Merge branch 'dev-1.16' into merged-master-dev-1.16
This commit is contained in:
simplytunde
@@ -394,4 +394,8 @@ The design documents for
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[node affinity](https://git.k8s.io/community/contributors/design-proposals/scheduling/nodeaffinity.md)
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and for [inter-pod affinity/anti-affinity](https://git.k8s.io/community/contributors/design-proposals/scheduling/podaffinity.md) contain extra background information about these features.
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Once a Pod is assigned to a Node, the kubelet runs the Pod and allocates node-local resources.
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The [topology manager](/docs/tasks/administer-cluster/topology-manager/) can take part in node-level
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resource allocation decisions.
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{{% /capture %}}
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@@ -0,0 +1,62 @@
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---
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reviewers:
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- dchen1107
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- egernst
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- tallclair
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title: Pod Overhead
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content_template: templates/concept
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weight: 20
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---
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{{% capture overview %}}
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{{< feature-state for_k8s_version="v1.16" state="alpha" >}}
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When you run a Pod on a Node, the Pod itself takes an amount of system resources. These
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resources are additional to the resources needed to run the container(s) inside the Pod.
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_Pod Overhead_ is a feature for accounting for the resources consumed by the pod infrastructure
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on top of the container requests & limits.
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{{% /capture %}}
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{{% capture body %}}
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## Pod Overhead
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In Kubernetes, the pod's overhead is set at
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[admission](https://kubernetes.io/docs/reference/access-authn-authz/extensible-admission-controllers/#what-are-admission-webhooks)
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time according to the overhead associated with the pod's
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[RuntimeClass](https://kubernetes.io/docs/concepts/containers/runtime-class/).
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When Pod Overhead is enabled, the overhead is considered in addition to the sum of container
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resource requests when scheduling a pod. Similarly, Kubelet will include the pod overhead when sizing
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the pod cgroup, and when carrying out pod eviction ranking.
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### Set Up
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You need to make sure that the `PodOverhead`
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[feature gate](/docs/reference/command-line-tools-reference/feature-gates/) is enabled (it is off by default)
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across your cluster. This means:
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- in {{< glossary_tooltip text="kube-scheduler" term_id="kube-scheduler" >}}
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- in {{< glossary_tooltip text="kube-apiserver" term_id="kube-apiserver" >}}
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- in the {{< glossary_tooltip text="kubelet" term_id="kubelet" >}} on each Node
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- in any custom API servers that use feature gates
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{{< note >}}
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Users who can write to RuntimeClass resources are able to have cluster-wide impact on
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workload performance. You can limit access to this ability using Kubernetes access controls.
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See [Authorization Overview](/docs/reference/access-authn-authz/authorization/) for more details.
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{{< /note >}}
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{{% capture whatsnext %}}
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* [RuntimeClass](/docs/concepts/containers/runtime-class/)
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* [PodOverhead Design](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/20190226-pod-overhead.md)
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{{% /capture %}}
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@@ -0,0 +1,197 @@
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---
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reviewers:
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- bsalamat
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- k82cn
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- ahg-g
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title: Resource Bin Packing for Extended Resources
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content_template: templates/concept
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weight: 10
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---
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{{% capture overview %}}
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{{< feature-state for_k8s_version="1.16" state="alpha" >}}
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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.
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{{% /capture %}}
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{{% capture body %}}
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## Enabling Bin Packing using RequestedToCapacityRatioResourceAllocation
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Before Kubernetes 1.15, Kube-scheduler used to allow scoring nodes based on the request to capacity ratio of primary resources like CPU and Memory. Kubernetes 1.16 added a new parameter to the priority function that 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 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`. Shape allows the user to tune the function as least requested or most requested based on `utilization` and `score` values. Resources
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consists of `name` which specifies the resource to be considered during scoring and `weight` specify the weight of each resource.
