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+# Granular Resource Limits in Node Autoscalers
+
+## Objective
+
+Node Autoscalers should allow setting more granular resource limits that would
+apply to arbitrary subsets of nodes, beyond the existing limiting mechanisms.
+
+## Background
+
+Cluster Autoscaler supports cluster-wide limits on resources (like total CPU and
+memory) and per-node-group node count limits. Karpenter supports
+setting [resource limits on a NodePool](https://karpenter.sh/docs/concepts/nodepools/#speclimits).
+Also, as mentioned
+in [AWS docs](https://docs.aws.amazon.com/eks/latest/best-practices/karpenter.html),
+cluster-wide limits are not supported too. This is not flexible enough for many
+use cases.
+
+Users often need to configure more granular limits. For instance, a user might
+want to limit the total resources consumed by nodes of a specific machine
+family, nodes with a particular OS, or nodes with specialized hardware like
+GPUs. The current resource limits implementations in both node autoscalers do
+not support these scenarios.
+
+This proposal introduces a new API to extend the Node Autoscalers’
+functionality, allowing limits to be applied to arbitrary sets of nodes.
+
+## Proposal: The AutoscalingResourceQuota API
+
+We propose a new Kubernetes custom resource, AutoscalingResourceQuota, to define
+resource limits on specific subsets of nodes. Node subsets are targeted using
+standard Kubernetes label selectors, offering a flexible way to group nodes.
+
+A node's eligibility for provisioning operation will be checked against all
+AutoscalingResourceQuota objects that select it. The operation will only be
+permitted if it does not violate any of the applicable limits. This should be
+compatible with the existing limiting mechanisms, i.e. CAS’ cluster-wide limits
+and Karpenter’s NodePool limits. Therefore, if the operation doesn’t violate
+AutoscalingResourceQuota, but violates existing limiting mechanisms, it should
+be rejected.
+
+### API Specification
+
+An AutoscalingResourceQuota object would look as follows:
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: example-resource-quota
+spec:
+  selector:
+    matchLabels:
+      example.cloud.com/machine-family: e2
+  limits:
+    resources:
+      cpu: 64
+      memory: 256Gi
+```
+
+* `selector`: A standard Kubernetes label selector that determines which nodes
+  the limits apply to. This allows for fine-grained control based on any label
+  present on the nodes, such as zone, region, OS, machine family, or custom
+  user-defined labels.
+* `limits`: Defines the limits of summed up resources of the selected nodes.
+
+This approach is highly flexible – adding a new dimension for limits only
+requires ensuring the nodes are labeled appropriately, with no code changes
+needed in the autoscaler.
+
+### Node as a Resource
+
+The AutoscalingResourceQuota API can be naturally extended to treat the number
+of nodes itself as a limitable resource, as shown in one of the examples below.
+
+### AutoscalingResourceQuota Status
+
+For better observability, the AutoscalingResourceQuota resource could be
+enhanced with a status field. This field, updated by a controller, would display
+the current resource usage for the selected nodes, allowing users to quickly
+check usage against the defined limits via kubectl describe. The controller can
+run in a separate thread as a part of the node autoscaler component.
+
+An example of the status field:
+
+```yaml
+status:
+  usage:
+    cpu: 32
+    memory: 128Gi
+    nodes: 50
+```
+
+## Alternatives considered
+
+### Minimum limits support
+
+The initial design, besides the maximum limits, also included minimum limits.
+Minimum limits were supposed to affect the node consolidation in the node
+autoscalers. A consolidation would be allowed only if removing the node wouldn’t
+violate any minimum limits. Cluster-wide minimum limits are implemented in CAS
+together with the maximum limits, so at first, it seemed logical to include both
+limit directions in the design.
+
+Despite being conceptually similar, minimum and maximum limits cover completely
+different use cases. Maximum limits can be used to control the cloud provider
+costs, to limit scaling certain types of compute, or to control distribution of
+compute resources between teams working on the same cluster. Minimum limits’
+main use case is ensuring a baseline capacity for users’ workloads, for example
+to handle sudden spikes in traffic. However, minimum limits defined as a minimum
+amount of resources in the cluster or a subset of nodes do not guarantee that
+the workloads will be schedulable on those resources. For example, two nodes
+with 2 CPUs each satisfy the minimum limit of 4 CPUs. If a user created a
+workload requesting 2 CPUs, that workload would not fit into existing nodes,
+making the baseline capacity effectively useless. This scenario will be better
+handled by
+the [CapacityBuffer API](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/proposals/buffers.md),
+which allows the user to provide an exact shape of their workloads, including
+the resource requests. In our example, the user would create a CapacityBuffer
+with a pod template requesting 2 CPUs. Such a CapacityBuffer would ensure that a
+pod with that shape is always schedulable on the existing nodes.
+
+Therefore, we decided to remove minimum limits from the design of granular
+limits, as CapacityBuffers are a better way to provide a baseline capacity for
+user workloads.
+
+### Kubernetes LimitRange and ResourceQuota
+
+It has been discussed whether the same result could be accomplished by using the
+standard Kubernetes
+resources: [LimitRange](https://kubernetes.io/docs/concepts/policy/limit-range/)
+and [ResourceQuota](https://kubernetes.io/docs/concepts/policy/resource-quotas/).
