264 lines
7.4 KiB
Markdown
264 lines
7.4 KiB
Markdown
---
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title: 扩展资源的资源装箱
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content_type: concept
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weight: 80
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---
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<!--
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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_type: concept
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weight: 80
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---
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-->
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<!-- overview -->
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{{< feature-state for_k8s_version="1.16" state="alpha" >}}
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<!--
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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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-->
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使用 `RequestedToCapacityRatioResourceAllocation` 优先级函数，可以将 kube-scheduler
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配置为支持包含扩展资源在内的资源装箱操作。
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优先级函数可用于根据自定义需求微调 kube-scheduler 。
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<!-- body -->
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<!--
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## Enabling Bin Packing using RequestedToCapacityRatioResourceAllocation
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Kubernetes allows the users to specify the resources along with weights for
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each resource to score nodes based on the request to capacity ratio. This
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allows users to bin pack extended resources by using appropriate parameters
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and improves the utilization of scarce resources in large clusters. The
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behavior of the `RequestedToCapacityRatioResourceAllocation` priority function
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can be controlled by a configuration option called `RequestedToCapacityRatioArgs`.
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This argument consists of two parameters `shape` and `resources`. The `shape`
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parameter allows the user to tune the function as least requested or most
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requested based on `utilization` and `score` values. The `resources` parameter
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consists of `name` of the resource to be considered during scoring and `weight`
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specify the weight of each resource.
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-->
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## 使用 RequestedToCapacityRatioResourceAllocation 启用装箱
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Kubernetes 允许用户指定资源以及每类资源的权重，
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以便根据请求数量与可用容量之比率为节点评分。
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这就使得用户可以通过使用适当的参数来对扩展资源执行装箱操作，从而提高了大型集群中稀缺资源的利用率。
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`RequestedToCapacityRatioResourceAllocation` 优先级函数的行为可以通过名为
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`RequestedToCapacityRatioArgs` 的配置选项进行控制。
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该标志由两个参数 `shape` 和 `resources` 组成。
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`shape` 允许用户根据 `utilization` 和 `score` 值将函数调整为
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最少请求（least requested）或最多请求（most requested）计算。
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`resources` 包含由 `name` 和 `weight` 组成，`name` 指定评分时要考虑的资源，
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`weight` 指定每种资源的权重。
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<!--
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Below is an example configuration that sets
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`requestedToCapacityRatioArguments` to bin packing behavior for extended
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resources `intel.com/foo` and `intel.com/bar`.
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-->
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以下是一个配置示例，该配置将 `requestedToCapacityRatioArguments` 设置为对扩展资源
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`intel.com/foo` 和 `intel.com/bar` 的装箱行为
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```yaml
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apiVersion: kubescheduler.config.k8s.io/v1beta1
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kind: KubeSchedulerConfiguration
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profiles:
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# ...
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pluginConfig:
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- name: RequestedToCapacityRatio
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args:
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shape:
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- utilization: 0
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score: 10
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- utilization: 100
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score: 0
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resources:
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- name: intel.com/foo
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weight: 3
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- name: intel.com/bar
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weight: 5
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```
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<!--
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Referencing the `KubeSchedulerConfiguration` file with the kube-scheduler
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flag `--config=/path/to/config/file` will pass the configuration to the
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scheduler.
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-->
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使用 kube-scheduler 标志 `--config=/path/to/config/file`
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引用 `KubeSchedulerConfiguration` 文件将配置传递给调度器。
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<!--
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**This feature is disabled by default**
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-->
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**默认情况下此功能处于被禁用状态**
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<!--
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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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-->
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### 调整 RequestedToCapacityRatioResourceAllocation 优先级函数
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`shape` 用于指定 `RequestedToCapacityRatioPriority` 函数的行为。
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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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<!--
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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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-->
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上面的参数在 `utilization` 为 0% 时给节点评分为 0，在 `utilization` 为
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100% 时给节点评分为 10，因此启用了装箱行为。
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要启用最少请求（least requested）模式，必须按如下方式反转得分值。
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```yaml
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{"utilization": 0, "score": 10},
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{"utilization": 100, "score": 0}
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```
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<!--
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`resources` is an optional parameter which by defaults is set to:
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-->
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`resources` 是一个可选参数，默认情况下设置为：
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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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<!--
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It can be used to add extended resources as follows:
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-->
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它可以用来添加扩展资源，如下所示：
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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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<!--
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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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-->
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weight 参数是可选的，如果未指定，则设置为 1。
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同时，weight 不能设置为负值。
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<!--
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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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### RequestedToCapacityRatioResourceAllocation 优先级函数如何对节点评分
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本节适用于希望了解此功能的内部细节的人员。
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以下是如何针对给定的一组值来计算节点得分的示例。
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```
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请求的资源
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intel.com/foo: 2
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Memory: 256MB
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CPU: 2
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资源权重
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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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节点 Node 1 配置
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可用：
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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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已用：
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intel.com/foo: 1
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Memory: 256MB
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CPU: 1
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节点得分：
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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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节点 Node 2 配置
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可用：
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intel.com/foo: 8
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Memory: 1GB
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CPU: 8
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已用：
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intel.com/foo: 2
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Memory: 512MB
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CPU: 6
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节点得分：
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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 - 50)
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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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