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Below is an example configuration that sets `requestedToCapacityRatioArguments` to bin packing behavior for extended resources `intel.com/foo` and `intel.com/bar`
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```json
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{
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"kind" : "Policy",
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"apiVersion" : "v1",
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...
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"priorities" : [
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...
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{
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"name": "RequestedToCapacityRatioPriority",
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"weight": 2,
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"argument": {
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"requestedToCapacityRatioArguments": {
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"shape": [
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{"utilization": 0, "score": 0},
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{"utilization": 100, "score": 10}
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],
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"resources": [
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{"name": "intel.com/foo", "weight": 3},
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{"name": "intel.com/bar", "weight": 5}
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]
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}
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}
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}
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],
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}
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```
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**This feature is disabled by default**
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### Tuning RequestedToCapacityRatioResourceAllocation Priority Function
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`shape` is used to specify the behavior of the `RequestedToCapacityRatioPriority` function.
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```yaml
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{"utilization": 0, "score": 0},
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{"utilization": 100, "score": 10}
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```
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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.
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```yaml
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{"utilization": 0, "score": 100},
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{"utilization": 100, "score": 0}
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```
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`resources` is an optional parameter which by defaults is set to:
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``` yaml
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"resources": [
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{"name": "CPU", "weight": 1},
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{"name": "Memory", "weight": 1}
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]
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```
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It can be used to add extended resources as follows:
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```yaml
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"resources": [
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{"name": "intel.com/foo", "weight": 5},
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{"name": "CPU", "weight": 3},
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{"name": "Memory", "weight": 1}
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]
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```
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The weight parameter is optional and is set to 1 if not specified. Also, the weight cannot be set to a negative value.
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### How the RequestedToCapacityRatioResourceAllocation Priority Function Scores Nodes
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This section is intended for those who want to understand the internal details
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of this feature.
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Below is an example of how the node score is calculated for a given set of values.
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```
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Requested Resources
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intel.com/foo : 2
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Memory: 256MB
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CPU: 2
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Resource Weights
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intel.com/foo : 5
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Memory: 1
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CPU: 3
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FunctionShapePoint {{0, 0}, {100, 10}}
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Node 1 Spec
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Available:
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intel.com/foo : 4
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Memory : 1 GB
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CPU: 8
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Used:
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intel.com/foo: 1
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Memory: 256MB
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CPU: 1
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Node Score:
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intel.com/foo = resourceScoringFunction((2+1),4)
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= (100 - ((4-3)*100/4)
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= (100 - 25)
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= 75
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= rawScoringFunction(75)
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= 7
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Memory = resourceScoringFunction((256+256),1024)
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= (100 -((1024-512)*100/1024))
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= 50
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= rawScoringFunction(50)
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= 5
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CPU = resourceScoringFunction((2+1),8)
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= (100 -((8-3)*100/8))
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= 37.5
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= rawScoringFunction(37.5)
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= 3
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NodeScore = (7 * 5) + (5 * 1) + (3 * 3) / (5 + 1 + 3)
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= 5
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Node 2 Spec
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Available:
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intel.com/foo: 8
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Memory: 1GB
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CPU: 8
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Used:
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intel.com/foo: 2
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Memory: 512MB
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CPU: 6
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Node Score:
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intel.com/foo = resourceScoringFunction((2+2),8)
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= (100 - ((8-4)*100/8)
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= (100 - 25)
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= 50
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= rawScoringFunction(50)
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= 5
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Memory = resourceScoringFunction((256+512),1024)
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= (100 -((1024-768)*100/1024))
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= 75
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= rawScoringFunction(75)
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= 7
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CPU = resourceScoringFunction((2+6),8)
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= (100 -((8-8)*100/8))
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= 100
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= rawScoringFunction(100)
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= 10
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NodeScore = (5 * 5) + (7 * 1) + (10 * 3) / (5 + 1 + 3)
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= 7
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```
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{{% /capture %}}
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