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cylon

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彻底搞懂kubernetes调度框架与插件

调度框架 [1]

本文基于 kubernetes 1.24 进行分析

调度框架(Scheduling Framework)是Kubernetes 的调度器 kube-scheduler 设计的的可插拔架构,将插件(调度算法)嵌入到调度上下文的每个扩展点中,并编译为 kube-scheduler

kube-scheduler 1.22 之后,在 pkg/scheduler/framework/interface.go 中定义了一个 Plugininterface,这个 interface 作为了所有插件的父级。而每个未调度的 Pod,Kubernetes 调度器会根据一组规则尝试在集群中寻找一个节点。

type Plugin interface {
    Name() string
}
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下面会对每个算法是如何实现的进行分析

在初始化 scheduler 时,会创建一个 profile,profile是关于 scheduler 调度配置相关的定义

func New(client clientset.Interface,
...
    profiles, err := profile.NewMap(options.profiles, registry, recorderFactory, stopCh,
        frameworkruntime.WithComponentConfigVersion(options.componentConfigVersion),
        frameworkruntime.WithClientSet(client),
        frameworkruntime.WithKubeConfig(options.kubeConfig),
        frameworkruntime.WithInformerFactory(informerFactory),
        frameworkruntime.WithSnapshotSharedLister(snapshot),
        frameworkruntime.WithPodNominator(nominator),
        frameworkruntime.WithCaptureProfile(frameworkruntime.CaptureProfile(options.frameworkCapturer)),
        frameworkruntime.WithClusterEventMap(clusterEventMap),
        frameworkruntime.WithParallelism(int(options.parallelism)),
        frameworkruntime.WithExtenders(extenders),
    )
    if err != nil {
        return nil, fmt.Errorf("initializing profiles: %v", err)
    }

    if len(profiles) == 0 {
        return nil, errors.New("at least one profile is required")
    }
....
}
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关于 profile 的实现,则为 KubeSchedulerProfile,也是作为 yaml生成时传入的配置

// KubeSchedulerProfile 是一个 scheduling profile.
type KubeSchedulerProfile struct {
    // SchedulerName 是与此配置文件关联的调度程序的名称。
    // 如果 SchedulerName 与 pod “spec.schedulerName”匹配,则使用此配置文件调度 pod。
    SchedulerName string

    // Plugins指定应该启用或禁用的插件集。
    // 启用的插件是除了默认插件之外应该启用的插件。禁用插件应是禁用的任何默认插件。
    // 当没有为扩展点指定启用或禁用插件时,将使用该扩展点的默认插件(如果有)。
    // 如果指定了 QueueSort 插件,
    // 则必须为所有配置文件指定相同的 QueueSort Plugin 和 PluginConfig。
    // 这个Plugins展现的形式则是调度上下文中的所有扩展点(这是抽象),实际中会表现为多个扩展点
    Plugins *Plugins

    // PluginConfig 是每个插件的一组可选的自定义插件参数。
    // 如果省略PluginConfig参数等同于使用该插件的默认配置。
    PluginConfig []PluginConfig
}
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对于 profile.NewMap 就是根据给定的配置来构建这个framework,因为配置可能是存在多个的。而 Registry 则是所有可用插件的集合,内部构造则是 PluginFactory ,通过函数来构建出对应的 plugin

func NewMap(cfgs []config.KubeSchedulerProfile, r frameworkruntime.Registry, recorderFact RecorderFactory,
    stopCh <-chan struct{}, opts ...frameworkruntime.Option) (Map, error) {
    m := make(Map)
    v := cfgValidator{m: m}

    for _, cfg := range cfgs {
        p, err := newProfile(cfg, r, recorderFact, stopCh, opts...)
        if err != nil {
            return nil, fmt.Errorf("creating profile for scheduler name %s: %v", cfg.SchedulerName, err)
        }
        if err := v.validate(cfg, p); err != nil {
            return nil, err
        }
        m[cfg.SchedulerName] = p
    }
    return m, nil
}

// newProfile 给的配置构建出一个profile
func newProfile(cfg config.KubeSchedulerProfile, r frameworkruntime.Registry, recorderFact RecorderFactory,
    stopCh <-chan struct{}, opts ...frameworkruntime.Option) (framework.Framework, error) {
    recorder := recorderFact(cfg.SchedulerName)
    opts = append(opts, frameworkruntime.WithEventRecorder(recorder))
    return frameworkruntime.NewFramework(r, &cfg, stopCh, opts...)
}

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可以看到最终返回的是一个 Framework 。那么来看下这个 Framework

Framework 是一个抽象,管理着调度过程中所使用的所有插件,并在调度上下文中适当的位置去运行对应的插件

type Framework interface {
    Handle
    // QueueSortFunc 返回对调度队列中的 Pod 进行排序的函数
    // 也就是less,在Sort打分阶段的打分函数
    QueueSortFunc() LessFunc

    // RunPreFilterPlugins 运行配置的一组PreFilter插件。
    // 如果这组插件中,任何一个插件失败,则返回 *Status 并设置为non-success。
    // 如果返回状态为non-success,则调度周期中止。
    // 它还返回一个 PreFilterResult,它可能会影响到要评估下游的节点。

    RunPreFilterPlugins(ctx context.Context, state *CycleState, pod *v1.Pod) (*PreFilterResult, *Status)

    // RunPostFilterPlugins 运行配置的一组PostFilter插件。 
    // PostFilter 插件是通知性插件,在这种情况下应配置为先执行并返回 Unschedulable 状态,
    // 或者尝试更改集群状态以使 pod 在未来的调度周期中可能会被调度。
    RunPostFilterPlugins(ctx context.Context, state *CycleState, pod *v1.Pod, filteredNodeStatusMap NodeToStatusMap) (*PostFilterResult, *Status)

    // RunPreBindPlugins 运行配置的一组 PreBind 插件。
    // 如果任何一个插件返回错误,则返回 *Status 并且code设置为non-success。
    // 如果code为“Unschedulable”,则调度检查失败,
    // 则认为是内部错误。在任何一种情况下,Pod都不会被bound。
    RunPreBindPlugins(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string) *Status

    // RunPostBindPlugins 运行配置的一组PostBind插件
    RunPostBindPlugins(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string)

    // RunReservePluginsReserve运行配置的一组Reserve插件的Reserve方法。
    // 如果在这组调用中的任何一个插件返回错误,则不会继续运行剩余调用的插件并返回错误。
    // 在这种情况下,pod将不能被调度。
    RunReservePluginsReserve(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string) *Status

    // RunReservePluginsUnreserve运行配置的一组Reserve插件的Unreserve方法。
    RunReservePluginsUnreserve(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string)

    // RunPermitPlugins运行配置的一组Permit插件。
    // 如果这些插件中的任何一个返回“Success”或“Wait”之外的状态,则它不会继续运行其余插件并返回错误。
    // 否则,如果任何插件返回 “Wait”,则此函数将创建等待pod并将其添加到当前等待pod的map中,
    // 并使用“Wait” code返回状态。 Pod将在Permit插件返回的最短持续时间内保持等待pod。
    RunPermitPlugins(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string) *Status

    // 如果pod是waiting pod,WaitOnPermit 将阻塞,直到等待的pod被允许或拒绝。
    WaitOnPermit(ctx context.Context, pod *v1.Pod) *Status

    // RunBindPlugins运行配置的一组bind插件。 Bind插件可以选择是否处理Pod。
    // 如果 Bind 插件选择跳过binding,它应该返回 code=5("skip")状态。
    // 否则,它应该返回“Error”或“Success”。
    // 如果没有插件处理绑定,则RunBindPlugins返回code=5("skip")的状态。
    RunBindPlugins(ctx context.Context, state *CycleState, pod *v1.Pod, nodeName string) *Status

    // 如果至少定义了一个filter插件,则HasFilterPlugins返回true
    HasFilterPlugins() bool

    // 如果至少定义了一个PostFilter插件,则HasPostFilterPlugins返回 true。
    HasPostFilterPlugins() bool

