Kubernetes源码学习-Scheduler-P5-Pod优先级抢占调度

ゝ一世哀愁。 2024-04-18 07:54 142阅读 0赞

# P5-Pod优先级抢占调度 #

## 1. 前言 ##

**K8s源码分析完整系列一键直达：[k8s源码笔记-项目地址][k8s_-]，若有帮助，欢迎star，谢谢~**

前面的两篇文章中，已经讲过了调度pod的算法(predicate/priority)，在kubernetes v1.8版本之后可以指定pod优先级(v1alpha1)，若资源不足导致高优先级pod匹配失败，高优先级pod会转而将部分低优先级pod驱逐，以抢占低优先级pod的资源尽力保障自身能够调度成功，那么本篇就从代码的层面展开看一看pod抢占调度的逻辑。

## 2. 抢占调度入口 ##

在P1-入口篇中我们找到了调度算法计算的入口，随后展开了调度算法的两篇解读，本篇我们再次回到此入口的位置，接着往下看:

`pkg/scheduler/scheduler.go:457`

func (sched *Scheduler) scheduleOne() { 
    	... // 省略
      
      // 调度算法计算入口
    	scheduleResult, err := sched.schedule(pod) 
      
    	if err != nil { 
    		// schedule() may have failed because the pod would not fit on any host, so we try to
    		// preempt, with the expectation that the next time the pod is tried for scheduling it
    		// will fit due to the preemption. It is also possible that a different pod will schedule
    		// into the resources that were preempted, but this is harmless.
    		if fitError, ok := err.(*core.FitError); ok { 
    			if !util.PodPriorityEnabled() || sched.config.DisablePreemption { 
    				klog.V(3).Infof("Pod priority feature is not enabled or preemption is disabled by scheduler configuration." +
    					" No preemption is performed.")
    			} else { 
    				preemptionStartTime := time.Now()
    				sched.preempt(pod, fitError)  // 抢占调度逻辑入口
    				metrics.PreemptionAttempts.Inc()
    			  ... // 省略
    			metrics.PodScheduleFailures.Inc()
    		} else { 
    			klog.Errorf("error selecting node for pod: %v", err)
    			metrics.PodScheduleErrors.Inc()
    		}
    		return
    	}

注释中可看出，若在筛选算法中并未找到fitNode且返回了fitError，那么就会进入基于pod优先级的资源抢占的逻辑，入口是`sched.preempt(pod, fitError)`函数。在展开抢占逻辑之前，我们先来看一看pod优先级是怎么一回事吧。

### 2.1. Pod优先级的定义 ###

字面意义上来理解，pod优先级可以在调度的时候为高优先级的pod提供资源空间保障，若出现资源紧张的情况，则在其他约束规则允许的情况下，高优先级pod会抢占低优先级pod的资源。此功能在1.11版本以后默认开启，默认情况下pod的优先级是0，优先级值high is better，具体说明来看看官方文档的解释吧:

[**Pod Priority and Preemption**][Pod Priority and Preemption]

下面列举一个pod优先级使用的实例：

# Example PriorityClass
    apiVersion: scheduling.k8s.io/v1
    kind: PriorityClass
    metadata:
      name: high-priority
    value: 1000000
    globalDefault: false
    description: "This priority class should be used for XYZ service pods only."
    
    # Example Pod spec
    apiVersion: v1
    kind: Pod
    metadata:
      name: nginx
      labels:
        env: test
    spec:
      containers:
      - name: nginx
        image: nginx
        imagePullPolicy: IfNotPresent
      priorityClassName: high-priority

了解了定义及如何使用，那我们来看看代码层面是如何实现的吧！

## 3. 抢占调度算法 ##

从上面的入口跳转:

