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<title>kube-scheduler predicates 与 priorities 调度算法源码分析 · 田飞雨</title>
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<li class="chapter " data-level="1.1" data-path="../">
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<b>1.1.</b>
Introduction
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<b>1.2.</b>
kubernetes
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node controller 源码分析
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job controller 源码分析
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garbage collector controller 源码分析
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<b>1.2.6.</b>
daemonset controller 源码分析
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<b>1.2.7.</b>
statefulset controller 源码分析
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<b>1.2.8.</b>
deployment controller 源码分析
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<b>1.2.9.</b>
replicaset controller 源码分析
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<b>1.2.10.</b>
kube-scheduler 源码分析
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<li class="chapter active" data-level="1.2.11" data-path="kube_scheduler_algorithm.html">
<a href="kube_scheduler_algorithm.html">
<b>1.2.11.</b>
kube-scheduler predicates 与 priorities 调度算法源码分析
</a>
</li>
<li class="chapter " data-level="1.2.12" data-path="kube_scheduler_preempt.html">
<a href="kube_scheduler_preempt.html">
<b>1.2.12.</b>
kube-scheduler 优先级与抢占机制源码分析
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<b>1.2.13.</b>
kubernetes service 原理解析
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<b>1.2.14.</b>
kube-proxy 源码分析
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<b>1.2.15.</b>
kube-proxy iptables 模式源码分析
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<b>1.2.16.</b>
kube-proxy ipvs 模式源码分析
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<b>1.2.17.</b>
kubelet 架构浅析
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<b>1.2.18.</b>
kubelet 启动流程分析
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<li class="chapter " data-level="1.2.19" data-path="kubelet_create_pod.html">
<a href="kubelet_create_pod.html">
<b>1.2.19.</b>
kubelet 创建 pod 的流程
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<li class="chapter " data-level="1.2.20" data-path="node_status.html">
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<b>1.2.20.</b>
kubelet 状态上报的方式
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<b>1.2.21.</b>
kubelet 中事件处理机制
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<li class="chapter " data-level="1.2.22" data-path="kubelet_status_manager.html">
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<b>1.2.22.</b>
kubelet statusManager 源码分析
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<b>1.2.23.</b>
kubernetes 中 Qos 的设计与实现
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<li class="chapter " data-level="1.2.24" data-path="kubelet_garbage_collect.html">
<a href="kubelet_garbage_collect.html">
<b>1.2.24.</b>
kubelet 中垃圾回收机制的设计与实现
</a>
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<li class="chapter " data-level="1.2.25" data-path="k8s_evicted.html">
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<b>1.2.25.</b>
kubernetes 中 Evicted pod 是如何产生的
</a>
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<li class="chapter " data-level="1.2.26" data-path="kubernetes_descheduler.html">
<a href="kubernetes_descheduler.html">
<b>1.2.26.</b>
Kubernetes 中 Descheduler 组件的使用与扩展