    // 如果至少定义了一个Score插件,则HasScorePlugins返回 true。
    HasScorePlugins() bool

    // ListPlugins将返回map。key为扩展点名称,value则是配置的插件列表。
    ListPlugins() *config.Plugins

    // ProfileName则是与profile name关联的framework
    ProfileName() string
}
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而实现这个抽象的则是 frameworkImplframeworkImpl 是初始化与运行 scheduler plugins 的组件,并在调度上下文中会运行这些扩展点

type frameworkImpl struct {
   registry             Registry
   snapshotSharedLister framework.SharedLister
   waitingPods          *waitingPodsMap
   scorePluginWeight    map[string]int
   queueSortPlugins     []framework.QueueSortPlugin
   preFilterPlugins     []framework.PreFilterPlugin
   filterPlugins        []framework.FilterPlugin
   postFilterPlugins    []framework.PostFilterPlugin
   preScorePlugins      []framework.PreScorePlugin
   scorePlugins         []framework.ScorePlugin
   reservePlugins       []framework.ReservePlugin
   preBindPlugins       []framework.PreBindPlugin
   bindPlugins          []framework.BindPlugin
   postBindPlugins      []framework.PostBindPlugin
   permitPlugins        []framework.PermitPlugin

   clientSet       clientset.Interface
   kubeConfig      *restclient.Config
   eventRecorder   events.EventRecorder
   informerFactory informers.SharedInformerFactory

   metricsRecorder *metricsRecorder
   profileName     string

   extenders []framework.Extender
   framework.PodNominator

   parallelizer parallelize.Parallelizer
}
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那么来看下 Registry ,Registry 是作为一个可用插件的集合。framework 使用 registry 来启用和对插件配置的初始化。在初始化框架之前,所有插件都必须在注册表中。表现形式就是一个 map[]key 是插件的名称,value是 PluginFactory

type Registry map[string]PluginFactory
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而在 pkg\scheduler\framework\plugins\registry.go 中会将所有的 in-tree plugin 注册进来。通过 NewInTreeRegistry 。后续如果还有插件要注册,可以通过 WithFrameworkOutOfTreeRegistry 来注册其他的插件。

func NewInTreeRegistry() runtime.Registry {
    fts := plfeature.Features{
        EnableReadWriteOncePod:                       feature.DefaultFeatureGate.Enabled(features.ReadWriteOncePod),
        EnableVolumeCapacityPriority:                 feature.DefaultFeatureGate.Enabled(features.VolumeCapacityPriority),
        EnableMinDomainsInPodTopologySpread:          feature.DefaultFeatureGate.Enabled(features.MinDomainsInPodTopologySpread),
        EnableNodeInclusionPolicyInPodTopologySpread: feature.DefaultFeatureGate.Enabled(features.NodeInclusionPolicyInPodTopologySpread),
    }

    return runtime.Registry{
        selectorspread.Name:                  selectorspread.New,
        imagelocality.Name:                   imagelocality.New,
        tainttoleration.Name:                 tainttoleration.New,
        nodename.Name:                        nodename.New,
        nodeports.Name:                       nodeports.New,
        nodeaffinity.Name:                    nodeaffinity.New,
        podtopologyspread.Name:               runtime.FactoryAdapter(fts, podtopologyspread.New),
        nodeunschedulable.Name:               nodeunschedulable.New,
        noderesources.Name:                   runtime.FactoryAdapter(fts, noderesources.NewFit),
        noderesources.BalancedAllocationName: runtime.FactoryAdapter(fts, noderesources.NewBalancedAllocation),
        volumebinding.Name:                   runtime.FactoryAdapter(fts, volumebinding.New),
        volumerestrictions.Name:              runtime.FactoryAdapter(fts, volumerestrictions.New),
        volumezone.Name:                      volumezone.New,
        nodevolumelimits.CSIName:             runtime.FactoryAdapter(fts, nodevolumelimits.NewCSI),
        nodevolumelimits.EBSName:             runtime.FactoryAdapter(fts, nodevolumelimits.NewEBS),
        nodevolumelimits.GCEPDName:           runtime.FactoryAdapter(fts, nodevolumelimits.NewGCEPD),
        nodevolumelimits.AzureDiskName:       runtime.FactoryAdapter(fts, nodevolumelimits.NewAzureDisk),
        nodevolumelimits.CinderName:          runtime.FactoryAdapter(fts, nodevolumelimits.NewCinder),
        interpodaffinity.Name:                interpodaffinity.New,
        queuesort.Name:                       queuesort.New,
        defaultbinder.Name:                   defaultbinder.New,
        defaultpreemption.Name:               runtime.FactoryAdapter(fts, defaultpreemption.New),
    }
}
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这里插入一个题外话,关于 in-tree plugin

在这里没有找到关于,kube-scheduler ,只是找到有关的概念,大概可以解释为,in-tree表示为随kubernetes官方提供的二进制构建的 plugin 则为 in-tree,而独立于kubernetes代码库之外的为 out-of-tree [3] 。这种情况下,可以理解为,AA则是 out-of-treePod, DeplymentSet 等是 in-tree

接下来回到初始化 scheduler ,在初始化一个 scheduler 时,会通过NewInTreeRegistry 来初始化

func New(client clientset.Interface,
    ....
    registry := frameworkplugins.NewInTreeRegistry()
    if err := registry.Merge(options.frameworkOutOfTreeRegistry); err != nil {
        return nil, err
    }

    ...

    profiles, err := profile.NewMap(options.profiles, registry, recorderFactory, stopCh,
        frameworkruntime.WithComponentConfigVersion(options.componentConfigVersion),
        frameworkruntime.WithClientSet(client),
        frameworkruntime.WithKubeConfig(options.kubeConfig),
        frameworkruntime.WithInformerFactory(informerFactory),
        frameworkruntime.WithSnapshotSharedLister(snapshot),
        frameworkruntime.WithPodNominator(nominator),
        frameworkruntime.WithCaptureProfile(frameworkruntime.CaptureProfile(options.frameworkCapturer)),
        frameworkruntime.WithClusterEventMap(clusterEventMap),
        frameworkruntime.WithParallelism(int(options.parallelism)),
        frameworkruntime.WithExtenders(extenders),
    )
    ...
}
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接下来在调度上下文 scheduleOneschedulePod 时,会通过 framework 调用对应的插件来处理这个扩展点工作。具体的体现在,pkg\scheduler\schedule_one.go 中的预选阶段

func (sched *Scheduler) schedulePod(ctx context.Context, fwk framework.Framework, state *framework.CycleState, pod *v1.Pod) (result ScheduleResult, err error) {
    trace := utiltrace.New("Scheduling", utiltrace.Field{Key: "namespace", Value: pod.Namespace}, utiltrace.Field{Key: "name", Value: pod.Name})
    defer trace.LogIfLong(100 * time.Millisecond)

    if err := sched.Cache.UpdateSnapshot(sched.nodeInfoSnapshot); err != nil {
        return result, err
    }
    trace.Step("Snapshotting scheduler cache and node infos done")

    if sched.nodeInfoSnapshot.NumNodes() == 0 {
        return result, ErrNoNodesAvailable
    }

    feasibleNodes, diagnosis, err := sched.findNodesThatFitPod(ctx, fwk, state, pod)
    if err != nil {
        return result, err
    }
    trace.Step("Computing predicates done")

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与其他扩展点部分,在调度上下文 scheduleOne 中可以很好的看出,功能都是 framework 提供的。

func (sched *Scheduler) scheduleOne(ctx context.Context) {

    ...

    scheduleResult, err := sched.SchedulePod(schedulingCycleCtx, fwk, state, pod)

    ...

    // Run the Reserve method of reserve plugins.
    if sts := fwk.RunReservePluginsReserve(schedulingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost); !sts.IsSuccess() {
    }

    ...

    // Run "permit" plugins.
    runPermitStatus := fwk.RunPermitPlugins(schedulingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

        // One of the plugins returned status different than success or wait.
        fwk.RunReservePluginsUnreserve(schedulingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

...

