解决Kubernetes任务队列难题：Kueue常见问题与解决方案全解析-优快云博客

解决Kubernetes任务队列难题：Kueue常见问题与解决方案全解析

【免费下载链接】kueue Kubernetes-native Job Queueing 项目地址: https://gitcode.com/gh_mirrors/ku/kueue

引言：Kubernetes任务调度的痛点与解决方案

你是否还在为Kubernetes集群中的任务调度效率低下而烦恼？是否经历过资源分配不均、任务优先级混乱、多集群管理复杂等问题？作为Kubernetes原生的任务队列管理系统，Kueue为这些难题提供了一站式解决方案。本文将深入剖析Kueue在实际应用中的常见问题，并提供详尽的解决方案和最佳实践，帮助你充分发挥Kueue的强大功能，优化集群资源利用率，提升任务调度效率。

读完本文，你将能够：

快速解决Kueue部署与配置中的常见问题
优化资源分配策略，实现多租户公平共享
掌握高级调度特性，如抢占、拓扑感知调度等
有效监控和排查Kueue系统故障
成功部署和管理多集群任务调度

Kueue基础与架构

Kueue核心概念

Kueue（发音为"cue"）是一个Kubernetes原生的任务队列管理系统，它通过一组API和控制器实现任务级别的队列管理。Kueue决定任务何时应该被允许启动（即可以创建Pod）以及何时应该停止（即应该删除活动Pod）。

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核心组件与工作流程

Kueue的核心组件包括：

ResourceFlavor（资源 flavor）：表示集群中的资源类型，如CPU、内存、GPU等，可以关联特定的节点标签和污点。
ClusterQueue（集群队列）：定义集群级别的资源配额和调度策略，跨命名空间共享。
LocalQueue（本地队列）：命名空间级别的队列，关联到ClusterQueue，用户提交的任务通过LocalQueue进入系统。
Workload（工作负载）：Kueue对任务的抽象表示，包含任务所需的资源、优先级等信息。

Kueue的工作流程如下：

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常见问题与解决方案

1. 资源配置问题

问题描述：任务一直处于挂起状态，无法被调度

可能原因：

资源配额不足
资源 flavor 配置错误
队列优先级设置不当

解决方案：

首先，检查ClusterQueue和ResourceFlavor配置是否正确：

# 正确的ClusterQueue和ResourceFlavor配置示例
apiVersion: kueue.x-k8s.io/v1beta1
kind: ResourceFlavor
metadata:
  name: "default-flavor"
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "cluster-queue"
spec:
  namespaceSelector: {} # 匹配所有命名空间
  resourceGroups:
  - coveredResources: ["cpu", "memory"]
    flavors:
    - name: "default-flavor"
      resources:
      - name: "cpu"
        nominalQuota: 9
      - name: "memory"
        nominalQuota: 36Gi
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  namespace: "default"
  name: "user-queue"
spec:
  clusterQueue: "cluster-queue"

检查任务是否正确设置了队列标签：

apiVersion: batch/v1
kind: Job
metadata:
  generateName: sample-job-
  namespace: default
  labels:
    kueue.x-k8s.io/queue-name: user-queue # 确保此标签正确指向存在的LocalQueue
spec:
  parallelism: 3
  completions: 3
  suspend: true # 必须设置为true，由Kueue控制恢复
  template:
    spec:
      containers:
      - name: dummy-job
        image: registry.k8s.io/e2e-test-images/agnhost:2.53
        command: [ "/bin/sh" ]
        args: [ "-c", "sleep 60" ]
        resources:
          requests:
            cpu: "1"
            memory: "200Mi"
      restartPolicy: Never

如果资源配额不足，可以调整ClusterQueue的nominalQuota或启用资源借用：

# 调整资源配额示例
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "cluster-queue"
spec:
  # ... 其他配置
  resourceGroups:
  - coveredResources: ["cpu", "memory"]
    flavors:
    - name: "default-flavor"
      resources:
      - name: "cpu"
        nominalQuota: 15 # 增加CPU配额
        max: 20 # 允许最大借用至20
      - name: "memory"
        nominalQuota: 60Gi # 增加内存配额
        max: 80Gi # 允许最大借用至80Gi

