模型服务CPU负载均衡监控策略

监控指标配置

在模型服务中，需要重点监控以下CPU相关指标：

1. CPU使用率：cpu_usage_percent
2. CPU负载均衡：cpu_load_balancing_ratio
3. CPU核心利用率：cpu_core_utilization
4. 进程CPU占用：process_cpu_percent

告警配置方案

告警阈值设置

alerts:
  cpu_usage_high:
    threshold: 85
    duration: 5m
    severity: warning
  cpu_balancing_unequal:
    threshold: 0.3
    duration: 10m
    severity: critical

监控脚本实现

import psutil
import time
from prometheus_client import Gauge

# 创建指标
cpu_usage = Gauge('model_cpu_usage_percent', 'CPU usage percentage')
cpu_load_balance = Gauge('model_cpu_load_balancing_ratio', 'Load balancing ratio')

# 监控函数
async def monitor_cpu():
    while True:
        # 获取系统CPU使用率
        cpu_percent = psutil.cpu_percent(interval=1)
        cpu_usage.set(cpu_percent)
        
        # 计算负载均衡比率
        cpu_per_core = psutil.cpu_percent(percpu=True)
        max_core = max(cpu_per_core)
        min_core = min(cpu_per_core)
        balance_ratio = (max_core - min_core) / max_core if max_core > 0 else 0
        cpu_load_balance.set(balance_ratio)
        
        # 检查告警条件
        if cpu_percent > 85:
            # 发送警告
            pass
        
        if balance_ratio > 0.3:
            # 发送严重告警
            pass
        
        await asyncio.sleep(60)

复现步骤

1. 部署Prometheus监控服务
2. 配置模型服务指标收集
3. 设置告警规则文件
4. 部署告警通知系统