使用 Prometheus 在 KubeSphere 上监控 KubeEdge 边缘节点(Jetson) CPU、GPU 状态

作者:朱亚光,之江实验室工程师,云原生/开源爱好者。

KubeSphere 边缘节点的可观测性

在边缘计算场景下,KubeSphere 基于 KubeEdge 实现应用与工作负载在云端与边缘节点的统一分发与管理,解决在海量边、端设备上完成应用交付、运维、管控的需求。

根据 KubeSphere 的支持矩阵,只有 1.23.x 版本的 K8s 支持边缘计算,而且 KubeSphere 界面也没有边缘节点资源使用率等监控信息的显示。

本文基于 KubeSphere 和 KubeEdge 构建云边一体化计算平台,通过 Prometheus 来监控 Nvidia Jetson 边缘设备状态,实现 KubeSphere 在边缘节点的可观测性。

组件版本
KubeSphere3.4.1
containerd1.7.2
K8s1.26.0
KubeEdge1.15.1
Jetson 型号NVIDIA Jetson Xavier NX (16GB ram)
Jtop4.2.7
JetPack5.1.3-b29
Docker24.0.5

部署 K8s 环境

参考 KubeSphere 部署文档。通过 KubeKey 可以快速部署一套 K8s 集群。

//  all in one 方式部署一台 单 master 的 k8s 集群

./kk create cluster --with-kubernetes v1.26.0 --with-kubesphere v3.4.1 --container-manager containerd

部署 KubeEdge 环境

参考 在 KubeSphere 上部署最新版的 KubeEdge,部署 KubeEdge。

开启边缘节点日志查询功能

  • vim /etc/kubeedge/config/edgecore.yaml

  • enable=true

  • 开启后,可以方便查询 pod 日志,定位问题。

    修改 KubeSphere 配置

    开启 KubeEdge 边缘节点插件

  • 修改 configmap--ClusterConfiguration
  • advertiseAddress 设置为 cloudhub 所在的物理机地址
  • KubeSphere 开启边缘节点文档链接:https://www.kubesphere.io/zh/docs/v3.3/pluggable-components/kubeedge/。

    修改完发现可以显示边缘节点,但是没有 CPU 和 内存信息,发现边缘节点没有 node-exporter 这个 pod。

    修改 node-exporter 亲和性

    kubectl get ds -n kubesphere-monitoring-system 发现不会部署到边缘节点上。

    修改为:

        spec:
          affinity:
            nodeAffinity:
              requiredDuringSchedulingIgnoredDuringExecution:
                nodeSelectorTerms:
                - matchExpressions:
                  - key: node-role.kubernetes.io/edgetest  -- 修改这里,让亲和性失效
                    operator: DoesNotExist
    

    node-exporter 是部署在边缘节点上了,但是 pods 起不来。

    通过kubectl edit 该失败的 pod,我们发现 node-exporter 这个pod 里面有两个容器,其中 kube-rbac-proxy 这个容器启动失败。看这个容器的日志,发现是 kube-rbac-proxy 想要获取 KUBERNETES_SERVICE_HOSTKUBERNETES_SERVICE_PORT 这两个环境变量,但是获取失败,所以容器启动失败。

    在 K8s 的集群中,当创建 pod 时,会在 pod 中增加 KUBERNETES_SERVICE_HOSTKUBERNETES_SERVICE_PORT 这两个环境变量,用于 pod 内的进程对 kube-apiserver 的访问,但是在 KubeEdge 的 edge 节点上创建的 pod 中,这两个环境变量存在,但它是空的。

    向 KubeEdge 的开发人员咨询,他们说会在 KubeEdge 1.17 版本上增加这两个环境变量的设置。参考如下:
    https://github.com/wackxu/kubeedge/blob/4a7c00783de9b11e56e56968b2cc950a7d32a403/docs/proposals/edge-pod-list-watch-natively.md。

    另一方面,推荐安装 EdgeMesh,安装之后在 edge 的 pod 上就可以访问 kubernetes.default.svc.cluster.local:443 了。

    EdgeMesh 部署

  • 配置 cloudcore configmap

    kubectl edit cm cloudcore -n kubeedge 设置 dynamicController=true.

    修改完 重启 cloudcore kubectl delete pod cloudcore-776ffcbbb9-s6ff8 -n kubeedge

  • 配置 edgecore 模块,配置 metaServer=true 和 clusterDNS

    $ vim /etc/kubeedge/config/edgecore.yaml
    
    modules:
      ...
      metaManager:
        metaServer:
          enable: true   //配置这里
    ...
    
    modules:
      ...
      edged:
        ...
        tailoredKubeletConfig:
          ...
          clusterDNS:     //配置这里
          - 169.254.96.16
    ...
    