`pkg/scheduler/scheduler.go:469` \--> `pkg/scheduler/scheduler.go:290`

func (sched *Scheduler) preempt(preemptor *v1.Pod, scheduleErr error) (string, error) { 
    	preemptor, err := sched.config.PodPreemptor.GetUpdatedPod(preemptor)
    	if err != nil { 
    		klog.Errorf("Error getting the updated preemptor pod object: %v", err)
    		return "", err
    	}
      // 通过默认注册的抢占算法，计算得出最终被执行抢占调度的node、node上需要驱逐的pod等信息
    	node, victims, nominatedPodsToClear, err := sched.config.Algorithm.Preempt(preemptor, sched.config.NodeLister, scheduleErr)
    	if err != nil { 
    		klog.Errorf("Error preempting victims to make room for %v/%v.", preemptor.Namespace, preemptor.Name)
    		return "", err
    	}
    	var nodeName = ""
    	if node != nil { 
    		nodeName = node.Name
    		// Update the scheduling queue with the nominated pod information. Without
    		// this, there would be a race condition between the next scheduling cycle
    		// and the time the scheduler receives a Pod Update for the nominated pod.
        // 给调度队列内的preemptor pod加上提名node信息，避免下一个调度周期出现冲突
    		sched.config.SchedulingQueue.UpdateNominatedPodForNode(preemptor, nodeName)
    
    		// Make a call to update nominated node name of the pod on the API server.
        // 给待调度pod指定NominatedNodeName，pod.Status.NominatedNodeName = nodeName
    		err = sched.config.PodPreemptor.SetNominatedNodeName(preemptor, nodeName)
    		if err != nil { 
    			klog.Errorf("Error in preemption process. Cannot update pod %v/%v annotations: %v", preemptor.Namespace, preemptor.Name, err)
    			sched.config.SchedulingQueue.DeleteNominatedPodIfExists(preemptor)
    			return "", err
    		}
    
    		for _, victim := range victims { 
          // 对node上需要驱逐的pod执行删除操作
    			if err := sched.config.PodPreemptor.DeletePod(victim); err != nil { 
    				klog.Errorf("Error preempting pod %v/%v: %v", victim.Namespace, victim.Name, err)
    				return "", err
    			}
    			sched.config.Recorder.Eventf(victim, v1.EventTypeNormal, "Preempted", "by %v/%v on node %v", preemptor.Namespace, preemptor.Name, nodeName)
    		}
    		metrics.PreemptionVictims.Set(float64(len(victims)))
    	}
    	// Clearing nominated pods should happen outside of "if node != nil". Node could
    	// be nil when a pod with nominated node name is eligible to preempt again,
    	// but preemption logic does not find any node for it. In that case Preempt()
    	// function of generic_scheduler.go returns the pod itself for removal of the annotation.
      // 当找不到合适的抢占node时，可能是因为preemptor pod已经有了提名的node，但它又执行了一遍抢占逻辑，说明它
      // 在上一次调度周期中没有调度成功，因此，删除调度队列中比当前preemptor pod优先级更低的pod所指定的提名
      // node信息(pod.Status.NominatedNodeName)
    	for _, p := range nominatedPodsToClear { 
    		rErr := sched.config.PodPreemptor.RemoveNominatedNodeName(p)
    		if rErr != nil { 
    			klog.Errorf("Cannot remove nominated node annotation of pod: %v", rErr)
    			// We do not return as this error is not critical.
    		}
    	}
    	return nodeName, err
    }

如优先级筛选算法一样，调度算法最终也是要挑选出一个供以实际运行抢占调度逻辑的node，那么一起来看看这个计算算法是怎么样的。如schedule()方法一样，preempt()的默认方法也在`generic_scheduler.go`这个文件中：

`pkg/scheduler/core/generic_scheduler.go:288`

将函数内拆成几个重要的部分，其余部分省略，逐个说明

func (g *genericScheduler) Preempt(pod *v1.Pod, nodeLister algorithm.NodeLister, scheduleErr error) (*v1.Node, []*v1.Pod, []*v1.Pod, error) { 
    	// ... 省略
      
