Qiming Teng
359 lines
11 KiB
Markdown
359 lines
11 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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<!--
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In the [scheduling-plugin](/docs/reference/scheduling/config/#scheduling-plugins) `NodeResourcesFit` of kube-scheduler, there are two
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scoring strategies that support the bin packing of resources: `MostAllocated` and `RequestedToCapacityRatio`.
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-->
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在 kube-scheduler 的[调度插件](/zh-cn/docs/reference/scheduling/config/#scheduling-plugins)
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`NodeResourcesFit` 中存在两种支持资源装箱（bin packing）的策略：`MostAllocated` 和
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`RequestedToCapacityRatio`。
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<!-- body -->
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<!--
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## Enabling bin packing using MostAllocated strategy
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The `MostAllocated` strategy scores the nodes based on the utilization of resources, favoring the ones with higher allocation.
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For each resource type, you can set a weight to modify its influence in the node score.
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To set the `MostAllocated` strategy for the `NodeResourcesFit` plugin, use a
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[scheduler configuration](/docs/reference/scheduling/config) similar to the following:
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-->
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## 使用 MostAllocated 策略启用资源装箱 {#enabling-bin-packing-using-mostallocated-strategy}
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`MostAllocated` 策略基于资源的利用率来为节点计分，优选分配比率较高的节点。
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针对每种资源类型，你可以设置一个权重值以改变其对节点得分的影响。
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要为插件 `NodeResourcesFit` 设置 `MostAllocated` 策略，
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可以使用一个类似于下面这样的[调度器配置](/zh-cn/docs/reference/scheduling/config/)：
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```yaml
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apiVersion: kubescheduler.config.k8s.io/v1beta3
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kind: KubeSchedulerConfiguration
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profiles:
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- pluginConfig:
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- args:
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scoringStrategy:
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resources:
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- name: cpu
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weight: 1
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- name: memory
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weight: 1
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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: 3
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type: MostAllocated
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name: NodeResourcesFit
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```
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<!--
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To learn more about other parameters and their default configuration, see the API documentation for
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[`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-NodeResourcesFitArgs).
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-->
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要进一步了解其它参数及其默认配置，请参阅
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[`NodeResourcesFitArgs`](/zh-cn/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-NodeResourcesFitArgs)
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的 API 文档。
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<!--
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## Enabling bin packing using RequestedToCapacityRatio
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The `RequestedToCapacityRatio` strategy 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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to improve the utilization of scarce resources in large clusters. It favors nodes according to a
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configured function of the allocated resources. The behavior of the `RequestedToCapacityRatio` in
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the `NodeResourcesFit` score function can be controlled by the
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[scoringStrategy](/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-ScoringStrategy) field.
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Within the `scoringStrategy` field, you can configure two parameters: `requestedToCapacityRatioParam` and
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`resources`. The `shape` in `requestedToCapacityRatioParam`
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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 RequestedToCapacityRatio 策略来启用资源装箱 {#enabling-bin-packing-using-requestedtocapacityratio}
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`RequestedToCapacityRatio` 策略允许用户基于请求值与容量的比率，针对参与节点计分的每类资源设置权重。
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这一策略是的用户可以使用合适的参数来对扩展资源执行装箱操作，进而提升大规模集群中稀有资源的利用率。
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此策略根据所分配资源的一个配置函数来评价节点。
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`NodeResourcesFit` 计分函数中的 `RequestedToCapacityRatio` 可以通过字段
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[scoringStrategy](/zh-cn/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-ScoringStrategy)
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来控制。
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在 `scoringStrategy` 字段中，你可以配置两个参数：`requestedToCapacityRatioParam`
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和 `resources`。`requestedToCapacityRatioParam` 参数中的 `shape`
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设置使得用户能够调整函数的算法，基于 `utilization` 和 `score` 值计算最少请求或最多请求。
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`resources` 参数中包含计分过程中需要考虑的资源的 `name`，以及用来设置每种资源权重的 `weight`。
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<!--
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Below is an example configuration that sets
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the bin packing behavior for extended resources `intel.com/foo` and `intel.com/bar`
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using the `requestedToCapacityRatio` field.