    // bind the pod to its host asynchronously (we can do this b/c of the assumption step above).
    go func() {
        ...
        waitOnPermitStatus := fwk.WaitOnPermit(bindingCycleCtx, assumedPod)
        if !waitOnPermitStatus.IsSuccess() {
            ...
            // trigger un-reserve plugins to clean up state associated with the reserved Pod
            fwk.RunReservePluginsUnreserve(bindingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)
        }

        // Run "prebind" plugins.
        preBindStatus := fwk.RunPreBindPlugins(bindingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

        ...

            // trigger un-reserve plugins to clean up state associated with the reserved Pod
            fwk.RunReservePluginsUnreserve(bindingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

        ...

        ...
            // trigger un-reserve plugins to clean up state associated with the reserved Pod
            fwk.RunReservePluginsUnreserve(bindingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

        ...

        // Run "postbind" plugins.
        fwk.RunPostBindPlugins(bindingCycleCtx, state, assumedPod, scheduleResult.SuggestedHost)

    ...
}
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插件 [4]

插件(Plugins)(也可以算是调度策略)在 kube-scheduler 中的实现为 framework plugin,插件API的实现分为两个步骤:registerconfigured,然后都实现了其父方法 Plugin。然后可以通过配置(kube-scheduler --config 提供)启动或禁用插件;除了默认插件外,还可以实现自定义调度插件与默认插件进行绑定。

type Plugin interface {
    Name() string
}
// sort扩展点
type QueueSortPlugin interface {
    Plugin
    Less(*v1.pod, *v1.pod) bool
}
// PreFilter扩展点
type PreFilterPlugin interface {
    Plugin
    PreFilter(context.Context, *framework.CycleState, *v1.pod) error
}

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插件的载入过程

scheduler 被启动时,会 scheduler.New(cc.Client.. 这个时候会传入 profiles,整个的流如下:

NewScheduler

我们了解如何 New 一个 scheduler 即为 Setup 中去配置这些参数,

func Setup(ctx context.Context, opts *options.Options, outOfTreeRegistryOptions ...Option) (*schedulerserverconfig.CompletedConfig, *scheduler.Scheduler, error) {

    ...

    // Create the scheduler.
    sched, err := scheduler.New(cc.Client,
        cc.InformerFactory,
        cc.DynInformerFactory,
        recorderFactory,
        ctx.Done(),
        scheduler.WithComponentConfigVersion(cc.ComponentConfig.TypeMeta.APIVersion),
        scheduler.WithKubeConfig(cc.KubeConfig),
        scheduler.WithProfiles(cc.ComponentConfig.Profiles...),
        scheduler.WithPercentageOfNodesToScore(cc.ComponentConfig.PercentageOfNodesToScore),
        scheduler.WithFrameworkOutOfTreeRegistry(outOfTreeRegistry),
        scheduler.WithPodMaxBackoffSeconds(cc.ComponentConfig.PodMaxBackoffSeconds),
        scheduler.WithPodInitialBackoffSeconds(cc.ComponentConfig.PodInitialBackoffSeconds),
        scheduler.WithPodMaxInUnschedulablePodsDuration(cc.PodMaxInUnschedulablePodsDuration),
        scheduler.WithExtenders(cc.ComponentConfig.Extenders...),
        scheduler.WithParallelism(cc.ComponentConfig.Parallelism),
        scheduler.WithBuildFrameworkCapturer(func(profile kubeschedulerconfig.KubeSchedulerProfile) {
            // Profiles are processed during Framework instantiation to set default plugins and configurations. Capturing them for logging
            completedProfiles = append(completedProfiles, profile)
        }),
    )
    ...
}
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profile.NewMap

scheduler.New 中,会根据配置生成profile,而 profile.NewMap 会完成这一步

func New(client clientset.Interface,
    ...

    clusterEventMap := make(map[framework.ClusterEvent]sets.String)

    profiles, err := profile.NewMap(options.profiles, registry, recorderFactory, stopCh,
        frameworkruntime.WithComponentConfigVersion(options.componentConfigVersion),
        frameworkruntime.WithClientSet(client),
        frameworkruntime.WithKubeConfig(options.kubeConfig),
        frameworkruntime.WithInformerFactory(informerFactory),
        frameworkruntime.WithSnapshotSharedLister(snapshot),
        frameworkruntime.WithPodNominator(nominator),
        frameworkruntime.WithCaptureProfile(frameworkruntime.CaptureProfile(options.frameworkCapturer)),
        frameworkruntime.WithClusterEventMap(clusterEventMap),
        frameworkruntime.WithParallelism(int(options.parallelism)),
        frameworkruntime.WithExtenders(extenders),
    )

         ...
}
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NewFramework

newProfile 返回的则是一个创建好的 framework

func newProfile(cfg config.KubeSchedulerProfile, r frameworkruntime.Registry, recorderFact RecorderFactory,
    stopCh <-chan struct{}, opts ...frameworkruntime.Option) (framework.Framework, error) {
    recorder := recorderFact(cfg.SchedulerName)
    opts = append(opts, frameworkruntime.WithEventRecorder(recorder))
    return frameworkruntime.NewFramework(r, &cfg, stopCh, opts...)
}
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最终会走到 pluginsNeeded,这里会根据配置中开启的插件而返回一个插件集,这个就是最终在每个扩展点中要执行的插件。

func (f *frameworkImpl) pluginsNeeded(plugins *config.Plugins) sets.String {
    pgSet := sets.String{}

    if plugins == nil {
        return pgSet
    }

    find := func(pgs *config.PluginSet) {
        for _, pg := range pgs.Enabled {
            pgSet.Insert(pg.Name)
        }
    }
    // 获取到所有的扩展点,找到为Enabled的插件加入到pgSet
    for _, e := range f.getExtensionPoints(plugins) {
        find(e.plugins)
    }
    // Parse MultiPoint separately since they are not returned by f.getExtensionPoints()
    find(&plugins.MultiPoint)

    return pgSet
}
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插件的执行

在对插件源码部分分析,会找几个典型的插件进行分析,而不会对全部的进行分析,因为总的来说是大同小异,分析的插件有 NodePortsNodeResourcesFitpodtopologyspread

NodePorts

这里以一个简单的插件来分析;NodePorts 插件用于检查Pod请求的端口,在节点上是否为空闲端口。

NodePorts 实现了 FilterPluginPreFilterPlugin

PreFilter 将会被 frameworkPreFilter 扩展点被调用。

func (pl *NodePorts) PreFilter(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod) (*framework.PreFilterResult, *framework.Status) {
    s := getContainerPorts(pod) // 或得Pod得端口
    // 写入状态
    cycleState.Write(preFilterStateKey, preFilterState(s))
    return nil, nil
}
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Filter 将会被 frameworkFilter 扩展点被调用。

// Filter invoked at the filter extension point.
func (pl *NodePorts) Filter(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod, nodeInfo *framework.NodeInfo) *framework.Status {
   wantPorts, err := getPreFilterState(cycleState)
   if err != nil {
      return framework.AsStatus(err)
   }

   fits := fitsPorts(wantPorts, nodeInfo)
   if !fits {
      return framework.NewStatus(framework.Unschedulable, ErrReason)
   }

   return nil
}

func fitsPorts(wantPorts []*v1.ContainerPort, nodeInfo *framework.NodeInfo) bool {
    // 对比existingPorts 和 wantPorts是否冲突,冲突则调度失败
    existingPorts := nodeInfo.UsedPorts
    for _, cp := range wantPorts {
        if existingPorts.CheckConflict(cp.HostIP, string(cp.Protocol), cp.HostPort) {
            return false
        }
    }
    return true
}
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New ,初始化新插件,在 register 中注册得

func New(_ runtime.Object, _ framework.Handle) (framework.Plugin, error) {
    return &NodePorts{}, nil
}
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在调用中,如果有任何一个插件返回错误,则跳过该扩展点注册得其他插件,返回失败。