      // 每次开始抢占调度之前，检查一下当前pod是否已经有了提名抢占调度的节点，且该节点上当前不包含正在终结中的pod，若pod已有提名调度节点，且该节点上已经有pod正在终结中，则视为已经在执行抢占的动作了，所以不再往下重复执行。可以自行进去查看，比较简单，不拿出来讲了。
    	if !podEligibleToPreemptOthers(pod, g.nodeInfoSnapshot.NodeInfoMap) { 
    		klog.V(5).Infof("Pod %v/%v is not eligible for more preemption.", pod.Namespace, pod.Name)
    		return nil, nil, nil, nil
    	}
      // ... 省略
      
      // potentialNodes，找出潜在的可能进行抢占调度的节点，下方详解
    	potentialNodes := nodesWherePreemptionMightHelp(allNodes, fitError.FailedPredicates)
    
      // ... 省略
      
      // pdb,pod Disruption Budget,是用来保障可用副本的一种功能，下方详解
    	pdbs, err := g.pdbLister.List(labels.Everything())
    	if err != nil { 
    		return nil, nil, nil, err
    	}
      
      // 最重要的抢占算法入口，下方详解
    	nodeToVictims, err := selectNodesForPreemption(pod, g.nodeInfoSnapshot.NodeInfoMap, potentialNodes, g.predicates,
    		g.predicateMetaProducer, g.schedulingQueue, pdbs)
    	if err != nil { 
    		return nil, nil, nil, err
    	}
    
    	// ... 省略
      
      // candidateNode，从所有提名的node中挑选一个真正执行抢占步骤
    	candidateNode := pickOneNodeForPreemption(nodeToVictims)
    	if candidateNode == nil { 
    		return nil, nil, nil, nil
    	}
    
    	// 返回3个值，分别是选中的node、node上将要驱逐的pod、调度队列中比当前pod优先级更低的pod
    	nominatedPods := g.getLowerPriorityNominatedPods(pod, candidateNode.Name)
    	if nodeInfo, ok := g.nodeInfoSnapshot.NodeInfoMap[candidateNode.Name]; ok { 
    		return nodeInfo.Node(), nodeToVictims[candidateNode].Pods, nominatedPods, nil
    	}
    
    	return nil, nil, nil, fmt.Errorf(
    		"preemption failed: the target node %s has been deleted from scheduler cache",
    		candidateNode.Name)
    }

### 3.1. potentialNodes ###

第一步，先找出所有潜在的可能会参与抢占调度的node,何为潜在可能呢？意思是node调度此pod调度失败的原因并非"硬伤"类原因。所谓硬伤原因，指的是即使驱逐调几个pod，也无法改变此node无法运行这个pod的事实。这些硬伤包括哪些？来看看代码：

`pkg/scheduler/core/generic_scheduler.go:306 -> pkg/scheduler/core/generic_scheduler.go:1082`

func nodesWherePreemptionMightHelp(nodes []*v1.Node, failedPredicatesMap FailedPredicateMap) []*v1.Node { 
    	potentialNodes := []*v1.Node{ }
    	for _, node := range nodes { 
    		unresolvableReasonExist := false
    		failedPredicates, _ := failedPredicatesMap[node.Name]
    		// If we assume that scheduler looks at all nodes and populates the failedPredicateMap
    		// (which is the case today), the !found case should never happen, but we'd prefer
    		// to rely less on such assumptions in the code when checking does not impose
    		// significant overhead.
    		// Also, we currently assume all failures returned by extender as resolvable.
    		for _, failedPredicate := range failedPredicates { 
    			switch failedPredicate { 
    			case
            // 下面所有的failedPredicates，都视为"硬伤"，因此若相应的节点上若出现下面的失败原因之一，则视为该node不可参与抢占调度。
    				predicates.ErrNodeSelectorNotMatch,
    				predicates.ErrPodAffinityRulesNotMatch,
    				predicates.ErrPodNotMatchHostName,
    				predicates.ErrTaintsTolerationsNotMatch,
    				predicates.ErrNodeLabelPresenceViolated,
    				// Node conditions won't change when scheduler simulates removal of preemption victims.
    				// So, it is pointless to try nodes that have not been able to host the pod due to node
    				// conditions. These include ErrNodeNotReady, ErrNodeUnderPIDPressure, ErrNodeUnderMemoryPressure, ....
    				predicates.ErrNodeNotReady,
    				predicates.ErrNodeNetworkUnavailable,
    				predicates.ErrNodeUnderDiskPressure,
    				predicates.ErrNodeUnderPIDPressure,
    				predicates.ErrNodeUnderMemoryPressure,
    				predicates.ErrNodeUnschedulable,
    				predicates.ErrNodeUnknownCondition,
    				predicates.ErrVolumeZoneConflict,
    				predicates.ErrVolumeNodeConflict,
    				predicates.ErrVolumeBindConflict:
    				unresolvableReasonExist = true
    				break
    			}
    		}
    		if !unresolvableReasonExist { 
    			klog.V(3).Infof("Node %v is a potential node for preemption.", node.Name)
    			potentialNodes = append(potentialNodes, node)
    		}
    	}
    	return potentialNodes
    }

