
• For historical context, read Process ID Limiting for Stability Improvements in Kubernetes 1.14.
• Read Managing Resources for Containers.
• Learn how to Configure Out of Resource Handling.

4 - Node Resource Managers

In order to support latency-critical and high-throughput workloads, Kubernetes offers a suite of Resource Managers. The managers aim to co-ordinate and optimise node's resources alignment for pods configured with a specific requirement for CPUs, devices, and memory (hugepages) resources.

Hardware topology alignment policies

Topology Manager is a kubelet component that aims to coordinate the set of components that are responsible for these optimizations. The the overall resource management process is governed using the policy you specify. To learn more, read Control Topology Management Policies on a Node.

Policies for assigning CPUs to Pods

Once a Pod is bound to a Node, the kubelet on that node may need to either multiplex the existing hardware (for example, sharing CPUs across multiple Pods) or allocate hardware by dedicating some resource (for example, assigning one of more CPUs for a Pod's exclusive use).

By default, the kubelet uses CFS quota to enforce pod CPU limits.  When the node runs many CPU-bound pods, the workload can move to different CPU cores depending on whether the pod is throttled and which CPU cores are available at scheduling time. Many workloads are not sensitive to this migration and thus work fine without any intervention.

However, in workloads where CPU cache affinity and scheduling latency significantly affect workload performance, the kubelet allows alternative CPU management policies to determine some placement preferences on the node. This is implemented using the CPU Manager and its policy. There are two available policies:

• none: the none policy explicitly enables the existing default CPU affinity scheme, providing no affinity beyond what the OS scheduler does automatically.  Limits on CPU usage for Guaranteed pods and Burstable pods are enforced using CFS quota.
• static: the static policy allows containers in Guaranteed pods with integer CPU requests access to exclusive CPUs on the node. This exclusivity is enforced using the cpuset cgroup controller.

CPU Manager doesn't support offlining and onlining of CPUs at runtime.

Static policy

The static policy enables finer-grained CPU management and exclusive CPU assignment. This policy manages a shared pool of CPUs that initially contains all CPUs in the node. The amount of exclusively allocatable CPUs is equal to the total number of CPUs in the node minus any CPU reservations set by the kubelet configuration. CPUs reserved by these options are taken, in integer quantity, from the initial shared pool in ascending order by physical core ID.  This shared pool is the set of CPUs on which any containers in BestEffort and Burstable pods run. Containers in Guaranteed pods with fractional CPU requests also run on CPUs in the shared pool. Only containers that are both part of a Guaranteed pod and have integer CPU requests are assigned exclusive CPUs.

As Guaranteed pods whose containers fit the requirements for being statically assigned are scheduled to the node, CPUs are removed from the shared pool and placed in the cpuset for the container. CFS quota is not used to bound the CPU usage of these containers as their usage is bound by the scheduling domain itself. In others words, the number of CPUs in the container cpuset is equal to the integer CPU limit specified in the pod spec. This static assignment increases CPU affinity and decreases context switches due to throttling for the CPU-bound workload.

Consider the containers in the following pod specs:

spec:
  containers:
  - name: nginx
    image: nginx

The pod above runs in the BestEffort QoS class because no resource requests or limits are specified. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
      requests:
        memory: "100Mi"

The pod above runs in the Burstable QoS class because resource requests do not equal limits and the cpu quantity is not specified. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
      requests:
        memory: "100Mi"
        cpu: "1"

The pod above runs in the Burstable QoS class because resource requests do not equal limits. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"
      requests:
        memory: "200Mi"
        cpu: "2"

The pod above runs in the Guaranteed QoS class because requests are equal to limits. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "1.5"
      requests:
        memory: "200Mi"
        cpu: "1.5"

The pod above runs in the Guaranteed QoS class because requests are equal to limits. But the container's resource limit for the CPU resource is a fraction. It runs in the shared pool.

spec:
  containers:
  - name: nginx
    image: nginx
    resources:
      limits:
        memory: "200Mi"
        cpu: "2"

The pod above runs in the Guaranteed QoS class because only limits are specified and requests are set equal to limits when not explicitly specified. And the container's resource limit for the CPU resource is an integer greater than or equal to one. The nginx container is granted 2 exclusive CPUs.

Static policy options

The behavior of the static policy can be fine-tuned using the CPU Manager policy options. The following policy options exist for the static CPU management policy: {{/* options in alphabetical order */}}

align-by-socket (alpha, hidden by default)

Align CPUs by physical package / socket boundary, rather than logical NUMA boundaries (available since Kubernetes v1.25) distribute-cpus-across-cores (alpha, hidden by default)

Allocate virtual cores, sometimes called hardware threads, across different physical cores (available since Kubernetes v1.31) distribute-cpus-across-numa (alpha, hidden by default)

Spread CPUs across different NUMA domains, aiming for an even balance between the selected domains (available since Kubernetes v1.23) full-pcpus-only (beta, visible by default)

Always allocate full physical cores (available since Kubernetes v1.22) strict-cpu-reservation (alpha, hidden by default)

Prevent all the pods regardless of their Quality of Service class to run on reserved CPUs (available since Kubernetes v1.32) prefer-align-cpus-by-uncorecache (alpha, hidden by default)

Align CPUs by uncore (Last-Level) cache boundary on a best-effort way (available since Kubernetes v1.32)

You can toggle groups of options on and off based upon their maturity level using the following feature gates:

• CPUManagerPolicyBetaOptions (default enabled). Disable to hide beta-level options.
• CPUManagerPolicyAlphaOptions (default disabled). Enable to show alpha-level options. You will still have to enable each option using the cpuManagerPolicyOptions field in the kubelet configuration file.