func (f *frameworkImpl) RunFilterPlugins(
    ctx context.Context,
    state *framework.CycleState,
    pod *v1.Pod,
    nodeInfo *framework.NodeInfo,
) framework.PluginToStatus {
    statuses := make(framework.PluginToStatus)
    for _, pl := range f.filterPlugins {
        pluginStatus := f.runFilterPlugin(ctx, pl, state, pod, nodeInfo)
        if !pluginStatus.IsSuccess() {
            if !pluginStatus.IsUnschedulable() 
                errStatus := framework.AsStatus(fmt.Errorf("running %q filter plugin: %w", pl.Name(), pluginStatus.AsError())).WithFailedPlugin(pl.Name())
                return map[string]*framework.Status{pl.Name(): errStatus}
            }
            pluginStatus.SetFailedPlugin(pl.Name())
            statuses[pl.Name()] = pluginStatus
        }
    }

    return statuses
}
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返回得状态是一个 Status 结构体,该结构体表示了插件运行的结果。由 Codereasons、(可选)errfailedPlugin (失败的那个插件名)组成。当 code 不是 Success 时,应说明原因。而且,当 codeSuccess 时,其他所有字段都应为空。nil 状态也被视为成功。

type Status struct {
    code    Code
    reasons []string
    err     error
    // failedPlugin is an optional field that records the plugin name a Pod failed by.
    // It's set by the framework when code is Error, Unschedulable or UnschedulableAndUnresolvable.
    failedPlugin string
}
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NodeResourcesFit [5]

NodeResourcesFit 扩展检查节点是否拥有 Pod 请求的所有资源。分数可以使用以下三种策略之一,扩展点为:preFilterfilterscore

  • LeastAllocated (默认)
  • MostAllocated
  • RequestedToCapacityRatio

Fit

NodeResourcesFit PreFilter 可以看到调用得 computePodResourceRequest

// PreFilter invoked at the prefilter extension point.
func (f *Fit) PreFilter(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod) (*framework.PreFilterResult, *framework.Status) {
   cycleState.Write(preFilterStateKey, computePodResourceRequest(pod))
   return nil, nil
}
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computePodResourceRequest 这里有一个注释,总体解释起来是这样得:computePodResourceRequest ,返回值( framework.Resource)覆盖了每一个维度中资源的最大宽度。因为将按照 init-containers , containers 得顺序运行,会通过迭代方式收集每个维度中的最大值。计算时会对常规容器的资源向量求和,因为containers 运行会同时运行多个容器。计算示例为:

Pod:
  InitContainers
    IC1:
      CPU: 2
      Memory: 1G
    IC2:
      CPU: 2
      Memory: 3G
  Containers
    C1:
      CPU: 2
      Memory: 1G
    C2:
      CPU: 1
      Memory: 1G
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在维度1中(InitContainers)所需资源最大值时,CPU=2, Memory=3G;而维度2(Containers)所需资源最大值为:CPU=2, Memory=1G;那么最终结果为 CPU=3, Memory=3G,因为在维度1,最大资源时Memory=3G;而维度2最大资源是CPU=1+2, Memory=1+1,取每个维度中最大资源最大宽度即为 CPU=3, Memory=3G。

下面则看下代码得实现

func computePodResourceRequest(pod *v1.Pod) *preFilterState {
    result := &preFilterState{}
    for _, container := range pod.Spec.Containers {
        result.Add(container.Resources.Requests)
    }

    // 取最大得资源
    for _, container := range pod.Spec.InitContainers {
        result.SetMaxResource(container.Resources.Requests)
    }

    // 如果Overhead正在使用,需要将其计算到总资源中
    if pod.Spec.Overhead != nil {
        result.Add(pod.Spec.Overhead)
    }
    return result
}

// SetMaxResource 是比较ResourceList并为每个资源取最大值。
func (r *Resource) SetMaxResource(rl v1.ResourceList) {
    if r == nil {
        return
    }

    for rName, rQuantity := range rl {
        switch rName {
        case v1.ResourceMemory:
            r.Memory = max(r.Memory, rQuantity.Value())
        case v1.ResourceCPU:
            r.MilliCPU = max(r.MilliCPU, rQuantity.MilliValue())
        case v1.ResourceEphemeralStorage:
            if utilfeature.DefaultFeatureGate.Enabled(features.LocalStorageCapacityIsolation) {
                r.EphemeralStorage = max(r.EphemeralStorage, rQuantity.Value())
            }
        default:
            if schedutil.IsScalarResourceName(rName) {
                r.SetScalar(rName, max(r.ScalarResources[rName], rQuantity.Value()))
            }
        }
    }
}
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leastAllocate

LeastAllocated 是 NodeResourcesFit 的打分策略 ,LeastAllocated 打分的标准是更偏向于请求资源较少的Node。将会先计算出Node上调度的pod请求的内存、CPU与其他资源的百分比,然后并根据请求的比例与容量的平均值的最小值进行优先级排序。

计算公式是这样的:$\frac{\frac{cpu((capacity-requested) \times MaxNodeScore \times cpuWeight)}{capacity} + \frac{memory((capacity-requested) \times MaxNodeScore \times memoryWeight}{capacity}) + ...}{weightSum}$

下面来看下实现

func leastResourceScorer(resToWeightMap resourceToWeightMap) func(resourceToValueMap, resourceToValueMap) int64 {
    return func(requested, allocable resourceToValueMap) int64 {
        var nodeScore, weightSum int64
        for resource := range requested {
            weight := resToWeightMap[resource]
            //  计算出的资源分数乘weight
            resourceScore := leastRequestedScore(requested[resource], allocable[resource])
            nodeScore += resourceScore * weight
            weightSum += weight
        }
        if weightSum == 0 {
            return 0
        }
        // 最终除weightSum
        return nodeScore / weightSum
    }
}
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leastRequestedScore 计算标准为未使用容量的计算范围为 0~MaxNodeScore,0 为最低优先级,MaxNodeScore 为最高优先级。未使用的资源越多,得分越高。

func leastRequestedScore(requested, capacity int64) int64 {
    if capacity == 0 {
        return 0
    }
    if requested > capacity {
        return 0
    }
    // 容量 - 请求的 x 预期值(100)/ 容量
    return ((capacity - requested) * int64(framework.MaxNodeScore)) / capacity
}
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Topology [6]

Concept

在对 podtopologyspread 插件进行分析前,先需要掌握Pod拓扑的概念。

Pod拓扑(Pod Topology)是Kubernetes Pod调度机制,可以将Pod分布在集群中不同 Zone ,以及用户自定义的各种拓扑域 (topology domains)。当有了拓扑域后,用户可以更高效的利用集群资源。

如何来解释拓扑域,首先需要提及为什么需要拓扑域,在集群有3个节点,并且当Pod副本数为2时,又不希望两个Pod在同一个Node上运行。在随着扩大Pod的规模,副本数扩展到到15个时,这时候最理想的方式是每个Node运行5个Pod,在这种背景下,用户希望对集群中Zone的安排为相似的副本数量,并且在集群存在部分问题时可以更好的自愈(也是按照相似的副本数量均匀的分布在Node上)。在这种情况下Kubernetes 提供了Pod 拓扑约束来解决这个问题。

定义一个Topology

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
spec:
  # Configure a topology spread constraint
  topologySpreadConstraints:
    - maxSkew: <integer> # 
      minDomains: <integer> # optional; alpha since v1.24
      topologyKey: <string>
      whenUnsatisfiable: <string>
      labelSelector: <object>
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参数的描述

  • maxSkew:Required,Pod分布不均的程度,并且数字必须大于零
    • whenUnsatisfiable: DoNotSchedule,则定义目标拓扑中匹配 pod 的数量与 全局最小值拓扑域中的标签选择器匹配的 pod 的最小数量maxSkew之间的最大允许差异。例如有 3 个 Zone,分别具有 2、4 和 5 个匹配的 pod,则全局最小值为 2
    • whenUnsatisfiable: ScheduleAnywayscheduler 会为减少倾斜的拓扑提供更高的优先级。
  • minDomains:optional,符合条件的域的最小数量。
    • 如果不指定该选项 minDomains,则约束的行为 minDomains: 1
    • minDomains必须大于 0。minDomainswhenUnsatisfiable 一起时为whenUnsatisfiable: DoNotSchedule
  • topologyKey:Node label的key,如果多个Node都使用了这个lable key那么 scheduler 将这些 Node 看作为相同的拓扑域。
  • whenUnsatisfiable:当 Pod 不满足分布的约束时,怎么去处理
    • DoNotSchedule(默认)不要调度。
    • ScheduleAnyway仍然调度它,同时优先考虑最小化倾斜节点
  • labelSelector:查找匹配的 Pod label选择器的node进行技术,以计算Pod如何分布在拓扑域中