### 3.2. Pod Disruption Budget(pdb) ###

这种资源类型本人没有实际应用过，查阅了一下官方的手册，实际上它也是kubernetes设计的一种抽象资源，主要用作面对主动中断时，保障副本可用数量的一种功能，与deployment的maxUnavailable不一样，maxUnavailable是在滚动更新(非主动中断)时用来保障，pdb通常是面对主动中断的场景，例如删除pod,drain node等主动操作，更多详细说明参考官方的手册:

[Specifying a Disruption Budget for your Application][]

资源实例:

apiVersion: policy/v1beta1
    kind: PodDisruptionBudget
    metadata:
      name: zk-pdb
    spec:
      minAvailable: 2
      selector:
        matchLabels:
          app: zookeeper

$ kubectl get poddisruptionbudgets
    NAME      MIN-AVAILABLE   ALLOWED-DISRUPTIONS   AGE
    zk-pdb    2               1                     7s
    
    
    $ kubectl get poddisruptionbudgets zk-pdb -o yaml
    apiVersion: policy/v1beta1
    kind: PodDisruptionBudget
    metadata:
      creationTimestamp: 2017-08-28T02:38:26Z
      generation: 1
      name: zk-pdb
    ...
    status:
      currentHealthy: 3
      desiredHealthy: 3
      disruptedPods: null
      disruptionsAllowed: 1
      expectedPods: 3
      observedGeneration: 1

为什么这个资源相关的逻辑会出现在抢占调度里面呢？因为设计者将pod抢占造成的低优先级pod驱逐动作视为主动中断，有了这一层理解，我们接着往下。

### 3.3. nodeToVictims ###

selectNodesForPreemption()函数很重要，这个函数将会返回所有可行的node驱逐方案

`pkg/scheduler/core/generic_scheduler.go:316` `selectNodesForPreemption` \--> `pkg/scheduler/core/generic_scheduler.go:916`

func selectNodesForPreemption(pod *v1.Pod,
    	nodeNameToInfo map[string]*schedulernodeinfo.NodeInfo,
    	potentialNodes []*v1.Node,
    	fitPredicates map[string]predicates.FitPredicate,
    	metadataProducer predicates.PredicateMetadataProducer,
    	queue internalqueue.SchedulingQueue,
    	pdbs []*policy.PodDisruptionBudget,
    ) (map[*v1.Node]*schedulerapi.Victims, error) { 
      // 返回的结构体，类型是map，key是*v1.Node，value是一个结构体，包含两个元素:node上待驱逐的pod信息和将会违反PDB规则的次数
    	nodeToVictims := map[*v1.Node]*schedulerapi.Victims{ }
    	var resultLock sync.Mutex
    
