智能运维新时代：基于AI的动态配置管理

import pandas as pd
from sklearn.preprocessing import StandardScaler

# 读取服务器运行数据
data = pd.read_csv("server_data.csv")

# 数据清洗：去除缺失值
data = data.dropna()

# 数据预处理：标准化处理
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# 划分训练集和测试集
X = scaled_data[:, :-1]
y = scaled_data[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 构建随机森林回归模型
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 评估模型性能
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"模型均方误差: {mse}")

import time

# 假设我们有一个函数可以获取实时服务器状态数据
def get_server_status():
    # 模拟获取服务器状态数据
    return [0.5, 0.3, 0.8, 0.2]  # 示例数据

# 根据模型预测结果动态调整配置
while True:
    status = get_server_status()
    status_scaled = scaler.transform([status])
    prediction = model.predict(status_scaled)
    
    if prediction > 0.7:  # 预测负载高时进行调整
        print("高负载，调整服务器配置以提升性能")
        # 执行配置调整的具体操作
    else:
        print("负载正常，无需调整")
    
    time.sleep(60)  # 每分钟检查一次