对于拓扑域的理解

对于拓扑域,官方是这么说明的,假设有一个带有以下lable的 4 节点集群:

NAME    STATUS   ROLES    AGE     VERSION   LABELS
node1   Ready    <none>   4m26s   v1.16.0   node=node1,zone=zoneA
node2   Ready    <none>   3m58s   v1.16.0   node=node2,zone=zoneA
node3   Ready    <none>   3m17s   v1.16.0   node=node3,zone=zoneB
node4   Ready    <none>   2m43s   v1.16.0   node=node4,zone=zoneB
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那么集群拓扑如图:

image

图1:集群拓扑图

Source:https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/

假设一个 4 节点集群,其中 3个label被标记为foo: bar的 Pod 分别位于Node1、Node2 和 Node3:

image

图2:集群拓扑图

Source:https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/

这种情况下,新部署一个Pod,并希望新Pod与现有Pod跨 Zone均匀分布,资源清单文件如下:

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: k8s.gcr.io/pause:3.1
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这个清单对于拓扑域来说,topologyKey: zone 表示对Pod均匀分布仅应用于已标记的节点(如 foo: bar),将会跳过没有标签的节点(如zone: <any value>)。如果 scheduler 找不到满足约束的方法,whenUnsatisfiable: DoNotSchedule 设置的策略则是 scheduler 对新部署的Pod保持 Pendding

如果此时 scheduler 将新Pod 调度至 $Zone_A$,此时Pod分布在拓扑域间为 $[3,1]$ ,而 maxSkew 配置的值是1。此时倾斜值为 $Zone_A - Zone_B = 3-1=2$,不满足 maxSkew=1,故这个Pod只能被调度到 $Zone_B$。

此时Pod调度拓扑图为图3或图4

image

图3:集群拓扑图

Source:https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/

image

图4:集群拓扑图

Source:https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/

如果需要将Pod调度到 $Zone_A$ ,可以按照如下方式进行:

  • 修改 maxSkew=2
  • 修改 topologyKey: node 而不是 Zone ,这种模式下可以将 Pod 均匀分布在Node而不是Zone之间。
  • 修改 whenUnsatisfiable: DoNotSchedulewhenUnsatisfiable: ScheduleAnyway 确保新的Pod始终可被调度

下面再通过一个例子增强对拓扑域了解

多拓扑约束

设拥有一个 4 节点集群,其中 3 个现有 Pod 标记 foo: bar分别位于 node1node2node3

image

图5:集群拓扑图

Source:https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/

部署的资源清单如下:可以看出拓扑分布约束配置了多个

kind: Pod
apiVersion: v1
metadata:
  name: mypod
  labels:
    foo: bar
spec:
  topologySpreadConstraints:
  - maxSkew: 1
    topologyKey: zone
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  - maxSkew: 1
    topologyKey: node
    whenUnsatisfiable: DoNotSchedule
    labelSelector:
      matchLabels:
        foo: bar
  containers:
  - name: pause
    image: k8s.gcr.io/pause:3.1
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在这种情况下,为了匹配第一个约束条件,新Pod 只能放置在 $Zone_B$ ;而就第二个约束条件,新Pod只能调度到 node4。在这种配置多约束条件下, scheduler 只考虑满足所有约束的值,因此唯一有效的是 node4

如何为集群设置一个默认拓扑域约束

默认情况下,拓扑域约束也作 scheduler 的为 scheduler configurtion 中的一部分参数,这也意味着,可以通过profile为整个集群级别指定一个默认的拓扑域调度约束,

apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints:
            - maxSkew: 1
              topologyKey: topology.kubernetes.io/zone
              whenUnsatisfiable: ScheduleAnyway
          defaultingType: List
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默认约束策略

如果在没有配置集群级别的约束策略时,kube-scheduler 内部 topologyspread 插件提供了一个默认的拓扑约束策略,大致上如下列清单所示

defaultConstraints:
  - maxSkew: 3
    topologyKey: "kubernetes.io/hostname"
    whenUnsatisfiable: ScheduleAnyway
  - maxSkew: 5
    topologyKey: "topology.kubernetes.io/zone"
    whenUnsatisfiable: ScheduleAnyway
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上述清单中内容可以在 pkg\scheduler\framework\plugins\podtopologyspread\plugin.go

var systemDefaultConstraints = []v1.TopologySpreadConstraint{
    {
        TopologyKey:       v1.LabelHostname,
        WhenUnsatisfiable: v1.ScheduleAnyway,
        MaxSkew:           3,
    },
    {
        TopologyKey:       v1.LabelTopologyZone,
        WhenUnsatisfiable: v1.ScheduleAnyway,
        MaxSkew:           5,
    },
}
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可以通过在配置文件中留空,来禁用默认配置

  • defaultConstraints: []
  • defaultingType: List
apiVersion: kubescheduler.config.k8s.io/v1beta3
kind: KubeSchedulerConfiguration

profiles:
  - schedulerName: default-scheduler
    pluginConfig:
      - name: PodTopologySpread
        args:
          defaultConstraints: []
          defaultingType: List
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通过源码学习Topology

podtopologyspread 实现了4种扩展点方法,包含 filterscore

PreFilter

可以看到 PreFilter 的核心为 calPreFilterState

func (pl *PodTopologySpread) PreFilter(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod) (*framework.PreFilterResult, *framework.Status) {
    s, err := pl.calPreFilterState(ctx, pod)
    if err != nil {
        return nil, framework.AsStatus(err)
    }
    cycleState.Write(preFilterStateKey, s)
    return nil, nil
}
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calPreFilterState 主要功能是用在计算如何在拓扑域中分布Pod,首先看段代码时,需要掌握下属几个概念

func (pl *PodTopologySpread) calPreFilterState(ctx context.Context, pod *v1.Pod) (*preFilterState, error) {
    // 获取Node
    allNodes, err := pl.sharedLister.NodeInfos().List()
    if err != nil {
        return nil, fmt.Errorf("listing NodeInfos: %w", err)
    }
    var constraints []topologySpreadConstraint
    if len(pod.Spec.TopologySpreadConstraints) > 0 {
        // 这里会构建出TopologySpreadConstraints,因为约束是不确定的
        constraints, err = filterTopologySpreadConstraints(
            pod.Spec.TopologySpreadConstraints,
            v1.DoNotSchedule,
            pl.enableMinDomainsInPodTopologySpread,
            pl.enableNodeInclusionPolicyInPodTopologySpread,
        )
        if err != nil {
            return nil, fmt.Errorf("obtaining pod's hard topology spread constraints: %w", err)
        }
    } else {
        // buildDefaultConstraints使用".DefaultConstraints"与pod匹配的
        // service、replication controllers、replica sets 
        // 和stateful sets的选择器为pod构建一个约束。
        constraints, err = pl.buildDefaultConstraints(pod, v1.DoNotSchedule)
        if err != nil {
            return nil, fmt.Errorf("setting default hard topology spread constraints: %w", err)
        }
    }
    if len(constraints) == 0 { // 如果是空的,则返回空preFilterState
        return &preFilterState{}, nil
    }
    // 初始化一个 preFilterState 状态
    s := preFilterState{
        Constraints:          constraints,
        TpKeyToCriticalPaths: make(map[string]*criticalPaths, len(constraints)),
        TpPairToMatchNum:     make(map[topologyPair]int, sizeHeuristic(len(allNodes), constraints)),
    }
    // 根据node统计拓扑域数量
    tpCountsByNode := make([]map[topologyPair]int, len(allNodes))
    // 获取pod亲和度配置
    requiredNodeAffinity := nodeaffinity.GetRequiredNodeAffinity(pod)
    processNode := func(i int) {
        nodeInfo := allNodes[i]
        node := nodeInfo.Node()
        if node == nil {
            klog.ErrorS(nil, "Node not found")
            return
        }
        // 通过spreading去过滤node以用作filters,错误解析以向后兼容
        if !pl.enableNodeInclusionPolicyInPodTopologySpread {
            if match, _ := requiredNodeAffinity.Match(node); !match {
                return
            }
        }

