LLM负载均衡的特殊性

传统Web服务的负载均衡（轮询、加权轮询）在LLM场景下效果不佳——LLM请求的长度差异巨大（10 token vs 10000 token），处理时间差异可达100倍。简单的轮询会导致某些节点被长请求占满，而短请求也被迫排队。

策略一：最小连接数

class LeastConnectionsBalancer:
    def __init__(self, backends):
        self.backends = {b: 0 for b in backends}  # backend -> active_connections
        self.lock = asyncio.Lock()
    
    async def get_backend(self):
        async with self.lock:
            backend = min(self.backends, key=self.backends.get)
            self.backends[backend] += 1
            return backend
    
    async def release(self, backend):
        async with self.lock:
            self.backends[backend] -= 1

策略二：基于队列长度

class QueueAwareBalancer:
    def __init__(self, backends):
        self.queues = {b: asyncio.Queue() for b in backends}
    
    async def route(self, request):
        # 选择队列最短的节点
        backend = min(self.queues, key=lambda b: self.queues[b].qsize())
        await self.queues[backend].put(request)
        return backend

策略三：延迟感知

class LatencyAwareBalancer:
    def __init__(self, backends):
        self.backends = backends
        self.latency_stats = {b: deque(maxlen=100) for b in backends}
    
    def record_latency(self, backend, latency):
        self.latency_stats[backend].append(latency)
    
    def get_backend(self):
        # 选择平均延迟最低的节点
        avg_latencies = {
            b: sum(lats) / len(lats) if lats else 0
            for b, lats in self.latency_stats.items()
        }
        return min(avg_latencies, key=avg_latencies.get)

策略四：请求长度路由

class LengthAwareRouter:
    def __init__(self, short_backends, long_backends, threshold=500):
        self.short_backends = short_backends  # 小模型，处理短请求
        self.long_backends = long_backends    # 大模型，处理长请求
        self.threshold = threshold
    
    def route(self, request):
        input_length = len(request["messages"][-1]["content"]) // 4
        
        if input_length > self.threshold:
            return self.select_least_loaded(self.long_backends)
        else:
            return self.select_least_loaded(self.short_backends)

健康检查

class HealthChecker:
    def __init__(self, backends, check_interval=10):
        self.backends = {b: {"healthy": True, "last_check": 0} for b in backends}
        self.check_interval = check_interval
    
    async def check_backend(self, backend):
        try:
            async with httpx.AsyncClient() as client:
                resp = await client.get(f"{backend}/health", timeout=5)
                return resp.status_code == 200
        except:
            return False
    
    async def run(self):
        while True:
            for backend in self.backends:
                healthy = await self.check_backend(backend)
                self.backends[backend]["healthy"] = healthy
                if not healthy:
                    logger.warning(f"Backend {backend} unhealthy")
            await asyncio.sleep(self.check_interval)

结语

LLM负载均衡需要考虑请求长度差异、节点异构性和KV Cache状态。最小连接数+延迟感知的组合策略在大多数场景下表现最佳。配合健康检查和自动故障转移，可以构建高可用的LLM推理服务。

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