# 启动Prometheus
sudo systemctl start prometheus
sudo systemctl enable prometheus

# 验证Prometheus
curl http://localhost:9090

# 安装Grafana
sudo dnf install grafana

# 启动Grafana
sudo systemctl start grafana-server
sudo systemctl enable grafana-server

# 验证Grafana
curl http://localhost:3000

# 启动Alertmanager
sudo systemctl start prometheus-alertmanager
sudo systemctl enable prometheus-alertmanager

# 安装机器学习异常检测工具
sudo pip3 install prometheus-anomaly-detector

# 配置异常检测
sudo vim /etc/prometheus/anomaly-detector.yml

# 启动异常检测服务
sudo systemctl start prometheus-anomaly-detector
sudo systemctl enable prometheus-anomaly-detector

# 配置Prometheus数据导出
sudo vim /etc/prometheus/prometheus.yml

# 导出数据到文件
promtool tsdb dump /var/lib/prometheus | gzip > /data/metrics/prometheus_dump.gz

# 安装数据处理工具
sudo pip3 install pandas numpy scikit-learn

# 数据预处理脚本
cat > /data/metrics/preprocess.py << 'EOF' import pandas as pd import numpy as np # 加载数据 data = pd.read_csv('metrics.csv') # 数据清洗 data = data.dropna() # 特征工程 data['cpu_usage'] = data['cpu_user'] + data['cpu_system'] data['memory_usage_percent'] = (data['memory_used'] / data['memory_total']) * 100 # 保存处理后的数据 data.to_csv('processed_metrics.csv', index=False) EOF # 运行预处理脚本 python3 /data/metrics/preprocess.py

# 训练模型脚本
cat > /data/metrics/train_model.py << 'EOF' import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier import joblib # 加载数据 data = pd.read_csv('processed_metrics.csv') # 准备特征和标签 X = data[['cpu_usage', 'memory_usage_percent', 'disk_io', 'network_io']] y = data['fault_label'] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练模型 model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) # 评估模型 accuracy = model.score(X_test, y_test) print(f"模型准确率: {accuracy:.2f}") # 保存模型 joblib.dump(model, 'fault_prediction_model.joblib') EOF # 运行训练脚本 python3 /data/metrics/train_model.py

# 创建自动化运维剧本
cat > /etc/ansible/playbooks/auto_remediation.yml << 'EOF' --- - name: 自动修复系统问题 hosts: all become: yes tasks: - name: 检查CPU使用率 shell: "top -bn1 | grep 'Cpu(s)' | awk '{print $2}'" register: cpu_usage - name: 检查内存使用率 shell: "free | grep Mem | awk '{print $3/$2 * 100.0}'" register: memory_usage - name: 清理系统缓存 shell: "sync && echo 3 > /proc/sys/vm/drop_caches”
when: “memory_usage.stdout|float > 80”

– name: 重启高CPU进程
shell: “ps aux –sort=-%cpu | head -n 3 | awk ‘{print $2}’ | xargs kill -9”
when: “cpu_usage.stdout|float > 90”
EOF

# 运行自动化脚本
ansible-playbook /etc/ansible/playbooks/auto_remediation.yml