    	// We can use the same metadata producer for all nodes.
    	meta := metadataProducer(pod, nodeNameToInfo)
    	checkNode := func(i int) { 
    		nodeName := potentialNodes[i].Name
    		var metaCopy predicates.PredicateMetadata
    		if meta != nil { 
    			metaCopy = meta.ShallowCopy()
    		}
        // selectVictimsOnNode()是核心计算的函数
    		pods, numPDBViolations, fits := selectVictimsOnNode(pod, metaCopy, nodeNameToInfo[nodeName], fitPredicates, queue, pdbs)
    		if fits { 
    			resultLock.Lock()
    			victims := schedulerapi.Victims{ 
    				Pods:             pods,
    				NumPDBViolations: numPDBViolations,
    			}
    			nodeToVictims[potentialNodes[i]] = &victims
    			resultLock.Unlock()
    		}
    	}
      // 熟悉的并发控制模型
    	workqueue.ParallelizeUntil(context.TODO(), 16, len(potentialNodes), checkNode)
    	return nodeToVictims, nil
    }

上面已在代码中对重要部分进行注释，不难发现，重要的计算函数是selectVictimsOnNode()函数，每个node所需要驱逐的pod，以及违反PDB规则次数信息，都由此函数来计算返回，最终组成nodeToVictims这个map，返回给上层调用函数。所以，接着来看selectVictimsOnNode()函数是怎么运行的。

**selectVictimsOnNode**

func selectVictimsOnNode(
    	pod *v1.Pod,
    	meta predicates.PredicateMetadata,
    	nodeInfo *schedulernodeinfo.NodeInfo,
    	fitPredicates map[string]predicates.FitPredicate,
    	queue internalqueue.SchedulingQueue,
    	pdbs []*policy.PodDisruptionBudget,
    ) ([]*v1.Pod, int, bool) { 
    	if nodeInfo == nil { 
    		return nil, 0, false
    	}
      
      // 潜在的受害者(pod)，按优先级排序的有序list，高优先级的排序靠前，低优先级的排序靠后
    	potentialVictims := util.SortableList{ CompFunc: util.HigherPriorityPod}
      // 在实际调度之前，所有的资源的考量计算都只能是预估，因此不能实际实施到node上，所以，基于node的元数据进行一个复制，将node信息的复制样本来参与计算，最终计算得到正确的结果后才会考虑实际往node上实施。
    	nodeInfoCopy := nodeInfo.Clone()
      
      // 基于node复制样本，假设减去一个pod之后，复制样本重新计算得到的数据。例如node a上运行着有若干pod，假设减去了其上的pod1，pod1 request的内存是4Gi，那么可假设node可分配的内存就多了4Gi
    	removePod := func(rp *v1.Pod) { 
    		nodeInfoCopy.RemovePod(rp)
    		if meta != nil { 
    			meta.RemovePod(rp)
    		}
    	}
      
      // 基于node复制样本，假设加上一个pod之后，复制样本重新计算得到的数据。
    	addPod := func(ap *v1.Pod) { 
    		nodeInfoCopy.AddPod(ap)
    		if meta != nil { 
    			meta.AddPod(ap, nodeInfoCopy)
    		}
    	}
    
      // 首先，枚举出node上所有的低于待调度pod优先级的pod，并将它们加入潜在受害者potentialVictims，计算假设剔出它们后，node上现有的资源信息
    	podPriority := util.GetPodPriority(pod)
    	for _, p := range nodeInfoCopy.Pods() { 
    		if util.GetPodPriority(p) < podPriority { 
    			potentialVictims.Items = append(potentialVictims.Items, p)
    			removePod(p)
    		}
    	}
    	potentialVictims.Sort()
    
      // 第二步，判断待调度pod是否fit此node，主要是亲和性方面的考量，这个podFitsOnNode函数前面筛选算法已经讲过了，这里不再复述，这个函数通过后，会把待调度pod的request资源加入nodeInfoCopy内。
    	if fits, _, err := podFitsOnNode(pod, meta, nodeInfoCopy, fitPredicates, queue, false); !fits { 
    		if err != nil { 
    			klog.Warningf("Encountered error while selecting victims on node %v: %v", nodeInfo.Node().Name, err)
    		}
    		return nil, 0, false
    	}
    	var victims []*v1.Pod
    	numViolatingVictim := 0
      