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-->
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下面是一个配置示例，使用 `requestedToCapacityRatio` 字段为扩展资源 `intel.com/foo`
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和 `intel.com/bar` 设置装箱行为：
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```yaml
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apiVersion: kubescheduler.config.k8s.io/v1beta3
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kind: KubeSchedulerConfiguration
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profiles:
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- pluginConfig:
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- args:
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scoringStrategy:
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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: 3
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requestedToCapacityRatioParam:
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shape:
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- utilization: 0
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score: 0
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- utilization: 100
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score: 10
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type: RequestedToCapacityRatio
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name: NodeResourcesFit
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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，可以将配置传递给调度器。
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<!--
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To learn more about other parameters and their default configuration, see the API documentation for
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[`NodeResourcesFitArgs`](/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-NodeResourcesFitArgs).
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-->
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要进一步了解其它参数及其默认配置，可以参阅
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[`NodeResourcesFitArgs`](/zh-cn/docs/reference/config-api/kube-scheduler-config.v1beta3/#kubescheduler-config-k8s-io-v1beta3-NodeResourcesFitArgs)
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的 API 文档。
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<!--
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### Tuning the score function
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`shape` is used to specify the behavior of the `RequestedToCapacityRatio` function.
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-->
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### 调整计分函数 {#tuning-the-score-function}
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`shape` 用于指定 `RequestedToCapacityRatio` 函数的行为。
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```yaml
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shape:
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- utilization: 0
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score: 0
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- utilization: 100
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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
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`utilization` 100%, thus enabling bin packing behavior. To enable least
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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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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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```
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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
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weight: 1
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- name: memory
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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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-->
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它可以像下面这样用来添加扩展资源：
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```yaml
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resources:
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- name: intel.com/foo
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weight: 5
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- name: cpu
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weight: 3
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- name: memory
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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
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`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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### Node scoring for capacity allocation
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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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### 节点容量分配的评分 {#node-scoring-for-capacity-allocation}
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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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Requested resources:
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-->
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请求的资源：
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```
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intel.com/foo : 2
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memory: 256MB
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cpu: 2
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```
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<!--
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Resource weights:
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-->
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资源权重：
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```
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intel.com/foo : 5
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memory: 1
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cpu: 3
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```
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```
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FunctionShapePoint {{0, 0}, {100, 10}}
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```
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<!--
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Node 1 spec:
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-->
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节点 1 配置：
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```
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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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```
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<!--
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Node score:
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-->
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节点得分：
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```
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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 # requested + used = 75% * available
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= rawScoringFunction(75)
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= 7 # floor(75/10)
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memory = resourceScoringFunction((256+256),1024)
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= (100 -((1024-512)*100/1024))
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= 50 # requested + used = 50% * available
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= rawScoringFunction(50)
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= 5 # floor(50/10)
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cpu = resourceScoringFunction((2+1),8)
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= (100 -((8-3)*100/8))
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= 37.5 # requested + used = 37.5% * available
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= rawScoringFunction(37.5)
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= 3 # floor(37.5/10)
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NodeScore = (7 * 5) + (5 * 1) + (3 * 3) / (5 + 1 + 3)
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= 5
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```
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<!--
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Node 2 spec:
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-->
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节点 2 配置：
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```
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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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```
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<!--
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Node score:
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-->
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节点得分：
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```
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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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## {{% heading "whatsnext" %}}
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<!--
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- Read more about the [scheduling framework](/docs/concepts/scheduling-eviction/scheduling-framework/)
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- Read more about [scheduler configuration](/docs/reference/scheduling/config/)
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-->
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- 继续阅读[调度器框架](/zh-cn/docs/concepts/scheduling-eviction/scheduling-framework/)
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- 继续阅读[调度器配置](/zh-cn/docs/reference/scheduling/config/)
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