# 2. 实施步骤
# 步骤1：分析容器性能瓶颈
# 步骤2：调整容器资源限制
# 步骤3：优化容器镜像
# 步骤4：优化网络配置
# 步骤5：验证性能改进
# 步骤6：测试与验证

# 3. 应用效果
# 提高了Web应用的响应速度
# 增加了并发处理能力
# 减少了容器的资源使用

# 优化容器镜像
# 使用多阶段构建
cat > Dockerfile << 'EOF' FROM node:14-alpine as build WORKDIR /app COPY package*.json ./ RUN npm install COPY . . RUN npm run build FROM nginx:alpine COPY --from=build /app/build /usr/share/nginx/html EXPOSE 80 CMD ["nginx", "-g", "daemon off;"] EOF # 构建镜像 docker build -t fgedu/web:latest . # 运行容器，设置高级资源限制 docker run -d \ --name fgedu-web \ --cpus="2" \ --memory="4g" \ --memory-swap="4g" \ --cpuset-cpus="0-1" \ --blkio-weight="500" \ --network=host \ --restart unless-stopped \ fgedu/web:latest # 监控容器性能 docker stats fgedu-web # 测试性能 ab -n 10000 -c 1000 http://localhost/

# 2. 实施步骤
# 步骤1：分析容器性能瓶颈
# 步骤2：调整Pod资源限制
# 步骤3：优化Pod调度
# 步骤4：优化网络配置
# 步骤5：验证性能改进
# 步骤6：测试与验证

# 3. 应用效果
# 提高了微服务应用的响应速度
# 增加了并发处理能力
# 减少了Pod的资源使用

# 配置Pod资源限制和调度
cat > deployment.yaml << 'EOF' apiVersion: apps/v1 kind: Deployment metadata: name: fgedu-microservice namespace: default spec: replicas: 3 selector: matchLabels: app: fgedu-microservice template: metadata: labels: app: fgedu-microservice spec: nodeSelector: role: worker affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: app operator: In values: - fgedu-microservice topologyKey: "kubernetes.io/hostname" containers: - name: fgedu-microservice image: fgedu/microservice:latest resources: requests: cpu: "1" memory: "2Gi" limits: cpu: "2" memory: "4Gi" readinessProbe: httpGet: path: /health port: 8080 initialDelaySeconds: 5 periodSeconds: 10 livenessProbe: httpGet: path: /health port: 8080 initialDelaySeconds: 15 periodSeconds: 20 ports: - containerPort: 8080 EOF # 应用配置 kubectl apply -f deployment.yaml # 配置服务 cat > service.yaml << 'EOF' apiVersion: v1 kind: Service metadata: name: fgedu-microservice namespace: default spec: selector: app: fgedu-microservice ports: - port: 80 targetPort: 8080 type: LoadBalancer EOF # 应用服务配置 kubectl apply -f service.yaml # 查看Pod状态 kubectl get pods # 查看Pod性能 kubectl top pod # 测试性能 ab -n 10000 -c 1000 http://$(kubectl get svc fgedu-microservice -o jsonpath='{.status.loadBalancer.ingress[0].ip}')/

# 2. 实施步骤
# 步骤1：分析网络性能瓶颈
# 步骤2：选择合适的网络插件
# 步骤3：优化网络配置
# 步骤4：配置网络策略
# 步骤5：验证性能改进
# 步骤6：测试与验证

# 3. 应用效果
# 提高了容器间的通信速度
# 减少了网络延迟
# 增加了网络吞吐量

# 安装Calico网络插件
kubectl apply -f https://docs.projectcalico.org/manifests/calico.yaml

# 配置网络策略
cat > network-policy.yaml << 'EOF' apiVersion: networking.k8s.io/v1 kind: NetworkPolicy metadata: name: fgedu-network-policy namespace: default spec: podSelector: matchLabels: app: fgedu-app policyTypes: - Ingress - Egress ingress: - from: - podSelector: matchLabels: app: fgedu-frontend ports: - protocol: TCP port: 8080 egress: - to: - podSelector: matchLabels: app: fgedu-db ports: - protocol: TCP port: 3306 EOF # 应用网络策略 kubectl apply -f network-policy.yaml # 测试Pod间通信 kubectl exec -it $(kubectl get pods -l app=fgedu-frontend -o jsonpath='{.items[0].metadata.name}') -- ping -c 10 $(kubectl get pods -l app=fgedu-app -o jsonpath='{.items[0].metadata.name}') # 测试网络性能 kubectl exec -it $(kubectl get pods -l app=fgedu-frontend -o jsonpath='{.items[0].metadata.name}') -- iperf3 -c $(kubectl get pods -l app=fgedu-app -o jsonpath='{.items[0].status.podIP}')