</a>
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<b>1.3.</b>
knative
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<b>1.3.1.</b>
在 minkube 上部署 knative
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<b>1.3.2.</b>
knative serving 组件分析
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<b>1.4.</b>
cgroup
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<b>1.4.1.</b>
blkio cgroup
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<li class="chapter " data-level="1.4.2" data-path="../cgroup/pid_cgroup.html">
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<b>1.4.2.</b>
pid cgroup
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<b>1.5.</b>
go
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<b>1.5.1.</b>
golang 中函数使用值返回与指针返回的区别，底层原理分析
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<b>1.5.2.</b>
Golang 程序启动流程分析
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</ul>
</li>
<li><a href="#总结">总结</a></li>
</ul>
<p>在上篇文章<a href="http://blog.tianfeiyu.com/2019/10/21/kube_scheduler_process/" target="_blank">kube-scheduler 源码分析</a>中已经介绍了 kube-scheduler 的设计以及从源码角度分析了其执行流程，这篇文章会专注介绍调度过程中 predicates 和 priorities 这两个调度策略主要发生作用的阶段。</p>
<blockquote>
<p>kubernetes 版本: v1.16</p>
</blockquote>
<h3 id="predicates-调度算法源码分析"><a name="predicates-调度算法源码分析" class="anchor-navigation-ex-anchor" href="#predicates-调度算法源码分析"><i class="fa fa-link" aria-hidden="true"></i></a>predicates 调度算法源码分析</h3>
<p>predicates 算法主要是对集群中的 node 进行过滤，选出符合当前 pod 运行的 nodes。</p>
<h4 id="调度算法说明"><a name="调度算法说明" class="anchor-navigation-ex-anchor" href="#调度算法说明"><i class="fa fa-link" aria-hidden="true"></i></a>调度算法说明</h4>
<p>上节已经提到默认的调度算法在<code>pkg/scheduler/algorithmprovider/defaults/defaults.go</code>中定义了：</p>
<pre class="language-"><code>func defaultPredicates() sets.String {
return sets.NewString(
predicates.NoVolumeZoneConflictPred,
predicates.MaxEBSVolumeCountPred,
predicates.MaxGCEPDVolumeCountPred,
predicates.MaxAzureDiskVolumeCountPred,
predicates.MaxCSIVolumeCountPred,
predicates.MatchInterPodAffinityPred,
predicates.NoDiskConflictPred,
predicates.GeneralPred,
predicates.CheckNodeMemoryPressurePred,
predicates.CheckNodeDiskPressurePred,
predicates.CheckNodePIDPressurePred,
predicates.CheckNodeConditionPred,
predicates.PodToleratesNodeTaintsPred,
predicates.CheckVolumeBindingPred,
)
}
</code></pre><p>下面是对默认调度算法的一些说明：</p>
<table>
<thead>
<tr>
<th>predicates 算法</th>
<th>说明</th>
</tr>
</thead>
<tbody>
<tr>
<td>GeneralPred</td>
<td>GeneralPred 包含 PodFitsResources、PodFitsHost,、PodFitsHostPorts、PodMatchNodeSelector 四种算法</td>
</tr>
<tr>
<td>NoDiskConflictPred</td>
<td>检查多个 Pod 声明挂载的持久化 Volume 是否有冲突</td>
</tr>
<tr>
<td>MaxGCEPDVolumeCountPred</td>
<td>检查 GCE 持久化 Volume 是否超过了一定数目</td>
</tr>
<tr>
<td>MaxAzureDiskVolumeCountPred</td>
<td>检查 Azure 持久化 Volume 是否超过了一定数目</td>
</tr>
<tr>
<td>MaxCSIVolumeCountPred</td>
<td>检查 CSI 持久化 Volume 是否超过了一定数目（已废弃）</td>
</tr>
<tr>
<td>MaxEBSVolumeCountPred</td>
<td>检查 EBS 持久化 Volume 是否超过了一定数目</td>
</tr>
<tr>
<td>NoVolumeZoneConflictPred</td>
<td>检查持久化 Volume 的 Zone（高可用域）标签是否与节点的 Zone 标签相匹配</td>
</tr>
<tr>
<td>CheckVolumeBindingPred</td>
<td>检查该 Pod 对应 PV 的 nodeAffinity 字段是否跟某个节点的标签相匹配，Local Persistent Volume(本地持久化卷)必须使用 nodeAffinity 来跟某个具体的节点绑定</td>
</tr>
<tr>
<td>PodToleratesNodeTaintsPred</td>
<td>检查 Node 的 Taint 机制，只有当 Pod 的 Toleration 字段与 Node 的 Taint 字段能够匹配时，这个 Pod 才能被调度到该节点上</td>
</tr>
<tr>
<td>MatchInterPodAffinityPred</td>
<td>检查待调度 Pod 与 Node 上的已有 Pod 之间的亲密（affinity）和反亲密（anti-affinity）关系</td>
</tr>
<tr>