    //重启edgecore
    $ systemctl restart edgecore
    

  • 修改完,验证是否修改成功。

    $ curl 127.0.0.1:10550/api/v1/services
    
    {"apiVersion":"v1","items":[{"apiVersion":"v1","kind":"Service","metadata":{"creationTimestamp":"2021-04-14T06:30:05Z","labels":{"component":"apiserver","provider":"kubernetes"},"name":"kubernetes","namespace":"default","resourceVersion":"147","selfLink":"default/services/kubernetes","uid":"55eeebea-08cf-4d1a-8b04-e85f8ae112a9"},"spec":{"clusterIP":"10.96.0.1","ports":[{"name":"https","port":443,"protocol":"TCP","targetPort":6443}],"sessionAffinity":"None","type":"ClusterIP"},"status":{"loadBalancer":{}}},{"apiVersion":"v1","kind":"Service","metadata":{"annotations":{"prometheus.io/port":"9153","prometheus.io/scrape":"true"},"creationTimestamp":"2021-04-14T06:30:07Z","labels":{"k8s-app":"kube-dns","kubernetes.io/cluster-service":"true","kubernetes.io/name":"KubeDNS"},"name":"kube-dns","namespace":"kube-system","resourceVersion":"203","selfLink":"kube-system/services/kube-dns","uid":"c221ac20-cbfa-406b-812a-c44b9d82d6dc"},"spec":{"clusterIP":"10.96.0.10","ports":[{"name":"dns","port":53,"protocol":"UDP","targetPort":53},{"name":"dns-tcp","port":53,"protocol":"TCP","targetPort":53},{"name":"metrics","port":9153,"protocol":"TCP","targetPort":9153}],"selector":{"k8s-app":"kube-dns"},"sessionAffinity":"None","type":"ClusterIP"},"status":{"loadBalancer":{}}}],"kind":"ServiceList","metadata":{"resourceVersion":"377360","selfLink":"/api/v1/services"}}
    
  • 安装 EdgeMesh

    git clone https://github.com/kubeedge/edgemesh.git
    cd edgemesh
    
    kubectl apply -f build/crds/istio/
    
    kubectl apply -f build/agent/resources/
    

  • dnsPolicy

    EdgeMesh 部署完成后,edge 节点上的 node-exporter 中的两个境变量还是空的,也无法访问 kubernetes.default.svc.cluster.local:443,原因是该 pod 中 DNS 服务器配置错误,应该是 169.254.96.16 的,但是却是跟宿主机一样的 DNS 配置。

    kubectl exec -it node-exporter-hcmfg -n kubesphere-monitoring-system -- sh
    Defaulted container "node-exporter" out of: node-exporter, kube-rbac-proxy
    $ cat /etc/resolv.conf
    nameserver 127.0.0.53
    

    将 dnsPolicy 修改为 ClusterFirstWithHostNet,之后重启 node-exporter,DNS 的配置正确。

    kubectl edit ds node-exporter -n kubesphere-monitoring-system

      dnsPolicy: ClusterFirstWithHostNet
      hostNetwork: true
    

    添加环境变量

    vim /etc/systemd/system/edgecore.service

    Environment=METASERVER_DUMMY_IP=kubernetes.default.svc.cluster.local
    Environment=METASERVER_DUMMY_PORT=443
    

    修改完重启 edgecore。

    systemctl daemon-reload
    systemctl restart edgecore
    

    node-exporter 变成 running!!!!

    在边缘节点 curl http://127.0.0.1:9100/metrics 可以发现采集到了边缘节点的数据。

    最后我们可以将 KubeSphere 的 K8s 服务通过 NodePort 暴露出来。就可以在页面查看。

    apiVersion: v1
    kind: Service
    metadata:
      labels:
        app.kubernetes.io/component: prometheus
        app.kubernetes.io/instance: k8s
        app.kubernetes.io/name: prometheus
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/version: 2.39.1
      name: prometheus-k8s-nodeport
      namespace: kubesphere-monitoring-system
    spec:
      ports:
      - port: 9090
        targetPort: 9090
        protocol: TCP
        nodePort: 32143
      selector:
        app.kubernetes.io/component: prometheus
        app.kubernetes.io/instance: k8s
        app.kubernetes.io/name: prometheus
        app.kubernetes.io/part-of: kube-prometheus
      sessionAffinity: ClientIP
      sessionAffinityConfig:
        clientIP:
          timeoutSeconds: 10800
      type: NodePort
    

