大模型微服务负载均衡策略对比

大模型微服务负载均衡策略对比

在大模型微服务架构中，负载均衡策略直接影响系统性能和用户体验。本文将对比三种主流策略：轮询、加权轮询和最少连接。

轮询策略实现

import requests
import time
from collections import defaultdict

class RoundRobinBalancer:
    def __init__(self, servers):
        self.servers = servers
        self.current_index = 0
    
    def get_next_server(self):
        server = self.servers[self.current_index]
        self.current_index = (self.current_index + 1) % len(self.servers)
        return server

# 使用示例
servers = ['http://model1:8000', 'http://model2:8000', 'http://model3:8000']
balancer = RoundRobinBalancer(servers)

加权轮询策略

import random

class WeightedRoundRobinBalancer:
    def __init__(self, servers_with_weights):
        self.servers = servers_with_weights
        self.current_weight = 0
        self.max_weight = max(weight for _, weight in servers_with_weights)
    
    def get_next_server(self):
        # 实现加权轮询逻辑
        pass

最少连接策略

import threading
from collections import defaultdict

class LeastConnectionsBalancer:
    def __init__(self):
        self.connection_count = defaultdict(int)
        self.lock = threading.Lock()
    
    def get_next_server(self, servers):
        with self.lock:
            # 选择连接数最少的服务器
            min_connections = float('inf')
            selected_server = None
            for server in servers:
                if self.connection_count[server] < min_connections:
                    min_connections = self.connection_count[server]
                    selected_server = server
            return selected_server

在实际部署中，建议根据大模型推理延迟和资源使用率动态调整策略，结合Prometheus监控指标进行优化。