cluster-proportional-autoscaler源码分析及如何解决KubeDNS性能瓶颈
cluster-proportional-autoscaler源码分析及如何解决KubeDNS性能瓶颈
WaltonWang 发表于2个月前
cluster-proportional-autoscaler源码分析及如何解决KubeDNS性能瓶颈
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标题:腾讯云 新注册用户域名抢购1元起>>>   

摘要: 最近在做TensorFlow on Kubernetes项目,发现一个问题:随着Kubernetes中跑的TensorFlow训练任务规模增大,很快就遇到了KubeDNS性能的瓶颈问题(经常遇到500+ TensorFlow workers规模时,某些worker ping 其他worker/ps service name时,连续十几次只有一次能解析到pod ip),为了解决这个问题,我们引入了kubernetes的孵化项目kubernetes-incubator/cluster-proportional-autoscaler来对KubeDNS进行动态伸缩,本文是对其工作机制的解析及其源码分析

Author: xidianwangtao@gmail.com

工作机制

cluster-proportional-autoscaler是kubernetes的孵化项目之一,用来根据集群规模动态的扩缩容指定的namespace下的target(只支持RC, RS, Deployment),还不支持对StatefulSet。目前只提供两种autoscale模式,一种是linear,另一种是ladder,你能很容易的定制开发新的模式,代码接口非常清晰。

cluster-proportional-autoscaler工作机制很简单,每隔一定时间(通过--poll-period-seconds配置,默认10s)重复进行如下操作:

  • 统计一次集群中ScheduableNodes和ScheduableCores;
  • 从apiserver中获取最新configmap数据;
  • 根据对应的autoscale模式,进行configmap参数解析;
  • 据对应的autoscale模式,计算新的期望副本数;
  • 如果与上一次期望副本数不同,则调用Scale接口触发AutoScale;

配置说明

cluster-proportional-autoscaler一共有下面6项flag:

  • --namespace: 要autoscale的对象所在的namespace;
  • --target: 要autoscale的对象,只支持deployment/replicationcontroller/replicaset,不区分大小写;
  • --configmap: 配置实现创建好的configmap,里面存储要使用的模式及其配置,后面会有具体的示例;
  • --default-params: 如果--configmap中配置的configmap不存在或者后来被删除了,则使用该配置来创建新的configmap,建议要配置;
  • --poll-period-seconds: 检查周期,默认为10s。
  • --version: 打印vesion并退出。

源码分析

pollAPIServer

pkg/autoscaler/autoscaler_server.go:82
func (s *AutoScaler) pollAPIServer() {
	// Query the apiserver for the cluster status --- number of nodes and cores
	clusterStatus, err := s.k8sClient.GetClusterStatus()
	if err != nil {
		glog.Errorf("Error while getting cluster status: %v", err)
		return
	}
	glog.V(4).Infof("Total nodes %5d, schedulable nodes: %5d", clusterStatus.TotalNodes, clusterStatus.SchedulableNodes)
	glog.V(4).Infof("Total cores %5d, schedulable cores: %5d", clusterStatus.TotalCores, clusterStatus.SchedulableCores)

	// Sync autoscaler ConfigMap with apiserver
	configMap, err := s.syncConfigWithServer()
	if err != nil || configMap == nil {
		glog.Errorf("Error syncing configMap with apiserver: %v", err)
		return
	}

	// Only sync updated ConfigMap or before controller is set.
	if s.controller == nil || configMap.ObjectMeta.ResourceVersion != s.controller.GetParamsVersion() {
		// Ensure corresponding controller type and scaling params.
		s.controller, err = plugin.EnsureController(s.controller, configMap)
		if err != nil || s.controller == nil {
			glog.Errorf("Error ensuring controller: %v", err)
			return
		}
	}

	// Query the controller for the expected replicas number
	expReplicas, err := s.controller.GetExpectedReplicas(clusterStatus)
	if err != nil {
		glog.Errorf("Error calculating expected replicas number: %v", err)
		return
	}
	glog.V(4).Infof("Expected replica count: %3d", expReplicas)