    通过访问 master IP + 32143 端口,就可以访问边缘节点 node-exporter 数据。

    然后界面上也出现了 CPU 和内存的信息。

    搞定了 CPU 和内存,接下来就是 GPU 了。

    监控 Jetson GPU 状态

    安装 Jtop

    首先 Jetson 是一个 ARM 设备,所以无法运行 nvidia-smi ,需要安装 Jtop。

    sudo apt-get install python3-pip python3-dev -y
    sudo -H pip3 install jetson-stats
    sudo systemctl restart jtop.service
    

    安装 Jetson GPU Exporter

    参考博客,制作 Jetson GPU Exporter 镜像,并且对应的 Grafana 仪表盘都有。

    Dockerfile

    FROM python:3-buster
    RUN pip install --upgrade pip && pip install -U jetson-stats prometheus-client
    RUN mkdir -p /root
    COPY jetson_stats_prometheus_collector.py /root/jetson_stats_prometheus_collector.py
    WORKDIR /root
    USER root
    RUN chmod +x /root/jetson_stats_prometheus_collector.py
    ENTRYPOINT ["python3", "/root/jetson_stats_prometheus_collector.py"]
    

    jetson_stats_prometheus_collector.py 代码

    #!/usr/bin/python3
    # -*- coding: utf-8 -*-
    
    import atexit
    import os
    from jtop import jtop, JtopException
    from prometheus_client.core import InfoMetricFamily, GaugeMetricFamily, REGISTRY, CounterMetricFamily
    from prometheus_client import make_wsgi_app
    from wsgiref.simple_server import make_server
    
    class CustomCollector(object):
        def __init__(self):
            atexit.register(self.cleanup)
            self._jetson = jtop()
            self._jetson.start()
    
        def cleanup(self):
            print("Closing jetson-stats connection...")
            self._jetson.close()
    
        def collect(self):
            # spin传入true,表示不会等待下一次数据读取完成
            if self._jetson.ok(spin=True):
                #
                # Board info
                #
                i = InfoMetricFamily('gpu_info_board', 'Board sys info', labels=['board_info'])
                i.add_metric(['info'], {
                    'machine': self._jetson.board['info']['machine'] if 'machine' in self._jetson.board.get('info', {}) else self._jetson.board['hardware']['Module'],
                    'jetpack': self._jetson.board['info']['jetpack'] if 'jetpack' in self._jetson.board.get('info', {}) else self._jetson.board['hardware']['Jetpack'],
                    'l4t':  self._jetson.board['info']['L4T'] if 'L4T' in self._jetson.board.get('info', {}) else self._jetson.board['hardware']['L4T']
                    })
                yield i
    
                i = InfoMetricFamily('gpu_info_hardware', 'Board hardware info', labels=['board_hw'])
                i.add_metric(['hardware'], {
                    'codename': self._jetson.board['hardware'].get('Codename', self._jetson.board['hardware'].get('CODENAME', 'unknown')),
                    'soc': self._jetson.board['hardware'].get('SoC', self._jetson.board['hardware'].get('SOC', 'unknown')),
                    'module': self._jetson.board['hardware'].get('P-Number', self._jetson.board['hardware'].get('MODULE', 'unknown')),
                    'board': self._jetson.board['hardware'].get('699-level Part Number', self._jetson.board['hardware'].get('BOARD', 'unknown')),
                    'cuda_arch_bin': self._jetson.board['hardware'].get('CUDA Arch BIN', self._jetson.board['hardware'].get('CUDA_ARCH_BIN', 'unknown')),
                    'serial_number': self._jetson.board['hardware'].get('Serial Number', self._jetson.board['hardware'].get('SERIAL_NUMBER', 'unknown')),
                    })
                yield i
    
                #
                # NV power mode
                #
                i = InfoMetricFamily('gpu_nvpmode', 'NV power mode', labels=['nvpmode'])
                i.add_metric(['mode'], {'mode': self._jetson.nvpmodel.name})
                yield i
    
                #
                # System uptime
                #
                g = GaugeMetricFamily('gpu_uptime', 'System uptime', labels=['uptime'])
                days = self._jetson.uptime.days
                seconds = self._jetson.uptime.seconds
                hours = seconds//3600
                minutes = (seconds//60) % 60
                g.add_metric(['days'], days)
                g.add_metric(['hours'], hours)
                g.add_metric(['minutes'], minutes)
                yield g
    