<td>CheckNodeConditionPred</td>
<td>检查 NodeCondition</td>
</tr>
<tr>
<td>CheckNodePIDPressurePred</td>
<td>检查 NodePIDPressure</td>
</tr>
<tr>
<td>CheckNodeDiskPressurePred</td>
<td>检查 NodeDiskPressure</td>
</tr>
<tr>
<td>CheckNodeMemoryPressurePred</td>
<td>检查 NodeMemoryPressure</td>
</tr>
</tbody>
</table>
<p>默认的 predicates 调度算法主要分为五种类型：</p>
<p>1、第一种类型叫作 GeneralPredicates，包含 PodFitsResources、PodFitsHost、PodFitsHostPorts、PodMatchNodeSelector 四种策略，其具体含义如下所示：</p>
<ul>
<li>PodFitsHost：检查宿主机的名字是否跟 Pod 的 spec.nodeName 一致</li>
<li>PodFitsHostPorts：检查 Pod 申请的宿主机端口（spec.nodePort）是不是跟已经被使用的端口有冲突</li>
<li>PodMatchNodeSelector：检查 Pod 的 nodeSelector 或者 nodeAffinity 指定的节点是否与节点匹配等</li>
<li>PodFitsResources：检查主机的资源是否满足 Pod 的需求，根据实际已经分配（Request）的资源量做调度 </li>
</ul>
<p>kubelet 在启动 Pod 前，会执行一个 Admit 操作来进行二次确认，这里二次确认的规则就是执行一遍 GeneralPredicates。</p>
<p>2、第二种类型是与 Volume 相关的过滤规则，主要有NoDiskConflictPred、MaxGCEPDVolumeCountPred、MaxAzureDiskVolumeCountPred、MaxCSIVolumeCountPred、MaxEBSVolumeCountPred、NoVolumeZoneConflictPred、CheckVolumeBindingPred。</p>
<p>3、第三种类型是宿主机相关的过滤规则，主要是 PodToleratesNodeTaintsPred。</p>
<p>4、第四种类型是 Pod 相关的过滤规则，主要是 MatchInterPodAffinityPred。</p>
<p>5、第五种类型是新增的过滤规则，与宿主机的运行状况有关，主要有 CheckNodeCondition、 CheckNodeMemoryPressure、CheckNodePIDPressure、CheckNodeDiskPressure 四种。若启用了 <code>TaintNodesByCondition FeatureGates</code> 则在 predicates 算法中会将该四种算法移除，<code>TaintNodesByCondition</code> 基于 <a href="https://kubernetes.io/docs/concepts/architecture/nodes/#condition" target="_blank">node conditions</a> 当 node 出现 pressure 时自动为 node 打上 taints 标签，该功能在 v1.8 引入，v1.12 成为 beta 版本，目前 v1.16 中也是 beta 版本，但在 v1.13 中该功能已默认启用。</p>
<p>predicates 调度算法也有一个顺序，要不然在一台资源已经严重不足的宿主机上，上来就开始计算 PodAffinityPredicate 是没有实际意义的，其默认顺序如下所示：</p>
<p><code>k8s.io/kubernetes/pkg/scheduler/algorithm/predicates/predicates.go:146</code></p>
<pre class="language-"><code>var (
predicatesOrdering = []string{CheckNodeConditionPred, CheckNodeUnschedulablePred,
GeneralPred, HostNamePred, PodFitsHostPortsPred,
MatchNodeSelectorPred, PodFitsResourcesPred, NoDiskConflictPred,
PodToleratesNodeTaintsPred, PodToleratesNodeNoExecuteTaintsPred, CheckNodeLabelPresencePred,
CheckServiceAffinityPred, MaxEBSVolumeCountPred, MaxGCEPDVolumeCountPred, MaxCSIVolumeCountPred,
MaxAzureDiskVolumeCountPred, MaxCinderVolumeCountPred, CheckVolumeBindingPred, NoVolumeZoneConflictPred,
CheckNodeMemoryPressurePred, CheckNodePIDPressurePred, CheckNodeDiskPressurePred, EvenPodsSpreadPred, MatchInterPodAffinityPred}
)
</code></pre><h4 id="源码分析"><a name="源码分析" class="anchor-navigation-ex-anchor" href="#源码分析"><i class="fa fa-link" aria-hidden="true"></i></a>源码分析</h4>
<p>上节中已经说到调用预选以及优选算法的逻辑在 <code>k8s.io/kubernetes/pkg/scheduler/core/generic_scheduler.go:189</code>中，</p>
<pre class="language-"><code>func (g *genericScheduler) Schedule(pod *v1.Pod, pluginContext *framework.PluginContext) (result ScheduleResult, err error) {
......
// 执行 predicates 策略
filteredNodes, failedPredicateMap, filteredNodesStatuses, err := g.findNodesThatFit(pluginContext, pod)
......
// 执行 priorities 策略
priorityList, err := PrioritizeNodes(pod, g.nodeInfoSnapshot.NodeInfoMap, metaPrioritiesInterface, g.prioritizers, filteredNodes, g.extenders, g.framework, pluginContext)
......
return
}
</code></pre><p><code>findNodesThatFit()</code> 是 predicates 策略的实际调用方法，其基本流程如下：</p>
<ul>
<li>设定最多需要检查的节点数，作为预选节点数组的容量，避免总节点过多影响调度效率</li>
<li>通过<code>NodeTree()</code>不断获取下一个节点来判断该节点是否满足 pod 的调度条件</li>
<li>通过之前注册的各种 predicates 函数来判断当前节点是否符合 pod 的调度条件</li>
<li>最后返回满足调度条件的 node 列表，供下一步的优选操作</li>
</ul>