	// Update resource target with expected replicas.
	_, err = s.k8sClient.UpdateReplicas(expReplicas)
	if err != nil {
		glog.Errorf("Update failure: %s", err)
	}
}

GetClusterStatus

GetClusterStatus用于统计集群中SchedulableNodes, SchedulableCores,用于后面计算新的期望副本数。

pkg/autoscaler/k8sclient/k8sclient.go:142
func (k *k8sClient) GetClusterStatus() (clusterStatus *ClusterStatus, err error) {
	opt := metav1.ListOptions{Watch: false}

	nodes, err := k.clientset.CoreV1().Nodes().List(opt)
	if err != nil || nodes == nil {
		return nil, err
	}
	clusterStatus = &ClusterStatus{}
	clusterStatus.TotalNodes = int32(len(nodes.Items))
	var tc resource.Quantity
	var sc resource.Quantity
	for _, node := range nodes.Items {
		tc.Add(node.Status.Capacity[apiv1.ResourceCPU])
		if !node.Spec.Unschedulable {
			clusterStatus.SchedulableNodes++
			sc.Add(node.Status.Capacity[apiv1.ResourceCPU])
		}
	}

	tcInt64, tcOk := tc.AsInt64()
	scInt64, scOk := sc.AsInt64()
	if !tcOk || !scOk {
		return nil, fmt.Errorf("unable to compute integer values of schedulable cores in the cluster")
	}
	clusterStatus.TotalCores = int32(tcInt64)
	clusterStatus.SchedulableCores = int32(scInt64)
	k.clusterStatus = clusterStatus
	return clusterStatus, nil
}
  • Nodes数量统计时,是会剔除掉那些 Unschedulable Nodes的。
  • Cores数量统计时,是会减掉那些 Unschedulable Nodes对应Capacity。
    • 请注意,这里计算Cores时统计的是Node的Capacity,而不是Allocatable。
    • 我认为,使用Allocatable要比Capacity更好。
    • 这两者在大规模集群时就会体现出差别了,比如每个Node Allocatable比Capacity少1c4g,那么2K个Node集群规模时,就相差2000c8000g,这将是的target object number相差很大。

有些同学可能要问:Node Allocatable和Capacity有啥不同呢?

  • Capacity是Node硬件层面提供的全部资源,服务器配置的多少内存,cpu核数等,都是由硬件决定的。
  • Allocatable则要在Capacity的基础上减去kubelet flag中配置的kube-resreved和system-reserved资源大小,是Kubernetes给应用真正可分配的资源数。

syncConfigWithServer

syncConfigWithServer主要是从apiserver中获取最新configmap数据,注意这里并没有去watch configmap,而是按照--poll-period-seconds(默认10s)定期的去get,所以默认会存在最多10s的延迟。

pkg/autoscaler/autoscaler_server.go:124
func (s *AutoScaler) syncConfigWithServer() (*apiv1.ConfigMap, error) {
	// Fetch autoscaler ConfigMap data from apiserver
	configMap, err := s.k8sClient.FetchConfigMap(s.k8sClient.GetNamespace(), s.configMapName)
	if err == nil {
		return configMap, nil
	}
	if s.defaultParams == nil {
		return nil, err
	}
	glog.V(0).Infof("ConfigMap not found: %v, will create one with default params", err)
	configMap, err = s.k8sClient.CreateConfigMap(s.k8sClient.GetNamespace(), s.configMapName, s.defaultParams)
	if err != nil {
		return nil, err
	}
	return configMap, nil
}
  • 如果配置的--configmap在集群中已经存在,则从apiserver中获取最新的configmap并返回;
  • 如果配置的--configmap在集群中不存在,则根据--default-params的内容创建一个configmap并返回;
  • 如果配置的--configmap在集群中不存在,且--default-params又没有配置,则返回nil,意味着失败,整个流程结束,使用时请注意!