                #
                # CPU usage
                #
                g = GaugeMetricFamily('gpu_usage_cpu', 'CPU % schedutil', labels=['cpu'])
                g.add_metric(['cpu_1'], self._jetson.stats['CPU1'] if ('CPU1' in self._jetson.stats and isinstance(self._jetson.stats['CPU1'], int)) else 0)
                g.add_metric(['cpu_2'], self._jetson.stats['CPU2'] if ('CPU2' in self._jetson.stats and isinstance(self._jetson.stats['CPU2'], int)) else 0)
                g.add_metric(['cpu_3'], self._jetson.stats['CPU3'] if ('CPU3' in self._jetson.stats and isinstance(self._jetson.stats['CPU3'], int)) else 0)
                g.add_metric(['cpu_4'], self._jetson.stats['CPU4'] if ('CPU4' in self._jetson.stats and isinstance(self._jetson.stats['CPU4'], int)) else 0)
                g.add_metric(['cpu_5'], self._jetson.stats['CPU5'] if ('CPU5' in self._jetson.stats and isinstance(self._jetson.stats['CPU5'], int)) else 0)
                g.add_metric(['cpu_6'], self._jetson.stats['CPU6'] if ('CPU6' in self._jetson.stats and isinstance(self._jetson.stats['CPU6'], int)) else 0)
                g.add_metric(['cpu_7'], self._jetson.stats['CPU7'] if ('CPU7' in self._jetson.stats and isinstance(self._jetson.stats['CPU7'], int)) else 0)
                g.add_metric(['cpu_8'], self._jetson.stats['CPU8'] if ('CPU8' in self._jetson.stats and isinstance(self._jetson.stats['CPU8'], int)) else 0)
                yield g
    
                #
                # GPU usage
                #
                g = GaugeMetricFamily('gpu_usage_gpu', 'GPU % schedutil', labels=['gpu'])
                g.add_metric(['val'], self._jetson.stats['GPU'])
                yield g
    
                #
                # Fan usage
                #
                g = GaugeMetricFamily('gpu_usage_fan', 'Fan usage', labels=['fan'])
                g.add_metric(['speed'], self._jetson.fan.get('speed', self._jetson.fan.get('pwmfan', {'speed': [0] })['speed'][0]))
                yield g
    
                #
                # Sensor temperatures
                #
                g = GaugeMetricFamily('gpu_temperatures', 'Sensor temperatures', labels=['temperature'])
                keys = ['AO', 'GPU', 'Tdiode', 'AUX', 'CPU', 'thermal', 'Tboard']
                for key in keys:
                    if key in self._jetson.temperature:
                        g.add_metric([key.lower()], self._jetson.temperature[key]['temp'] if isinstance(self._jetson.temperature[key], dict) else self._jetson.temperature.get(key, 0))
                yield g
                #
                # Power
                #
                g = GaugeMetricFamily('gpu_usage_power', 'Power usage', labels=['power'])
                if isinstance(self._jetson.power, dict):
                    g.add_metric(['cv'], self._jetson.power['rail']['VDD_CPU_CV']['avg'] if 'VDD_CPU_CV' in self._jetson.power['rail'] else self._jetson.power['rail'].get('CV', { 'avg': 0 }).get('avg'))
                    g.add_metric(['gpu'], self._jetson.power['rail']['VDD_GPU_SOC']['avg'] if 'VDD_GPU_SOC' in self._jetson.power['rail'] else self._jetson.power['rail'].get('GPU', { 'avg': 0 }).get('avg'))
                    g.add_metric(['sys5v'], self._jetson.power['rail']['VIN_SYS_5V0']['avg'] if 'VIN_SYS_5V0' in self._jetson.power['rail'] else self._jetson.power['rail'].get('SYS5V', { 'avg': 0 }).get('avg'))
                if isinstance(self._jetson.power, tuple):
                    g.add_metric(['cv'], self._jetson.power[1]['CV']['cur'] if 'CV' in self._jetson.power[1] else 0)
                    g.add_metric(['gpu'], self._jetson.power[1]['GPU']['cur'] if 'GPU' in self._jetson.power[1] else 0)
                    g.add_metric(['sys5v'], self._jetson.power[1]['SYS5V']['cur'] if 'SYS5V' in self._jetson.power[1] else 0)
                yield g
    
