website/content/en/docs/concepts/scheduling-eviction/resource-bin-packing.md

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---
reviewers:
- bsalamat
- k82cn
- ahg-g
title: Resource Bin Packing
content_type: concept
weight: 80
---
<!-- overview -->
In the [scheduling-plugin](/docs/reference/scheduling/config/#scheduling-plugins) `NodeResourcesFit` of kube-scheduler, there are two
scoring strategies that support the bin packing of resources: `MostAllocated` and `RequestedToCapacityRatio`.
<!-- body -->
## Enabling bin packing using MostAllocated strategy
The `MostAllocated` strategy scores the nodes based on the utilization of resources, favoring the ones with higher allocation.
For each resource type, you can set a weight to modify its influence in the node score.
To set the `MostAllocated` strategy for the `NodeResourcesFit` plugin, use a
[scheduler configuration](/docs/reference/scheduling/config) similar to the following:
```yaml
apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
- args:
scoringStrategy:
resources:
- name: cpu
weight: 1
- name: memory
weight: 1
- name: intel.com/foo
weight: 3
- name: intel.com/bar
weight: 3
type: MostAllocated
name: NodeResourcesFit
```
To learn more about other parameters and their default configuration, see the API documentation for
[`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-NodeResourcesFitArgs).
## Enabling bin packing using RequestedToCapacityRatio
The `RequestedToCapacityRatio` strategy allows the users to specify the resources along with weights for
each resource to score nodes based on the request to capacity ratio. This
allows users to bin pack extended resources by using appropriate parameters
to improve the utilization of scarce resources in large clusters. It favors nodes according to a
configured function of the allocated resources. The behavior of the `RequestedToCapacityRatio` in
the `NodeResourcesFit` score function can be controlled by the
[scoringStrategy](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-ScoringStrategy) field.
Within the `scoringStrategy` field, you can configure two parameters: `requestedToCapacityRatio` and
`resources`. The `shape` in the `requestedToCapacityRatio`
parameter allows the user to tune the function as least requested or most
requested based on `utilization` and `score` values. The `resources` parameter
consists of `name` of the resource to be considered during scoring and `weight`
specify the weight of each resource.
Below is an example configuration that sets
the bin packing behavior for extended resources `intel.com/foo` and `intel.com/bar`
using the `requestedToCapacityRatio` field.
```yaml
apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
profiles:
- pluginConfig:
- args:
scoringStrategy:
resources:
- name: intel.com/foo
weight: 3
- name: intel.com/bar
weight: 3
requestedToCapacityRatio:
shape:
- utilization: 0
score: 0
- utilization: 100
score: 10
type: RequestedToCapacityRatio
name: NodeResourcesFit
```
Referencing the `KubeSchedulerConfiguration` file with the kube-scheduler
flag `--config=/path/to/config/file` will pass the configuration to the
scheduler.
To learn more about other parameters and their default configuration, see the API documentation for
[`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1/#kubescheduler-config-k8s-io-v1-NodeResourcesFitArgs).
### Tuning the score function
`shape` is used to specify the behavior of the `RequestedToCapacityRatio` function.
```yaml
shape:
- utilization: 0
score: 0
- utilization: 100
score: 10
```
The above arguments give the node a `score` of 0 if `utilization` is 0% and 10 for
`utilization` 100%, thus enabling bin packing behavior. To enable least
requested the score value must be reversed as follows.
```yaml
shape:
- utilization: 0
score: 10
- utilization: 100
score: 0
```
`resources` is an optional parameter which defaults to:
```yaml
resources:
- name: cpu
weight: 1
- name: memory
weight: 1
```
It can be used to add extended resources as follows:
```yaml
resources:
- name: intel.com/foo
weight: 5
- name: cpu
weight: 3
- name: memory
weight: 1
```
The `weight` parameter is optional and is set to 1 if not specified. Also, the
`weight` cannot be set to a negative value.
### Node scoring for capacity allocation
This section is intended for those who want to understand the internal details
of this feature.
Below is an example of how the node score is calculated for a given set of values.
Requested resources:
```
intel.com/foo : 2
memory: 256MB
cpu: 2
```
Resource weights:
```
intel.com/foo : 5
memory: 1
cpu: 3
```
FunctionShapePoint {{0, 0}, {100, 10}}
Node 1 spec:
```
Available:
intel.com/foo: 4
memory: 1 GB
cpu: 8
Used:
intel.com/foo: 1
memory: 256MB
cpu: 1
```
Node score:
```
intel.com/foo = resourceScoringFunction((2+1),4)
= (100 - ((4-3)*100/4)
= (100 - 25)
= 75 # requested + used = 75% * available
= rawScoringFunction(75)
= 7 # floor(75/10)
memory = resourceScoringFunction((256+256),1024)
= (100 -((1024-512)*100/1024))
= 50 # requested + used = 50% * available
= rawScoringFunction(50)
= 5 # floor(50/10)
cpu = resourceScoringFunction((2+1),8)
= (100 -((8-3)*100/8))
= 37.5 # requested + used = 37.5% * available
= rawScoringFunction(37.5)
= 3 # floor(37.5/10)
NodeScore = ((7 * 5) + (5 * 1) + (3 * 3)) / (5 + 1 + 3)
= 5
```
Node 2 spec:
```
Available:
intel.com/foo: 8
memory: 1GB
cpu: 8
Used:
intel.com/foo: 2
memory: 512MB
cpu: 6
```
Node score:
```
intel.com/foo = resourceScoringFunction((2+2),8)
= (100 - ((8-4)*100/8)
= (100 - 50)
= 50
= rawScoringFunction(50)
= 5
memory = resourceScoringFunction((256+512),1024)
= (100 -((1024-768)*100/1024))
= 75
= rawScoringFunction(75)
= 7
cpu = resourceScoringFunction((2+6),8)
= (100 -((8-8)*100/8))
= 100
= rawScoringFunction(100)
= 10
NodeScore = ((5 * 5) + (7 * 1) + (10 * 3)) / (5 + 1 + 3)
= 7
```
## {{% heading "whatsnext" %}}
- Read more about the [scheduling framework](/docs/concepts/scheduling-eviction/scheduling-framework/)
- Read more about [scheduler configuration](/docs/reference/scheduling/config/)