You are viewing documentation for Kubernetes version: v1.20
Kubernetes v1.20 documentation is no longer actively maintained. The version you are currently viewing is a static snapshot. For up-to-date documentation, see the latest version.
Resource Bin Packing for Extended Resources
Kubernetes v1.16 [alpha]
The kube-scheduler can be configured to enable bin packing of resources along
with extended resources using RequestedToCapacityRatioResourceAllocation
priority function. Priority functions can be used to fine-tune the
kube-scheduler as per custom needs.
Enabling Bin Packing using RequestedToCapacityRatioResourceAllocation
Kubernetes 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
and improves the utilization of scarce resources in large clusters. The
behavior of the RequestedToCapacityRatioResourceAllocation
priority function
can be controlled by a configuration option called
requestedToCapacityRatioArguments
. This argument consists of two parameters
shape
and resources
. The shape
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
requestedToCapacityRatioArguments
to bin packing behavior for extended
resources intel.com/foo
and intel.com/bar
.
apiVersion: v1
kind: Policy
# ...
priorities:
# ...
- name: RequestedToCapacityRatioPriority
weight: 2
argument:
requestedToCapacityRatioArguments:
shape:
- utilization: 0
score: 0
- utilization: 100
score: 10
resources:
- name: intel.com/foo
weight: 3
- name: intel.com/bar
weight: 5
This feature is disabled by default
Tuning the Priority Function
shape
is used to specify the behavior of the
RequestedToCapacityRatioPriority
function.
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.
shape:
- utilization: 0
score: 10
- utilization: 100
score: 0
resources
is an optional parameter which defaults to:
resources:
- name: CPU
weight: 1
- name: Memory
weight: 1
It can be used to add extended resources as follows:
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
What's next
- Read more about the scheduling framework
- Read more about scheduler configuration