                #
                # Processes
                #
                try:
                    processes = self._jetson.processes
                    # key exists in dict
                    i = InfoMetricFamily('gpu_processes', 'Process usage', labels=['process'])
                    for index in range(len(processes)):
                        i.add_metric(['info'], {
                            'pid': str(processes[index][0]),
                            'user': processes[index][1],
                            'gpu': processes[index][2],
                            'type': processes[index][3],
                            'priority': str(processes[index][4]),
                            'state': processes[index][5],
                            'cpu': str(processes[index][6]),
                            'memory': str(processes[index][7]),
                            'gpu_memory': str(processes[index][8]),
                            'name': processes[index][9],
                        })
                    yield i
                except AttributeError:
                    # key doesn't exist in dict
                    i = 0
    
    if __name__ == '__main__':
        port = os.environ.get('PORT', 9998)
        REGISTRY.register(CustomCollector())
        app = make_wsgi_app()
        httpd = make_server('', int(port), app)
        print('Serving on port: ', port)
        try:
            httpd.serve_forever()
        except KeyboardInterrupt:
            print('Goodbye!')
    

    记得给 Jetson 的板子打标签,确保 GPU 的 Exporter 在 Jetson 上执行。否则在其他 node 上执行会因为采集不到数据而报错.

    kubectl label node edge-wpx machine.type=jetson

    新建 KubeSphere 资源

    新建 ServiceAccount、DaemonSet、Service、servicemonitor,目的是将 jetson-exporter 采集到的数据提供给 KubeSphere 的 Prometheus。

    apiVersion: v1
    kind: ServiceAccount
    metadata:
      labels:
        app.kubernetes.io/component: exporter
        app.kubernetes.io/name: jetson-exporter
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/version: 1.0.0
      name: jetson-exporter
      namespace: kubesphere-monitoring-system
    ---
    apiVersion: apps/v1
    kind: DaemonSet
    metadata:
      labels:
        app.kubernetes.io/component: exporter
        app.kubernetes.io/name: jetson-exporter
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/version: 1.0.0
      name: jetson-exporter
      namespace: kubesphere-monitoring-system
    spec:
      revisionHistoryLimit: 10
      selector:
        matchLabels:
          app.kubernetes.io/component: exporter
          app.kubernetes.io/name: jetson-exporter
          app.kubernetes.io/part-of: kube-prometheus
      template:
        metadata:
          labels:
            app.kubernetes.io/component: exporter
            app.kubernetes.io/name: jetson-exporter
            app.kubernetes.io/part-of: kube-prometheus
            app.kubernetes.io/version: 1.0.0
        spec:
          affinity:
            nodeAffinity:
              requiredDuringSchedulingIgnoredDuringExecution:
                nodeSelectorTerms:
                - matchExpressions:
                  - key: node-role.kubernetes.io/edge
                    operator: Exists
          containers:
          - image: jetson-status-exporter:v1
            imagePullPolicy: IfNotPresent
            name: jetson-exporter
            resources:
              limits:
                cpu: "1"
                memory: 500Mi
              requests:
                cpu: 102m
                memory: 180Mi
            ports:
            - containerPort: 9998
              hostPort: 9998
              name: http
              protocol: TCP
            terminationMessagePath: /dev/termination-log
            terminationMessagePolicy: File
            volumeMounts:
            - mountPath: /run/jtop.sock
              name: jtop-sock
              readOnly: true
          dnsPolicy: ClusterFirstWithHostNet
          hostNetwork: true
          hostPID: true
          nodeSelector:
            kubernetes.io/os: linux
            machine.type: jetson
          restartPolicy: Always
          schedulerName: default-scheduler
          serviceAccount: jetson-exporter
          terminationGracePeriodSeconds: 30
          tolerations:
          - operator: Exists
          volumes:
          - hostPath:
              path: /run/jtop.sock
              type: Socket
            name: jtop-sock
      updateStrategy:
        rollingUpdate:
          maxSurge: 0
          maxUnavailable: 1
        type: RollingUpdate
    ---
    apiVersion: v1
    kind: Service
    metadata:
      labels:
        app.kubernetes.io/component: exporter
        app.kubernetes.io/name: jetson-exporter
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/version: 1.0.0
      name: jetson-exporter
      namespace: kubesphere-monitoring-system
    spec:
      clusterIP: None
      clusterIPs:
      - None
      internalTrafficPolicy: Cluster
      ipFamilies:
      - IPv4
      ipFamilyPolicy: SingleStack
      ports:
      - name: http
        port: 9998
        protocol: TCP
        targetPort: http
      selector:
        app.kubernetes.io/component: exporter
        app.kubernetes.io/name: jetson-exporter
        app.kubernetes.io/part-of: kube-prometheus
      sessionAffinity: None
      type: ClusterIP
    ---
    apiVersion: monitoring.coreos.com/v1
    kind: ServiceMonitor
    metadata:
      labels:
        app.kubernetes.io/component: exporter
        app.kubernetes.io/name: jetson-exporter
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/vendor: kubesphere
        app.kubernetes.io/version: 1.0.0
      name: jetson-exporter
      namespace: kubesphere-monitoring-system
    spec:
      endpoints:
      - bearerTokenFile: /var/run/secrets/kubernetes.io/serviceaccount/token
        interval: 1m
        port: http
        relabelings:
        - action: replace
          regex: (.*)
          replacement: $1
          sourceLabels:
          - __meta_kubernetes_pod_node_name
          targetLabel: instance
        - action: labeldrop
          regex: (service|endpoint|container)
        scheme: http
        tlsConfig:
          insecureSkipVerify: true
      jobLabel: app.kubernetes.io/name
      selector:
        matchLabels:
          app.kubernetes.io/component: exporter
          app.kubernetes.io/name: jetson-exporter
          app.kubernetes.io/part-of: kube-prometheus
    