建议一定要配置--default-params,因为--configmap配置的configmap有可能有意或者无意的被管理员/用户删除了,而你又没配置--default-params,那么这个时候pollAPIServer将就此结束,因为着你没达到autoscale target的目的,关键是你可能并在不知道集群这个时候出现了这个情况。

EnsureController

EnsureController用来根据configmap中配置的controller type创建对应Controller及解析参数。

pkg/autoscaler/controller/plugin/plugin.go:32

// EnsureController ensures controller type and scaling params
func EnsureController(cont controller.Controller, configMap *apiv1.ConfigMap) (controller.Controller, error) {
	// Expect only one entry, which uses the name of control mode as the key
	if len(configMap.Data) != 1 {
		return nil, fmt.Errorf("invalid configMap format, expected only one entry, got: %v", configMap.Data)
	}
	for mode := range configMap.Data {
		// No need to reset controller if control pattern doesn't change
		if cont != nil && mode == cont.GetControllerType() {
			break
		}
		switch mode {
		case laddercontroller.ControllerType:
			cont = laddercontroller.NewLadderController()
		case linearcontroller.ControllerType:
			cont = linearcontroller.NewLinearController()
		default:
			return nil, fmt.Errorf("not a supported control mode: %v", mode)
		}
		glog.V(1).Infof("Set control mode to %v", mode)
	}

	// Sync config with controller
	if err := cont.SyncConfig(configMap); err != nil {
		return nil, fmt.Errorf("Error syncing configMap with controller: %v", err)
	}
	return cont, nil
}
  • 检查configmap data中是否只有一个entry,如果不是,则该configmap不合法,流程结束;
  • 检查controller的类型是否为linearladder其中之一,并调用对应的方法创建对应的Controller,否则返回失败;
    • linear --> NewLinearController
    • ladder --> NewLadderController
  • 调用对应Controller的SyncConfig解析configmap data中参数和configmap ResourceVersion更新到Controller对象中;

GetExpectedReplicas

linear和ladder Controller分别实现了自己的GetExpectedReplicas方法,用来计算期望此次监控到的数据应该有的副本数。具体的看下面关于Linear Controller和Ladder Controller部分。

UpdateReplicas

UpdateReplicas将GetExpectedReplicas计算得到的期望副本数,通过调用对应target(rc/rs/deploy)对应的Scale接口,由Scale去完成target的缩容扩容。

pkg/autoscaler/k8sclient/k8sclient.go:172
func (k *k8sClient) UpdateReplicas(expReplicas int32) (prevRelicas int32, err error) {
	scale, err := k.clientset.Extensions().Scales(k.target.namespace).Get(k.target.kind, k.target.name)
	if err != nil {
		return 0, err
	}
	prevRelicas = scale.Spec.Replicas
	if expReplicas != prevRelicas {
		glog.V(0).Infof("Cluster status: SchedulableNodes[%v], SchedulableCores[%v]", k.clusterStatus.SchedulableNodes, k.clusterStatus.SchedulableCores)
		glog.V(0).Infof("Replicas are not as expected : updating replicas from %d to %d", prevRelicas, expReplicas)
		scale.Spec.Replicas = expReplicas
		_, err = k.clientset.Extensions().Scales(k.target.namespace).Update(k.target.kind, scale)
		if err != nil {
			return 0, err
		}
	}
	return prevRelicas, nil
}

下面是对Linear Controller和Ladder Controller具体实现的代码分析。

Linear Controller

先来看看linear Controller的参数:

pkg/autoscaler/controller/linearcontroller/linear_controller.go:50
type linearParams struct {
	CoresPerReplica           float64 `json:"coresPerReplica"`
	NodesPerReplica           float64 `json:"nodesPerReplica"`
	Min                       int     `json:"min"`
	Max                       int     `json:"max"`
	PreventSinglePointFailure bool    `json:"preventSinglePointFailure"`
}

写configmap时,参考如下:

kind: ConfigMap
apiVersion: v1
metadata:
  name: nginx-autoscaler
  namespace: default
data:
  linear: |-
    { 
      "coresPerReplica": 2,
      "nodesPerReplica": 1,
      "preventSinglePointFailure": true,
      "min": 1,
      "max": 100
    }