        // 确保node的lable 包含topologyKeys定义的值
        if !nodeLabelsMatchSpreadConstraints(node.Labels, constraints) {
            return
        }

        tpCounts := make(map[topologyPair]int, len(constraints))
        for _, c := range constraints { // 对应的约束列表
            if pl.enableNodeInclusionPolicyInPodTopologySpread &&
                !c.matchNodeInclusionPolicies(pod, node, requiredNodeAffinity) {
                continue
            }
            // 构建出 topologyPair 以key value形式,
            // 通常情况下TopologyKey属于什么类型的拓扑
            //  node.Labels[c.TopologyKey] 则是属于这个拓扑中那个子域
            pair := topologyPair{key: c.TopologyKey, value: node.Labels[c.TopologyKey]}
            // 计算与标签选择器相匹配的pod有多少个
            count := countPodsMatchSelector(nodeInfo.Pods, c.Selector, pod.Namespace)
            tpCounts[pair] = count
        }
        tpCountsByNode[i] = tpCounts // 最终形成的拓扑结构
    }
    // 执行上面的定义的processNode,执行的数量就是node的数量
    pl.parallelizer.Until(ctx, len(allNodes), processNode)
    // 最后构建出 TpPairToMatchNum
    // 表示每个拓扑域中的每个子域各分布多少Pod,如图6所示
    for _, tpCounts := range tpCountsByNode {
        for tp, count := range tpCounts {
            s.TpPairToMatchNum[tp] += count
        }
    }
    if pl.enableMinDomainsInPodTopologySpread {
        // 根据状态进行构建 preFilterState
        s.TpKeyToDomainsNum = make(map[string]int, len(constraints))
        for tp := range s.TpPairToMatchNum {
            s.TpKeyToDomainsNum[tp.key]++
        }
    }

    // 计算最小匹配出的拓扑对
    for i := 0; i < len(constraints); i++ {
        key := constraints[i].TopologyKey
        s.TpKeyToCriticalPaths[key] = newCriticalPaths()
    }
    for pair, num := range s.TpPairToMatchNum {
        s.TpKeyToCriticalPaths[pair.key].update(pair.value, num)
    }

    return &s, nil // 返回的值则包含最小的分布
}
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preFilterState

// preFilterState 是在PreFilter处计算并在Filter处使用。
// 它结合了 “TpKeyToCriticalPaths” 和 “TpPairToMatchNum” 来表示:
//(1)在每个分布约束上匹配最少pod的criticalPaths。 
// (2) 在每个分布约束上匹配的pod的数量。
// “nil preFilterState” 则表示没有设置(在PreFilter阶段);
// empty “preFilterState”对象则表示它是一个合法的状态,并在PreFilter阶段设置。

type preFilterState struct {
    Constraints []topologySpreadConstraint

    // 这里记录2条关键路径而不是所有关键路径。 
    // criticalPaths[0].MatchNum 始终保存最小匹配数。 
    // criticalPaths[1].MatchNum 总是大于或等于criticalPaths[0].MatchNum,但不能保证是第二个最小匹配数。
    TpKeyToCriticalPaths map[string]*criticalPaths

    // TpKeyToDomainsNum 以 “topologyKey” 作为key ,并以zone的数量作为值。
    TpKeyToDomainsNum map[string]int

    // TpPairToMatchNum 以 “topologyPair作为key” ,并以匹配到pod的数量作为value。
    TpPairToMatchNum map[topologyPair]int
}
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criticalPaths

// [2]criticalPath能够工作的原因是基于当前抢占算法的实现,特别是以下两个事实
// 事实 1:只抢占同一节点上的Pod,而不是多个节点上的 Pod。
// 事实 2:每个节点在其抢占周期期间在“preFilterState”的单独副本上进行评估。如果我们计划转向更复杂的算法,例如“多个节点上的任意pod”时则需要重新考虑这种结构。
type criticalPaths [2]struct {
    // TopologyValue代表映射到拓扑键的拓扑值。
    TopologyValue string
    // MatchNum代表匹配到的pod数量
    MatchNum int
}
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单元测试中的测试案例,具有两个约束条件的场景,通过表格来解析如下:

Node列表与标签如下表:

Node Name 🏷️Lable-zone 🏷️Lable-node
node-a zone1 node-a
node-b zone1 node-b
node-x zone2 node-x
node-y zone2 node-y

Pod列表与标签如下表:

Pod Name Node 🏷️Label
p-a1 node-a foo:
p-a2 node-a foo:
p-b1 node-b foo:
p-y1 node-y foo:
p-y2 node-y foo:
p-y3 node-y foo:
p-y4 node-y foo:

对应的拓扑约束

spec:
  topologySpreadConstraints:
  - MaxSkew: 1
    TopologyKey: zone
    labelSelector:
      matchLabels:
        foo: bar
    MinDomains: 1
    NodeAffinityPolicy: Honor
    NodeTaintsPolicy: Ignore
  - MaxSkew: 1
    TopologyKey: node
    labelSelector:
      matchLabels:
        foo: bar
    MinDomains: 1
    NodeAffinityPolicy: Honor
    NodeTaintsPolicy: Ignore
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那么整个分布如下:

image

图6:具有两个场景的分布图

实现的测试代码如下

{
    name: "normal case with two spreadConstraints",
    pod: st.MakePod().Name("p").Label("foo", "").
    SpreadConstraint(1, "zone", v1.DoNotSchedule, fooSelector, nil, nil, nil).
    SpreadConstraint(1, "node", v1.DoNotSchedule, fooSelector, nil, nil, nil).
    Obj(),
    nodes: []*v1.Node{
        st.MakeNode().Name("node-a").Label("zone", "zone1").Label("node", "node-a").Obj(),
        st.MakeNode().Name("node-b").Label("zone", "zone1").Label("node", "node-b").Obj(),
        st.MakeNode().Name("node-x").Label("zone", "zone2").Label("node", "node-x").Obj(),
        st.MakeNode().Name("node-y").Label("zone", "zone2").Label("node", "node-y").Obj(),
    },
    existingPods: []*v1.Pod{
        st.MakePod().Name("p-a1").Node("node-a").Label("foo", "").Obj(),
        st.MakePod().Name("p-a2").Node("node-a").Label("foo", "").Obj(),
        st.MakePod().Name("p-b1").Node("node-b").Label("foo", "").Obj(),
        st.MakePod().Name("p-y1").Node("node-y").Label("foo", "").Obj(),
        st.MakePod().Name("p-y2").Node("node-y").Label("foo", "").Obj(),
        st.MakePod().Name("p-y3").Node("node-y").Label("foo", "").Obj(),
        st.MakePod().Name("p-y4").Node("node-y").Label("foo", "").Obj(),
    },
    want: &preFilterState{
        Constraints: []topologySpreadConstraint{
            {
                MaxSkew:            1,
                TopologyKey:        "zone",
                Selector:           mustConvertLabelSelectorAsSelector(t, fooSelector),
                MinDomains:         1,
                NodeAffinityPolicy: v1.NodeInclusionPolicyHonor,
                NodeTaintsPolicy:   v1.NodeInclusionPolicyIgnore,
            },
            {
                MaxSkew:            1,
                TopologyKey:        "node",
                Selector:           mustConvertLabelSelectorAsSelector(t, fooSelector),
                MinDomains:         1,
                NodeAffinityPolicy: v1.NodeInclusionPolicyHonor,
                NodeTaintsPolicy:   v1.NodeInclusionPolicyIgnore,
            },
        },
        TpKeyToCriticalPaths: map[string]*criticalPaths{
            "zone": {{"zone1", 3}, {"zone2", 4}},
            "node": {{"node-x", 0}, {"node-b", 1}},
        },
        for pair, num := range s.TpPairToMatchNum {
        s.TpKeyToCriticalPaths[pair.key].update(pair.value, num)
    }
        TpPairToMatchNum: map[topologyPair]int{
            {key: "zone", value: "zone1"}:  3,
            {key: "zone", value: "zone2"}:  4,
            {key: "node", value: "node-a"}: 2,
            {key: "node", value: "node-b"}: 1,
            {key: "node", value: "node-x"}: 0,
            {key: "node", value: "node-y"}: 4,
        },
    },
},
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update

