降低90%推理成本：腾讯混元+云函数动态扩缩容策略详解

# 负载系数计算（Python实现）
def calculate_load(window_size=60):
    # 获取最近N秒的请求指标
    metrics = get_metrics_from_prometheus(window_size)
    
    # 计算关键指标加权值
    cpu_weight = 0.4
    mem_weight = 0.3
    rps_weight = 0.3
    
    load_score = (metrics['cpu'] * cpu_weight +
                 metrics['mem'] * mem_weight +
                 metrics['rps'] * rps_weight)
    
    # 应用指数平滑
    alpha = 0.7
    smoothed_score = alpha * load_score + (1 - alpha) * last_load_score
    
    return max(0, min(smoothed_score, 1))  # 归一化到[0,1]

def scale_decision(current_instances, load_score):
    # 定义扩缩容阈值
    SCALE_UP_THRESHOLD = 0.8
    SCALE_DOWN_THRESHOLD = 0.3
    MAX_INSTANCES = 50
    MIN_INSTANCES = 1
    
    # 扩容逻辑
    if load_score > SCALE_UP_THRESHOLD:
        # 指数退避扩容：每超出10%负载增加一倍实例
        over_load = load_score - SCALE_UP_THRESHOLD
        scale_factor = 2 ** (over_load * 10) 
        new_instances = min(MAX_INSTANCES, 
                           ceil(current_instances * scale_factor))
        return new_instances
    
    # 缩容逻辑
    elif load_score < SCALE_DOWN_THRESHOLD:
        # 对数缩容：保留sqrt(N)个实例
        new_instances = max(MIN_INSTANCES,
                           floor(sqrt(current_instances)))
        return new_instances
    
    return current_instances

# 预加载脚本（Bash实现）
#!/bin/bash

# 阶段1：基础环境预热
docker pull tencenthunyuan/hunyuan-serving:latest &
preload_model "embedding_layer.bin" &

# 阶段2：增量加载
wait # 等待基础资源完成
preload_model "transformer_block_*.bin" --parallel 4 &

# 阶段3：运行时优化
tune_runtime_parameters \
  --max_batch_size 8 \
  --fp16_enabled true \
  --kv_cache_size 2048

节约成本 = 1 - (动态扩缩容成本 / 传统部署成本)
         = 1 - (2760 / 28400) 
         = 90.28%

# 异常流量熔断器（Python实现）
class CircuitBreaker:
    def __init__(self, failure_threshold=5, recovery_timeout=30):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.state = "CLOSED"
        
    def protect(self, func):
        def wrapper(*args, **kwargs):
            if self.state == "OPEN":
                raise SystemBusyError("服务熔断中")
                
            try:
                result = func(*args, **kwargs)
                self._reset_counter()
                return result
            except Exception as e:
                self.failure_count += 1
                if self.failure_count >= self.failure_threshold:
                    self._trip_circuit()
                raise
        return wrapper
        
    def _trip_circuit(self):
        self.state = "OPEN"
        Timer(self.recovery_timeout, self._reset).start()
        
    def _reset(self):
        self.state = "HALF_OPEN"
        self.failure_count = 0
        
    def _reset_counter(self):
        if self.state == "HALF_OPEN":
            self.state = "CLOSED"

# serverless.yml
components:
  hunyuan_scf:
    component: "@tencent/hunyuan-scf"
    inputs:
      name: hunyuan-serving
      region: ap-shanghai
      memorySize: 4096   # 弹性内存配置
      timeout: 60
      environment:
        variables:
          MODEL_VERSION: "13B-4bit"
      triggers:
        - type: apigw
          parameters:
            protocols:
              - http
            serviceName: hunyuan-service
      policies:
        auto_scaling:
          min: 1
          max: 50
          thresholds:
            cpu: 80
            memory: 75
            outFlux: 1000000 # 1MB/s

预留实例数 = 日均QPS × P95响应时间 / 86400