+
+LimitRange is a resource used to configure minimum and maximum resource
+constraints for a namespace. For example, it can define the default CPU and
+memory requests for pods and containers within a namespace, or enforce a minimum
+and maximum CPU request for a pod. However, its scope is limited to a single
+resource, meaning that it doesn’t look at all pods in the namespace, but just
+looks if the pod requests and limits are within defined bounds.
+
+ResourceQuota allows to define and limit the aggregate resource consumption per
+namespace. This includes limiting the total CPU, memory, and storage that all
+pods and persistent volume claims within a namespace can request or consume. It
+also supports limiting the count of various Kubernetes objects, such as pods,
+services, and replication controllers. While resource quotas can be used to
+limit the resources provisioned by the CA to some degree, it’s not possible to
+guarantee that CA won’t scale up above the defined limit. Since the quotas
+operate on pod requests, and CA does not guarantee that bin packing will yield
+the optimal result, setting the quota to e.g. 64 CPUs does not mean that CA will
+stop scaling at 64 CPUs.
+
+Moreover, both of those resources are namespaced, so their scope is limited to
+the namespace in which they are defined, while the nodes are global. We can’t
+use namespaced resources to limit the creation and deletion of global resources.
+
+## User Stories
+
+### Story 1
+
+As a cluster administrator, I want to configure cluster-wide resource limits to
+avoid excessive cloud provider costs.
+
+**Note:** This is already supported in CAS, but not in Karpenter.
+
+Example AutoscalingResourceQuota:
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: cluster-wide-limits
+spec:
+  limits:
+    resources:
+      cpu: 128
+      memory: 256Gi
+```
+
+### Story 2
+
+As a cluster administrator, I want to configure separate resource limits for
+specific groups of nodes on top of cluster-wide limits, to avoid a situation
+where one group of nodes starves others of resources.
+
+**Note:** A specific group of nodes can be either a NodePool in Karpenter, a
+ComputeClass in GKE, or simply a set of nodes grouped by a user-defined label.
+This can be useful e.g. for organizations where multiple teams are running
+workloads in a shared cluster, and these teams have separate sets of nodes. This
+way, a cluster administrator can ensure that each team has a proper limit for
+their resources and it doesn’t starve other teams. This story is partly
+supported by Karpenter’s NodePool limits.
+
+Example AutoscalingResourceQuota:
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: team-a-limits
+spec:
+  selector:
+    matchLabels:
+      team: a
+  limits:
+    resources:
+      cpu: 32
+```
+
+### Story 3
+
+As a cluster administrator, I want to allow scaling up machines that are more
+expensive or less suitable for my workloads when better machines are
+unavailable, but I want to limit how many of them can be created, so that I can
+control extra cloud provider costs, or limit the impact of using non-optimal
+machine for my workloads.
+
+Example AutoscalingResourceQuota:
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: max-e2-resources
+spec:
+  selector:
+    matchLabels:
+      example.cloud.com/machine-family: e2
+  limits:
+    resources:
+      cpu: 32
+      memory: 64Gi
+```
+
+### Story 4
+
+As a cluster administrator, I want to limit the number of nodes in a specific
+zone if my cluster is unbalanced for any reason, so that I can avoid exhausting
+IP space in that zone, or enforce better balancing across zones.
+
+**Note:** Originally requested
+in [https://github.com/kubernetes/autoscaler/issues/6940](https://github.com/kubernetes/autoscaler/issues/6940).
+
+Example AutoscalingResourceQuota:
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: max-nodes-us-central1-b
+spec:
+  selector:
+    matchLabels:
+      topology.kubernetes.io/zone: us-central1-b
+  limits:
+    resources:
+      nodes: 64
+```
+
+### Story 5 (obsolete)
+
+As a cluster administrator, I want to ensure there is always a baseline capacity
+in my cluster or specific parts of my cluster below which the node autoscaler
+won’t consolidate the nodes, so that my workloads can quickly react to sudden
+spikes in traffic.
+
+This user story is obsolete. CapacityBuffer API covers this use case in a more
+flexible way.
+
+## Other AutoscalingResourceQuota examples
+
+The following examples illustrate the flexibility of the proposed API and
+demonstrate other possible use cases not described in the user stories.
+
+#### **Maximum Windows Nodes**
+
+Limit the total number of nodes running the Windows operating system to 8.
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: max-windows-nodes
+spec:
+  selector:
+    matchLabels:
+      kubernetes.io/os: windows
+  limits:
+    resources:
+      nodes: 8
+```
+
+#### **Maximum NVIDIA T4 GPUs**
+
+Limit the total number of NVIDIA T4 GPUs in the cluster to 16.
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: max-t4-gpus
+spec:
+  selector:
+    matchLabels:
+      example.cloud.com/gpu-type: nvidia-t4
+  limits:
+    resources:
+      nvidia.com/gpu: 16
+```
+
+#### **Cluster-wide Limits Excluding Control Plane Nodes**
+
+Apply cluster-wide CPU and memory limits while excluding nodes with the
+control-plane role.
+
+```yaml
+apiVersion: autoscaling.x-k8s.io/v1beta1
+kind: AutoscalingResourceQuota
+metadata:
+  name: cluster-limits-no-control-plane
+spec:
+  selector:
+    matchExpressions:
+    - key: node-role.kubernetes.io/control-plane
+      operator: DoesNotExist
+  limits:
+    resources:
+      cpu: 64
+      memory: 128Gi
+```