update 函数实际上时用于计算 criticalPaths 中的第一位始终保持为是一个最小Pod匹配值

func (p *criticalPaths) update(tpVal string, num int) {
    // first verify if `tpVal` exists or not
    i := -1
    if tpVal == p[0].TopologyValue {
        i = 0
    } else if tpVal == p[1].TopologyValue {
        i = 1
    }

    if i >= 0 {
        // `tpVal` 表示已经存在
        p[i].MatchNum = num
        if p[0].MatchNum > p[1].MatchNum {
            // swap paths[0] and paths[1]
            p[0], p[1] = p[1], p[0]
        }
    } else {
        // `tpVal` 表示不存在,如一个新初始化的值
        // num对应子域分布的pod
        // 说明第一个元素不是最小的,则作为交换
        if num < p[0].MatchNum {
            // update paths[1] with paths[0]
            p[1] = p[0]
            // update paths[0]
            p[0].TopologyValue, p[0].MatchNum = tpVal, num
        } else if num < p[1].MatchNum {
            // 如果小于 paths[1],则更新它,永远保证元素0是最小,1是次小的
            p[1].TopologyValue, p[1].MatchNum = tpVal, num
        }
    }
}
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综合来讲 Prefilter 主要做的工作是。循环所有的节点,先根据 NodeAffinity 或者 NodeSelector 进行过滤,然后根据约束中定义的 topologyKeys (拓扑划分的依据) 来选择节点。

接下来会计算出每个拓扑域下的拓扑对(可以理解为子域)匹配的 Pod 数量,存入 TpPairToMatchNum 中,最后就是要把所有约束中匹配的 Pod 数量最小(第二小)匹配出来的路径(代码是这么定义的,理解上可以看作是分布图)放入 TpKeyToCriticalPaths 中保存起来。整个 preFilterState 保存下来传递到后续的 filter 插件中使用。

Filter

preFilter 中 最后的计算结果会保存在 CycleState

cycleState.Write(preFilterStateKey, s)
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Filter 主要是从 PreFilter 处理的过程中拿到状态 preFilterState,然后看下每个拓扑约束中的 MaxSkew 是否合法,具体的计算公式为:$matchNum + selfMatchNum - minMatchNum$

  • matchNum:Prefilter 中计算出的对应的拓扑分布数量,可以在Prefilter中参考对应的内容
    • if tpCount, ok := s.TpPairToMatchNum[pair]; ok {
  • selfMatchNum:匹配到label的数量,匹配到则是1,否则为0
  • minMatchNum:获的 Prefilter 中计算出来的最小匹配的值
func (pl *PodTopologySpread) Filter(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod, nodeInfo *framework.NodeInfo) *framework.Status {
    node := nodeInfo.Node()
    if node == nil {
        return framework.AsStatus(fmt.Errorf("node not found"))
    }
    // 拿到 prefilter处理的s,即preFilterState
    s, err := getPreFilterState(cycleState)
    if err != nil {
        return framework.AsStatus(err)
    }

    // 一个 空类型的 preFilterState是合法的,这种情况下将容忍每一个被调度的 Pod
    if len(s.Constraints) == 0 {
        return nil
    }

    podLabelSet := labels.Set(pod.Labels) // 设置标签
    for _, c := range s.Constraints { // 因为拓扑约束允许多个所以
        tpKey := c.TopologyKey
        tpVal, ok := node.Labels[c.TopologyKey]
        if !ok {
            klog.V(5).InfoS("Node doesn't have required label", "node", klog.KObj(node), "label", tpKey)
            return framework.NewStatus(framework.UnschedulableAndUnresolvable, ErrReasonNodeLabelNotMatch)
        }

        // 判断标准
        // 现有的匹配数量 + 子匹配(1|0) - 全局minimum <= maxSkew
        minMatchNum, err := s.minMatchNum(tpKey, c.MinDomains, pl.enableMinDomainsInPodTopologySpread)
        if err != nil {
            klog.ErrorS(err, "Internal error occurred while retrieving value precalculated in PreFilter", "topologyKey", tpKey, "paths", s.TpKeyToCriticalPaths)
            continue
        }

        selfMatchNum := 0
        if c.Selector.Matches(podLabelSet) {
            selfMatchNum = 1
        }

        pair := topologyPair{key: tpKey, value: tpVal}
        matchNum := 0
        if tpCount, ok := s.TpPairToMatchNum[pair]; ok {
            matchNum = tpCount
        }
        skew := matchNum + selfMatchNum - minMatchNum
        if skew > int(c.MaxSkew) {
            klog.V(5).InfoS("Node failed spreadConstraint: matchNum + selfMatchNum - minMatchNum > maxSkew", "node", klog.KObj(node), "topologyKey", tpKey, "matchNum", matchNum, "selfMatchNum", selfMatchNum, "minMatchNum", minMatchNum, "maxSkew", c.MaxSkew)
            return framework.NewStatus(framework.Unschedulable, ErrReasonConstraintsNotMatch)
        }
    }

    return nil
}
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minMatchNum

// minMatchNum用于计算 倾斜的全局最小值,同时考虑 MinDomains。
func (s *preFilterState) minMatchNum(tpKey string, minDomains int32, enableMinDomainsInPodTopologySpread bool) (int, error) {
    paths, ok := s.TpKeyToCriticalPaths[tpKey]
    if !ok {
        return 0, fmt.Errorf("failed to retrieve path by topology key")
    }
    // 通常来说最小值是第一个
    minMatchNum := paths[0].MatchNum
    if !enableMinDomainsInPodTopologySpread { // 就是plugin的配置的 enableMinDomainsInPodTopologySpread
        return minMatchNum, nil
    }

    domainsNum, ok := s.TpKeyToDomainsNum[tpKey]
    if !ok {
        return 0, fmt.Errorf("failed to retrieve the number of domains by topology key")
    }

    if domainsNum < int(minDomains) {
        // 当有匹配拓扑键的符合条件的域的数量小于 配置的"minDomains"(每个约束条件的这个配置) 时,
        //它将全局“minimum” 设置为0。
        // 因为minimum默认就为1,如果他小于1,就让他为0
        minMatchNum = 0
    }

    return minMatchNum, nil
}
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PreScore

与 Filter 类似, PreScore 也是类似 PreFilter 的构成。 initPreScoreState 来完成过滤。

有了 PreFilter 基础后,对于 Score 来说大同小异

func (pl *PodTopologySpread) PreScore(
    ctx context.Context,
    cycleState *framework.CycleState,
    pod *v1.Pod,
    filteredNodes []*v1.Node,
) *framework.Status {
    allNodes, err := pl.sharedLister.NodeInfos().List()
    if err != nil {
        return framework.AsStatus(fmt.Errorf("getting all nodes: %w", err))
    }

    if len(filteredNodes) == 0 || len(allNodes) == 0 {
        // No nodes to score.
        return nil
    }

    state := &preScoreState{
        IgnoredNodes:            sets.NewString(),
        TopologyPairToPodCounts: make(map[topologyPair]*int64),
    }
    // Only require that nodes have all the topology labels if using
    // non-system-default spreading rules. This allows nodes that don't have a
    // zone label to still have hostname spreading.
    // 如果使用非系统默认分布规则,则仅要求节点具有所有拓扑标签。
    // 这将允许没有zone标签的节点仍然具有hostname分布。
    requireAllTopologies := len(pod.Spec.TopologySpreadConstraints) > 0 || !pl.systemDefaulted
    err = pl.initPreScoreState(state, pod, filteredNodes, requireAllTopologies)
    if err != nil {
        return framework.AsStatus(fmt.Errorf("calculating preScoreState: %w", err))
    }