      // 第三步，将前面枚举出的低优先级的pod有序list，拆分为两个有序list，一个是违反了PDB规则的(pdb.Status.PodDisruptionsAllowed <= 0,这个值等于0则代表理论上要求不能出现中断的pod副本)，一个是不违反PDB规则的。
    	violatingVictims, nonViolatingVictims := filterPodsWithPDBViolation(potentialVictims.Items, pdbs)
      
      // 第四步，前面枚举假设把所有的低优先级pod都剔除了，但实际上可能不用剔除这么多，因此，保证了待调度pod计算进来之后，这里再用贪心法将低优先级的pod按优先级排序尽可能多地加入回来，最终无法调度的pod，才归为实际驱逐的pod。显而易见的是，优先保障有PDB约束的pod。
    	reprievePod := func(p *v1.Pod) bool { 
    		addPod(p)
    		fits, _, _ := podFitsOnNode(pod, meta, nodeInfoCopy, fitPredicates, queue, false)
    		if !fits { 
    			removePod(p)
    			victims = append(victims, p)
    			klog.V(5).Infof("Pod %v/%v is a potential preemption victim on node %v.", p.Namespace, p.Name, nodeInfo.Node().Name)
    		}
    		return fits
    	}
    	for _, p := range violatingVictims { 
    		if !reprievePod(p) { 
    			numViolatingVictim++
    		}
    	}
    	// Now we try to reprieve non-violating victims.
    	for _, p := range nonViolatingVictims { 
    		reprievePod(p)
    	}
      // 第五步，返回最终node的运算结果，分别是驱逐的pod list，以及驱逐的数量
    	return victims, numViolatingVictim, true
    }

这个函数分5步，先是枚举出所有的低优先级pod，再贪心保障尽量多的pod能正常运行，从而计算出最终需要被驱逐的pod及相关信息，详见代码内注释。

### 3.4. candidateNode ###

上面函数返回每一个可抢占的node各自的抢占方案后，这里就需要筛选其中一个node来实际执行抢占调度操作。

`pkg/scheduler/core/generic_scheduler.go:330 pickOneNodeForPreemption()` \--> `pkg/scheduler/core/generic_scheduler.go:809`

func pickOneNodeForPreemption(nodesToVictims map[*v1.Node]*schedulerapi.Victims) *v1.Node { 
    	if len(nodesToVictims) == 0 { 
    		return nil
    	}
    	minNumPDBViolatingPods := math.MaxInt32
    	var minNodes1 []*v1.Node
    	lenNodes1 := 0
    	for node, victims := range nodesToVictims { 
    		if len(victims.Pods) == 0 { 
    		  // 可能在调度的过程中，有极小的概率某个node上有pod终结了，使node上不再有需要驱逐的pod，那么pod可直接调度到该node上
    			return node
    		}
    		// 按违反PDB约束的次数排序，越少的node优先级越高，若最大优先级的node只有一个，则直接返回违反次数最小的node，若有多个，则进入下一步筛选
    		numPDBViolatingPods := victims.NumPDBViolations
    		if numPDBViolatingPods < minNumPDBViolatingPods { 
    			minNumPDBViolatingPods = numPDBViolatingPods
    			minNodes1 = nil
    			lenNodes1 = 0
    		}
    		if numPDBViolatingPods == minNumPDBViolatingPods { 
    			minNodes1 = append(minNodes1, node)
    			lenNodes1++
    		}
    	}
    	if lenNodes1 == 1 { 
    		return minNodes1[0]
    	}
    
    	// 按node上需驱逐的第一个pod(即需驱逐的优先级最高的pod)的优先级大小排序，pod[0]优先级越小，则所属的node优先级越高，若最大优先级的node只有一个，则直接返回此node，若有多个，则进入下一步筛选
    	minHighestPriority := int32(math.MaxInt32)
    	var minNodes2 = make([]*v1.Node, lenNodes1)
    	lenNodes2 := 0
    	for i := 0; i < lenNodes1; i++ { 
    		node := minNodes1[i]
    		victims := nodesToVictims[node]
    		// highestPodPriority is the highest priority among the victims on this node.
    		highestPodPriority := util.GetPodPriority(victims.Pods[0])
    		if highestPodPriority < minHighestPriority { 
    			minHighestPriority = highestPodPriority
    			lenNodes2 = 0
    		}
    		if highestPodPriority == minHighestPriority { 
    			minNodes2[lenNodes2] = node
    			lenNodes2++
    		}
    	}
    	if lenNodes2 == 1 { 
    		return minNodes2[0]
    	}
    