2. 优先级与抢占问题

问题描述：高优先级任务无法抢占低优先级任务

可能原因：

抢占策略配置不当
队列优先级设置错误
资源借用限制

解决方案：

正确配置ClusterQueue的抢占策略：

apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "cluster-queue"
spec:
  # ... 其他配置
  preemption:
    withinCohort: ReclaimFromLowerPriority
    withinClusterQueue: LowerPriority
  cohort: "default-cohort"

设置任务优先级：

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: high-priority
value: 1000
globalDefault: false
description: "高优先级任务"
---
apiVersion: batch/v1
kind: Job
metadata:
  generateName: high-priority-job-
  namespace: default
  labels:
    kueue.x-k8s.io/queue-name: user-queue
spec:
  priorityClassName: high-priority # 设置优先级类
  parallelism: 1
  completions: 1
  suspend: true
  template:
    # ... 容器配置

抢占工作流程：

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3. 多集群调度问题

问题描述：MultiKueue配置后任务无法跨集群调度

可能原因：

集群间认证配置错误
MultiKueue控制器未正确部署
工作集群资源不足

解决方案：

正确配置MultiKueue：

# 创建MultiKueue配置
apiVersion: kueue.x-k8s.io/v1beta1
kind: MultiKueueConfig
metadata:
  name: multikueue-test
spec:
  clusters:
  - multikueue-test-worker1
---
# 配置工作集群连接信息
apiVersion: kueue.x-k8s.io/v1beta1
kind: MultiKueueCluster
metadata:
  name: multikueue-test-worker1
spec:
  kubeConfig:
    locationType: Secret
    location: worker1-secret # 包含工作集群kubeconfig的Secret
---
# 在ClusterQueue中启用MultiKueue
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: cluster-queue
spec:
  # ... 其他配置
  admissionChecks:
  - sample-multikueue
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: AdmissionCheck
metadata:
  name: sample-multikueue
spec:
  controllerName: kueue.x-k8s.io/multikueue
  parameters:
    apiGroup: kueue.x-k8s.io
    kind: MultiKueueConfig
    name: multikueue-test

检查工作集群的健康状态：

kubectl describe multikueuecluster multikueue-test-worker1

确保所有工作集群都已正确配置并处于活动状态。如果使用增量调度策略，可以调整调度参数：

# 在MultiKueueConfig中配置调度策略
apiVersion: kueue.x-k8s.io/v1beta1
kind: MultiKueueConfig
metadata:
  name: multikueue-test
spec:
  clusters:
  - multikueue-test-worker1
  - multikueue-test-worker2
  dispatcherName: kueue.x-k8s.io/multikueue-dispatcher-incremental

4. 监控与可见性问题

问题描述：无法有效监控挂起的工作负载和队列状态

可能原因：

可见性配置未启用
Grafana仪表板未正确导入
RBAC权限不足

解决方案：

部署可见性所需的RBAC配置：

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: kueue-visibility-server-api
rules:
- apiGroups:
  - "visibility.kueue.x-k8s.io"
  resources:
  - "clusterqueues/pendingworkloads"
  - "localqueues/pendingworkloads"
  verbs:
  - get
  - list
  - watch
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: kueue-visibility-server-api
subjects:
- kind: ServiceAccount
  name: default
  namespace: default
roleRef:
  kind: ClusterRole
  name: kueue-visibility-server-api
  apiGroup: rbac.authorization.k8s.io