    // return if incoming pod doesn't have soft topology spread Constraints.
    if len(state.Constraints) == 0 {
        cycleState.Write(preScoreStateKey, state)
        return nil
    }

    // Ignore parsing errors for backwards compatibility.
    requiredNodeAffinity := nodeaffinity.GetRequiredNodeAffinity(pod)
    processAllNode := func(i int) {
        nodeInfo := allNodes[i]
        node := nodeInfo.Node()
        if node == nil {
            return
        }

        if !pl.enableNodeInclusionPolicyInPodTopologySpread {
            // `node` should satisfy incoming pod's NodeSelector/NodeAffinity
            if match, _ := requiredNodeAffinity.Match(node); !match {
                return
            }
        }

        // All topologyKeys need to be present in `node`
        if requireAllTopologies && !nodeLabelsMatchSpreadConstraints(node.Labels, state.Constraints) {
            return
        }

        for _, c := range state.Constraints {
            if pl.enableNodeInclusionPolicyInPodTopologySpread &&
                !c.matchNodeInclusionPolicies(pod, node, requiredNodeAffinity) {
                continue
            }

            pair := topologyPair{key: c.TopologyKey, value: node.Labels[c.TopologyKey]}
            // If current topology pair is not associated with any candidate node,
            // continue to avoid unnecessary calculation.
            // Per-node counts are also skipped, as they are done during Score.
            tpCount := state.TopologyPairToPodCounts[pair]
            if tpCount == nil {
                continue
            }
            count := countPodsMatchSelector(nodeInfo.Pods, c.Selector, pod.Namespace)
            atomic.AddInt64(tpCount, int64(count))
        }
    }
    pl.parallelizer.Until(ctx, len(allNodes), processAllNode)
    // 保存状态给后面sorce调用
    cycleState.Write(preScoreStateKey, state)
    return nil
}
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与Filter中Update使用的函数一样,这里也会到这一步,这里会构建出TopologySpreadConstraints,因为约束是不确定的

func filterTopologySpreadConstraints(constraints []v1.TopologySpreadConstraint, action v1.UnsatisfiableConstraintAction, enableMinDomainsInPodTopologySpread, enableNodeInclusionPolicyInPodTopologySpread bool) ([]topologySpreadConstraint, error) {
    var result []topologySpreadConstraint
    for _, c := range constraints {
        if c.WhenUnsatisfiable == action { // 始终调度时
            selector, err := metav1.LabelSelectorAsSelector(c.LabelSelector)
            if err != nil {
                return nil, err
            }
            tsc := topologySpreadConstraint{
                MaxSkew:            c.MaxSkew,
                TopologyKey:        c.TopologyKey,
                Selector:           selector,
                MinDomains:         1,                            // If MinDomains is nil, we treat MinDomains as 1.
                NodeAffinityPolicy: v1.NodeInclusionPolicyHonor,  // If NodeAffinityPolicy is nil, we treat NodeAffinityPolicy as "Honor".
                NodeTaintsPolicy:   v1.NodeInclusionPolicyIgnore, // If NodeTaintsPolicy is nil, we treat NodeTaintsPolicy as "Ignore".
            }
            if enableMinDomainsInPodTopologySpread && c.MinDomains != nil {
                tsc.MinDomains = *c.MinDomains
            }
            if enableNodeInclusionPolicyInPodTopologySpread {
                if c.NodeAffinityPolicy != nil {
                    tsc.NodeAffinityPolicy = *c.NodeAffinityPolicy
                }
                if c.NodeTaintsPolicy != nil {
                    tsc.NodeTaintsPolicy = *c.NodeTaintsPolicy
                }
            }
            result = append(result, tsc)
        }
    }
    return result, nil
}
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Score

// 在分数扩展点调用分数。该函数返回的“score”是 `nodeName` 上匹配的 pod 数量,稍后会进行归一化。
func (pl *PodTopologySpread) Score(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod, nodeName string) (int64, *framework.Status) {
    nodeInfo, err := pl.sharedLister.NodeInfos().Get(nodeName)
    if err != nil {
        return 0, framework.AsStatus(fmt.Errorf("getting node %q from Snapshot: %w", nodeName, err))
    }

    node := nodeInfo.Node()
    s, err := getPreScoreState(cycleState)
    if err != nil {
        return 0, framework.AsStatus(err)
    }

    // Return if the node is not qualified.
    if s.IgnoredNodes.Has(node.Name) {
        return 0, nil
    }

    // 对于每个当前的 <pair>,当前节点获得 <matchSum> 的信用分。
    // 计算 <matchSum>总和 并将其作为该节点的分数返回。
    var score float64
    for i, c := range s.Constraints {
        if tpVal, ok := node.Labels[c.TopologyKey]; ok {
            var cnt int64
            if c.TopologyKey == v1.LabelHostname {
                cnt = int64(countPodsMatchSelector(nodeInfo.Pods, c.Selector, pod.Namespace))
            } else {
                pair := topologyPair{key: c.TopologyKey, value: tpVal}
                cnt = *s.TopologyPairToPodCounts[pair]
            }
            score += scoreForCount(cnt, c.MaxSkew, s.TopologyNormalizingWeight[i])
        }
    }
    return int64(math.Round(score)), nil
}
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Framework 中会运行 ScoreExtension ,即 NormalizeScore

// Run NormalizeScore method for each ScorePlugin in parallel.
f.Parallelizer().Until(ctx, len(f.scorePlugins), func(index int) {
    pl := f.scorePlugins[index]
    nodeScoreList := pluginToNodeScores[pl.Name()]
    if pl.ScoreExtensions() == nil {
        return
    }
    status := f.runScoreExtension(ctx, pl, state, pod, nodeScoreList)
    if !status.IsSuccess() {
        err := fmt.Errorf("plugin %q failed with: %w", pl.Name(), status.AsError())
        errCh.SendErrorWithCancel(err, cancel)
        return
    }
})
if err := errCh.ReceiveError(); err != nil {
    return nil, framework.AsStatus(fmt.Errorf("running Normalize on Score plugins: %w", err))
}
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NormalizeScore 会为所有的node根据之前计算出的权重进行打分

func (pl *PodTopologySpread) NormalizeScore(ctx context.Context, cycleState *framework.CycleState, pod *v1.Pod, scores framework.NodeScoreList) *framework.Status {
    s, err := getPreScoreState(cycleState)
    if err != nil {
        return framework.AsStatus(err)
    }
    if s == nil {
        return nil
    }

    // 计算 <minScore> 和 <maxScore>
    var minScore int64 = math.MaxInt64
    var maxScore int64
    for i, score := range scores {
        // it's mandatory to check if <score.Name> is present in m.IgnoredNodes
        if s.IgnoredNodes.Has(score.Name) {
            scores[i].Score = invalidScore
            continue
        }
        if score.Score < minScore {
            minScore = score.Score
        }
        if score.Score > maxScore {
            maxScore = score.Score
        }
    }

    for i := range scores {
        if scores[i].Score == invalidScore {
            scores[i].Score = 0
            continue
        }
        if maxScore == 0 {
            scores[i].Score = framework.MaxNodeScore
            continue
        }
        s := scores[i].Score
        scores[i].Score = framework.MaxNodeScore * (maxScore + minScore - s) / maxScore
    }
    return nil
}
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到此,对于pod拓扑插件功能大概可以明了了,

  • Filter 部分(PreFilterFilter)完成拓扑对(Topology Pair)划分
  • Score部分(PreScore, Score , NormalizeScore )主要是对拓扑对(可以理解为拓扑结构划分)来选择一个最适合的pod的节点(即分数最优的节点)

而在 scoring_test.go 给了很多用例,可以更深入的了解这部分算法

Reference

[1] scheduling code hierarchy

[2] scheduler algorithm

[3] in tree VS out of tree volume plugins

[4] scheduler_framework_plugins

[5] scheduling config

[6] topology spread constraints

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