    部署完成后,jetson-exporter pod running。

    重启 Prometheus pod,重新加载配置后,可以在 Prometheus 界面看到新增加的 GPU exporter 的 target。

    kubectl delete pod prometheus-k8s-0 -n kubesphere-monitoring-system
    

    在 KubeSphere 前端,查看 GPU 监控数据

    前端需要修改 KubeSphere 的 console 的代码,这里属于前端内容,这里就不详细说明了。

    其次将 Prometheus 的 SVC 端口暴露出来,通过 nodeport 的方式将 Prometheus 的端口暴露出来,前端通过 http 接口来查询 GPU 的状态。

    apiVersion: v1
    kind: Service
    metadata:
      labels:
        app.kubernetes.io/component: prometheus
        app.kubernetes.io/instance: k8s
        app.kubernetes.io/name: prometheus
        app.kubernetes.io/part-of: kube-prometheus
        app.kubernetes.io/version: 2.39.1
      name: prometheus-k8s-nodeport
      namespace: kubesphere-monitoring-system
    spec:
      ports:
      - port: 9090
        targetPort: 9090
        protocol: TCP
        nodePort: 32143
      selector:
        app.kubernetes.io/component: prometheus
        app.kubernetes.io/instance: k8s
        app.kubernetes.io/name: prometheus
        app.kubernetes.io/part-of: kube-prometheus
      sessionAffinity: ClientIP
      sessionAffinityConfig:
        clientIP:
          timeoutSeconds: 10800
      type: NodePort
    

    http 接口

    查询瞬时值:
    get http://masterip:32143/api/v1/query?query=gpu_info_board_info&time=1711431293.686
    get http://masterip:32143/api/v1/query?query=gpu_info_hardware_info&time=1711431590.574
    get http://masterip:32143/api/v1/query?query=gpu_usage_gpu&time=1711431590.574
    其中query为查询字段名,time是查询的时间
    
    查询某个时间段的采集值:
    get http://10.11.140.87:32143/api/v1/query_range?query=gpu_usage_gpu&start=1711428221.998&end=1711431821.998&step=14
    其中query为查询字段名,start和end是起始结束时间,step是间隔时间
    

    这样就成功在 KubeSphere,监控 KubeEdge 边缘节点 Jetson 的 GPU 状态了。

    总结

    基于 KubeEdge,我们在 KubeSphere 的前端界面上实现了边缘设备的可观测性,包括 GPU 信息的可观测性。

    对于边缘节点 CPU、内存状态的监控,首先修改亲和性,让 KubeSphere 自带的 node-exporter 能够采集边缘节点监控数据,接下来利用 KubeEdge 的 EdgeMesh 将采集的数据提供给 KubeSphere 的 Prometheus。这样就实现了 CPU、内存信息的监控。

    对于边缘节点 GPU 状态的监控,安装 jtop 获取 GPU 使用率、温度等数据,然后开发 Jetson GPU Exporter,将 jtop 获取的信息发送给 KubeSphere 的 Prometheus,通过修改 KubeSphere 前端 ks-console 的代码,在界面上通过 http 接口获取 Prometheus 数据,这样就实现了 GPU 使用率等信息监控。