其他参数不多说,我想提的是PreventSinglePointFailure,字面意思是防止单点故障,是一个bool值,代码中没有进行显示的初始化,意味着默认为false。可以在对应的configmap data或者dafault-params中设置"preventSinglePointFailure": true,但设置为true后,如果schedulableNodes > 1,则会保证target's replicas至少为2,也就是防止了target单点故障。

pkg/autoscaler/controller/linearcontroller/linear_controller.go:101

func (c *LinearController) GetExpectedReplicas(status *k8sclient.ClusterStatus) (int32, error) {
	// Get the expected replicas for the currently schedulable nodes and cores
	expReplicas := int32(c.getExpectedReplicasFromParams(int(status.SchedulableNodes), int(status.SchedulableCores)))

	return expReplicas, nil
}

func (c *LinearController) getExpectedReplicasFromParams(schedulableNodes, schedulableCores int) int {
	replicasFromCore := c.getExpectedReplicasFromParam(schedulableCores, c.params.CoresPerReplica)
	replicasFromNode := c.getExpectedReplicasFromParam(schedulableNodes, c.params.NodesPerReplica)
	// Prevent single point of failure by having at least 2 replicas when
	// there are more than one node.
	if c.params.PreventSinglePointFailure &&
		schedulableNodes > 1 &&
		replicasFromNode < 2 {
		replicasFromNode = 2
	}

	// Returns the results which yields the most replicas
	if replicasFromCore > replicasFromNode {
		return replicasFromCore
	}
	return replicasFromNode
}

func (c *LinearController) getExpectedReplicasFromParam(schedulableResources int, resourcesPerReplica float64) int {
	if resourcesPerReplica == 0 {
		return 1
	}
	res := math.Ceil(float64(schedulableResources) / resourcesPerReplica)
	if c.params.Max != 0 {
		res = math.Min(float64(c.params.Max), res)
	}
	return int(math.Max(float64(c.params.Min), res))
}
  • 根据schedulableCores和configmap中的CoresPerReplica,按照如下公式计算得到replicasFromCore;
    • replicasFromCore = ceil( schedulableCores * 1/CoresPerReplica )
  • 根据schedulableNodes和configmap中的NodesPerReplica,按照如下公式计算得到replicasFromNode;
    • replicasFromNode = ceil( schedulableNodes * 1/NodesPerReplica ) )
  • 如果configmap中配置了min或者max,则必须保证replicas在min和max范围内;
    • replicas = min(replicas, max)
    • replicas = max(replicas, min)
  • 如果配置了preventSinglePointFailure为true并且schedulableNodes > 1,则根据前面提到的逻辑进行防止单点故障,replicasFromNode必须大于2;
    • replicasFromNode = max(2, replicasFromNode)
  • 返回replicasFromNode和replicasFromCore中的最大者作为期望副本数。

概括起来,linear controller计算replicas的公式为:

replicas = max( ceil( cores * 1/coresPerReplica ) , ceil( nodes * 1/nodesPerReplica ) )
replicas = min(replicas, max)
replicas = max(replicas, min)

Ladder Controller

下面是ladder Controller的参数结构:

pkg/autoscaler/controller/laddercontroller/ladder_controller.go:66
type paramEntry [2]int
type paramEntries []paramEntry
type ladderParams struct {
	CoresToReplicas paramEntries `json:"coresToReplicas"`
	NodesToReplicas paramEntries `json:"nodesToReplicas"`
}

写configmap时,参考如下:

kind: ConfigMap
apiVersion: v1
metadata:
  name: nginx-autoscaler
  namespace: default
data:
  ladder: |-
    { 
      "coresToReplicas":
      [
        [ 1,1 ],
        [ 3,3 ],
        [256,4],
        [ 512,5 ],
        [ 1024,7 ]
      ],
      "nodesToReplicas":
      [
        [ 1,1 ],
        [ 2,2 ],
        [100, 5],
        [200, 12]
      ]
    }