导入Grafana仪表板监控挂起的工作负载：

{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": {
          "type": "grafana",
          "uid": "-- Grafana --"
        },
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "panels": [
    {
      "datasource": {
        "type": "yesoreyeram-infinity-datasource",
        "uid": "${DS_YESOREYERAM-INFINITY-DATASOURCE}"
      },
      "fieldConfig": {
        "defaults": {
          "color": {
            "mode": "thresholds"
          },
          "custom": {
            "align": "auto",
            "inspect": false
          },
          "mappings": [],
          "thresholds": {
            "mode": "absolute",
            "steps": [
              {
                "color": "green"
              },
              {
                "color": "red",
                "value": 80
              }
            ]
          }
        },
        "overrides": []
      },
      "gridPos": {
        "h": 9,
        "w": 24,
        "x": 0,
        "y": 0
      },
      "id": 1,
      "options": {
        "showHeader": true
      },
      "targets": [
        {
          "columns": [
            {
              "selector": "metadata.name",
              "text": "Name",
              "type": "string"
            },
            {
              "selector": "metadata.namespace",
              "text": "Namespace",
              "type": "string"
            },
            {
              "selector": "metadata.creationTimestamp",
              "text": "Creation Timestamp",
              "type": "timestamp"
            },
            {
              "selector": "priority",
              "text": "Priority",
              "type": "string"
            },
            {
              "selector": "localQueueName",
              "text": "Local Queue",
              "type": "string"
            },
            {
              "selector": "positionInClusterQueue",
              "text": "Cluster Queue Position",
              "type": "number"
            }
          ],
          "datasource": {
            "type": "yesoreyeram-infinity-datasource",
            "uid": "${DS_YESOREYERAM-INFINITY-DATASOURCE}"
          },
          "format": "table",
          "parser": "backend",
          "refId": "A",
          "root_selector": "items",
          "source": "url",
          "type": "json",
          "url": "https://kubernetes.default.svc/apis/visibility.kueue.x-k8s.io/v1beta1/clusterqueues/$cluster_queue/pendingworkloads"
        }
      ],
      "title": "Pending Workloads",
      "type": "table"
    }
  ],
  "title": "Pending Workloads for ClusterQueue visibility"
}

5. 资源供应问题

问题描述：需要动态扩展集群资源以应对任务需求

可能原因：

集群资源已达上限
资源供应配置未启用
云提供商配置错误

解决方案：

配置资源供应（Provisioning）：

# 创建ProvisioningRequest模板
apiVersion: kueue.x-k8s.io/v1beta1
kind: ProvisioningRequestTemplate
metadata:
  name: gpu-provisioning-template
spec:
  provisioningClassName: "gpu-provisioner"
  parameters:
    region: "cn-beijing"
    instanceType: "ecs.gn6v-c8g1.2xlarge"
  resourceGroups:
  - coveredResources: ["nvidia.com/gpu"]
    flavors:
    - name: "gpu-flavor"
      resources:
      - name: "nvidia.com/gpu"
        min: 1
        max: 4
---
# 在ClusterQueue中启用资源供应
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "gpu-queue"
spec:
  # ... 其他配置
  admissionChecks:
  - name: gpu-provisioning
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: AdmissionCheck
metadata:
  name: gpu-provisioning
spec:
  controllerName: kueue.x-k8s.io/provisioning
  parameters:
    apiGroup: kueue.x-k8s.io
    kind: ProvisioningRequestTemplate
    name: gpu-provisioning-template

提交需要GPU的任务：

apiVersion: batch/v1
kind: Job
metadata:
  generateName: gpu-job-
  namespace: default
  labels:
    kueue.x-k8s.io/queue-name: gpu-queue
  annotations:
    provreq.kueue.x-k8s.io/maxRunDurationSeconds: "3600"
spec:
  parallelism: 1
  completions: 1
  suspend: true
  template:
    spec:
      tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
      containers:
      - name: gpu-job
        image: nvidia/cuda:11.0.3-base-ubuntu20.04
        command: ["nvidia-smi"]
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
            nvidia.com/gpu: 1
          limits:
            nvidia.com/gpu: 1
      restartPolicy: Never

资源供应工作流程：

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高级功能与最佳实践

拓扑感知调度

拓扑感知调度允许优化Pod间通信吞吐量，通过感知数据中心拓扑进行调度：

apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: topology-aware-queue
spec:
  # ... 其他配置
  topologyAwareScheduling:
    enabled: true
    zoneKey: "topology.kubernetes.io/zone"
    regionKey: "

【免费下载链接】kueue Kubernetes-native Job Queueing 项目地址: https://gitcode.com/gh_mirrors/ku/kueue

创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考