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
KubeSphere	3.4.1
containerd	1.7.2
K8s	1.26.0
KubeEdge	1.15.1
Jetson 型号	NVIDIA Jetson Xavier NX (16GB ram)
Jtop	4.2.7
JetPack	5.1.3-b29
Docker	24.0.5

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

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

2. 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

1. 修改 configmap--ClusterConfiguration

2. 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_HOST 和 KUBERNETES_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_HOST 和 KUBERNETES_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

1. 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

2. 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"}}

3. 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。

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