    	// 按node上需驱逐的所有的pod的优先级总和计算，总和越小，node优先级越高，若最大优先级的node只有一个，则直接返回此node，若有多个，则进入下一步筛选
    	minSumPriorities := int64(math.MaxInt64)
    	lenNodes1 = 0
    	for i := 0; i < lenNodes2; i++ { 
    		var sumPriorities int64
    		node := minNodes2[i]
    		for _, pod := range nodesToVictims[node].Pods { 
    			// We add MaxInt32+1 to all priorities to make all of them >= 0. This is
    			// needed so that a node with a few pods with negative priority is not
    			// picked over a node with a smaller number of pods with the same negative
    			// priority (and similar scenarios).
    			sumPriorities += int64(util.GetPodPriority(pod)) + int64(math.MaxInt32+1)
    		}
    		if sumPriorities < minSumPriorities { 
    			minSumPriorities = sumPriorities
    			lenNodes1 = 0
    		}
    		if sumPriorities == minSumPriorities { 
    			minNodes1[lenNodes1] = node
    			lenNodes1++
    		}
    	}
    	if lenNodes1 == 1 { 
    		return minNodes1[0]
    	}
    
    	// 按node上需驱逐的所有的pod数量计算，数量越少，node优先级越高，若最大优先级的node只有一个，则直接返回此node，若有多个，则进入下一步筛选
    	minNumPods := math.MaxInt32
    	lenNodes2 = 0
    	for i := 0; i < lenNodes1; i++ { 
    		node := minNodes1[i]
    		numPods := len(nodesToVictims[node].Pods)
    		if numPods < minNumPods { 
    			minNumPods = numPods
    			lenNodes2 = 0
    		}
    		if numPods == minNumPods { 
    			minNodes2[lenNodes2] = node
    			lenNodes2++
    		}
    	}
    	// 若经过上面四个步骤的筛选，筛选出的node还是不止一个，那么就挑选其中的第一个作为最后选中被执行抢占调度的node
    	if lenNodes2 > 0 { 
    		return minNodes2[0]
    	}
    	klog.Errorf("Error in logic of node scoring for preemption. We should never reach here!")
    	return nil
    }

上面代码结合注释，可以归纳出，这个函数中做了非常细致地检查，最高分如下4个步骤来对node进行优先级排序，筛选出一个最终合适的node来被执行抢占调度pod的操作：

1.按违反PDB约束的次数排序

2.按node上需驱逐的第一个pod(即需驱逐的优先级最高的pod)的优先级大小排序

3.按node上需驱逐的所有的pod的优先级总和计算排序

4.按node上需驱逐的所有的pod数量计算排序

5.若经过上面四个步骤的筛选，筛选出的node还是不止一个，那么就挑选其中的第一个作为最后选中node

## 4. 总结 ##

抢占调度的逻辑可以说是非常细致和精彩，例如

1.从资源计算的角度：

*  基于nodeInfo快照的计算，所有计算在最终确定实施之前都是预计算
 *  先枚举出所有低优先级的pod，保障待调度pod能充分获取资源
 *  在待调度pod能运行后，再尽力保障最多的低优先级pod能同时运行

2.从node选取的角度：

*  分4个步骤筛选以选出驱逐造成影响最小一个node

本章完，感谢阅读！

[k8s_-]: https://github.com/yinwenqin/kubeSourceCodeNote
[Pod Priority and Preemption]: https://kubernetes.io/docs/concepts/configuration/pod-priority-preemption/
[Specifying a Disruption Budget for your Application]: https://kubernetes.io/docs/tasks/run-application/configure-pdb/