For more detail about the individual options you can configure, read on.

full-pcpus-only

If the full-pcpus-only policy option is specified, the static policy will always allocate full physical cores. By default, without this option, the static policy allocates CPUs using a topology-aware best-fit allocation. On SMT enabled systems, the policy can allocate individual virtual cores, which correspond to hardware threads. This can lead to different containers sharing the same physical cores; this behaviour in turn contributes to the noisy neighbours problem. With the option enabled, the pod will be admitted by the kubelet only if the CPU request of all its containers can be fulfilled by allocating full physical cores. If the pod does not pass the admission, it will be put in Failed state with the message SMTAlignmentError.

distribute-cpus-across-numa

If the distribute-cpus-across-numapolicy option is specified, the static policy will evenly distribute CPUs across NUMA nodes in cases where more than one NUMA node is required to satisfy the allocation. By default, the CPUManager will pack CPUs onto one NUMA node until it is filled, with any remaining CPUs simply spilling over to the next NUMA node. This can cause undesired bottlenecks in parallel code relying on barriers (and similar synchronization primitives), as this type of code tends to run only as fast as its slowest worker (which is slowed down by the fact that fewer CPUs are available on at least one NUMA node). By distributing CPUs evenly across NUMA nodes, application developers can more easily ensure that no single worker suffers from NUMA effects more than any other, improving the overall performance of these types of applications.

align-by-socket

If the align-by-socket policy option is specified, CPUs will be considered aligned at the socket boundary when deciding how to allocate CPUs to a container. By default, the CPUManager aligns CPU allocations at the NUMA boundary, which could result in performance degradation if CPUs need to be pulled from more than one NUMA node to satisfy the allocation. Although it tries to ensure that all CPUs are allocated from the minimum number of NUMA nodes, there is no guarantee that those NUMA nodes will be on the same socket. By directing the CPUManager to explicitly align CPUs at the socket boundary rather than the NUMA boundary, we are able to avoid such issues. Note, this policy option is not compatible with TopologyManager single-numa-node policy and does not apply to hardware where the number of sockets is greater than number of NUMA nodes.

distribute-cpus-across-cores

If the distribute-cpus-across-cores policy option is specified, the static policy will attempt to allocate virtual cores (hardware threads) across different physical cores. By default, the CPUManager tends to pack cpus onto as few physical cores as possible, which can lead to contention among cpus on the same physical core and result in performance bottlenecks. By enabling the distribute-cpus-across-cores policy, the static policy ensures that cpus are distributed across as many physical cores as possible, reducing the contention on the same physical core and thereby improving overall performance. However, it is important to note that this strategy might be less effective when the system is heavily loaded. Under such conditions, the benefit of reducing contention diminishes. Conversely, default behavior can help in reducing inter-core communication overhead, potentially providing better performance under high load conditions.

strict-cpu-reservation

The reservedSystemCPUs parameter in KubeletConfiguration, or the deprecated kubelet command line option --reserved-cpus, defines an explicit CPU set for OS system daemons and kubernetes system daemons. More details of this parameter can be found on the Explicitly Reserved CPU List page. By default this isolation is implemented only for guaranteed pods with integer CPU requests not for burstable and best-effort pods (and guaranteed pods with fractional CPU requests). Admission is only comparing the cpu requests against the allocatable cpus. Since the cpu limit is higher than the request, the default behaviour allows burstable and best-effort pods to use up the capacity of reservedSystemCPUs and cause host OS services to starve in real life deployments. If the strict-cpu-reservation policy option is enabled, the static policy will not allow any workload to use the CPU cores specified in reservedSystemCPUs.

prefer-align-cpus-by-uncorecache

If the prefer-align-cpus-by-uncorecache policy is specified, the static policy will allocate CPU resources for individual containers such that all CPUs assigned to a container share the same uncore cache block (also known as the Last-Level Cache or LLC). By default, the CPUManager will tightly pack CPU assignments which can result in containers being assigned CPUs from multiple uncore caches. This option enables the CPUManager to allocate CPUs in a way that maximizes the efficient use of the uncore cache. Allocation is performed on a best-effort basis, aiming to affine as many CPUs as possible within the same uncore cache. If the container's CPU requirement exceeds the CPU capacity of a single uncore cache, the CPUManager minimizes the number of uncore caches used in order to maintain optimal uncore cache alignment. Specific workloads can benefit in performance from the reduction of inter-cache latency and noisy neighbors at the cache level. If the CPUManager cannot align optimally while the node has sufficient resources, the container will still be admitted using the default packed behavior.

Memory Management Policies

The Kubernetes Memory Manager enables the feature of guaranteed memory (and hugepages) allocation for pods in the Guaranteed QoS class.

The Memory Manager employs hint generation protocol to yield the most suitable NUMA affinity for a pod. The Memory Manager feeds the central manager (Topology Manager) with these affinity hints. Based on both the hints and Topology Manager policy, the pod is rejected or admitted to the node.

Moreover, the Memory Manager ensures that the memory which a pod requests is allocated from a minimum number of NUMA nodes.

Other resource managers

The configuration of individual managers is elaborated in dedicated documents:

• Device Manager