下面是ladder Controller对应的计算期望副本值的方法。

func (c *LadderController) GetExpectedReplicas(status *k8sclient.ClusterStatus) (int32, error) {
	// Get the expected replicas for the currently schedulable nodes and cores
	expReplicas := int32(c.getExpectedReplicasFromParams(int(status.SchedulableNodes), int(status.SchedulableCores)))

	return expReplicas, nil
}

func (c *LadderController) getExpectedReplicasFromParams(schedulableNodes, schedulableCores int) int {
	replicasFromCore := getExpectedReplicasFromEntries(schedulableCores, c.params.CoresToReplicas)
	replicasFromNode := getExpectedReplicasFromEntries(schedulableNodes, c.params.NodesToReplicas)

	// Returns the results which yields the most replicas
	if replicasFromCore > replicasFromNode {
		return replicasFromCore
	}
	return replicasFromNode
}

func getExpectedReplicasFromEntries(schedulableResources int, entries []paramEntry) int {
	if len(entries) == 0 {
		return 1
	}
	// Binary search for the corresponding replicas number
	pos := sort.Search(
		len(entries),
		func(i int) bool {
			return schedulableResources < entries[i][0]
		})
	if pos > 0 {
		pos = pos - 1
	}
	return entries[pos][1]
}
  • 根据schedulableCores在configmap中的CoresToReplicas定义的那个范围中,就选择预先设定的期望副本数。
  • 根据schedulableNodes在configmap中的NodesToReplicas定义的那个范围中,就选择预先设定的期望副本数。
  • 返回上面两者中的最大者作为期望副本数。

注意:

  • ladder模式下,没有防止单点故障的设置项,用户配置configmap时候要自己注意;
  • ladder模式下,没有NodesToReplicas或者CoresToReplicas对应的配置为空,则对应的replicas设为1;

比如前面举例的configmap,如果集群中schedulableCores=400(对应期望副本为4),schedulableNodes=120(对应期望副本为5),则最终的期望副本数为5.

使用kube-dns-autoscaler解决KubeDNS性能瓶颈问题

通过如下yaml文件创建kube-dns-autoscaler Deployment和configmap, kube-dns-autoscaler每个30s会进行一次副本数计算检查,并可能触发AutoScale。

kind: ConfigMap
apiVersion: v1
metadata:
  name: kube-dns-autoscaler
  namespace: kube-system
data:
  linear: |
    {
    "nodesPerReplica": 10,
    "min": 1,
    "max": 50,
    "preventSinglePointFailure": true
    } 

‐‐‐

apiVersion: extensions/v1beta1
kind: Deployment
metadata:
  name: kube-dns-autoscaler
  namespace: kube-system
spec:
  template:
    metadata:
      labels:
        k8s-app: kube-dns-autoscaler
    spec:
      imagePullSecrets:
      - name: harborsecret
      containers:
      - name: autoscaler
        image: registry.vivo.xyz:4443/bigdata_release/cluster_proportional_autoscaler_amd64:1.0.0
        resources:
          requests:
            cpu: "50m"
            memory: "100Mi"
        command:
        - /cluster-proportional-autoscaler
        - --namespace=kube-system
        - --configmap=kube-dns-autoscaler
        - --target=Deployment/kube-dns
        - --default-params={"linear":{"nodesPerReplica":10,"min":1}}
        - --logtostderr=true
        - --v=2

总结和展望

  • cluster-proportional-autoscaler代码很简单,工作机制也很单纯,我们希望用它根据集群规模来动态扩展KubeDNS,以解决TensorFlow on Kubernetes项目中大规模的域名解析性能问题。

  • 目前它只支持根据SchedulableNodes和SchedulableCores来autoscale,在AI的场景中,存在集群资源极度压榨的情况,一个集群承载的svc和pod波动范围很大,后续我们可能会开发根据service number来autoscale kubedns的controller。

  • 另外,我还考虑将KubeDNS的部署从AI训练服务器中隔离出来,因为训练时经常会将服务器cpu跑到95%以上,KubeDNS也部署在这台服务器上的话,势必也会影响KubeDNS性能。

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