当前位置：首页 > news >正文

聊聊六种负载均衡算法

news 2025/10/13 8:55:22

负载均衡（Load Balancing）是一种计算机网络和服务器管理技术，旨在分配网络流量、请求或工作负载到多个服务器或资源，以确保这些服务器能够高效、均匀地处理负载，并且能够提供更高的性能、可用性和可扩展性。

这篇文章，我们聊聊六种通用的负载均衡算法。

1 轮询 (Round Robin)

轮询是指将请求按顺序轮流地分配到后端服务器上，它均衡地对待后端的每一台服务器，而不关心服务器实际的连接数和当前的系统负载。

示例代码：

import java.util.List;
import java.util.concurrent.atomic.AtomicInteger;public class RoundRobin {private final List<String> servers;private final AtomicInteger index = new AtomicInteger(0);public RoundRobin(List<String> servers) {this.servers = servers;}public String getServer() {int currentIndex = index.getAndIncrement() % servers.size();return servers.get(currentIndex);}
}

2 粘性轮询 (Sticky Round-Robin)

粘性轮询是标准轮询算法的一个变种，它通过记住客户端与服务实例的映射关系，确保来自同一客户端的连续请求会被路由到同一个服务实例上。

它的特点是：

会话保持：一旦客户端首次请求被分配到某个服务实例，后续请求会"粘"在这个实例上
客户端识别：通常基于客户端IP、会话ID或特定HTTP头来识别客户端
故障转移：当目标服务实例不可用时，系统会重新分配客户端到其他可用实例

示例代码：

import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicInteger;public class StickyRoundRobin {private final List<String> servers;private final AtomicInteger index = new AtomicInteger(0);private final Map<String, String> clientToServer = new ConcurrentHashMap<>();public StickyRoundRobin(List<String> servers) {this.servers = servers;}public String getServer(String clientId) {return clientToServer.computeIfAbsent(clientId, k -> servers.get(index.getAndIncrement() % servers.size()));}
}

3 加权轮询 (Weighted Round-Robin)

加权轮询是标准轮询算法的增强版本，它允许管理员为每个服务实例分配不同的权重值。权重越高的实例处理越多的请求，从而实现更精细的负载分配。

它的特点是:

权重分配：每个服务实例都有对应的权重值
比例分配：请求按权重比例分配到不同实例
动态调整：权重可以动态修改以适应不同场景

示例代码：

private static Map<String, Integer> serverMap = new ConcurrentHashMap<>();
//记录服务器权重总和
private static int totalWeight = 0;public static String weightRandom() {//获取服务器数量int serverCount = serverMap.size();//如果没有可用的服务器返回nullif (serverCount == 0) {return null;}//在此处为避免多线程并发操作造成错误，在方法内部进行锁操作synchronized (serverMap) {//计算服务器权重总和for (Map.Entry<String, Integer> entry : serverMap.entrySet()) {totalWeight += entry.getValue();}//生成一个随机数int randomWeight = new Random().nextInt(totalWeight);//遍历服务器列表，根据服务器权重值选择对应地址for (Map.Entry<String, Integer> entry : serverMap.entrySet()) {String serverAddress = entry.getKey();Integer weight = entry.getValue();randomWeight -= weight;if (randomWeight < 0) {return serverAddress;}}}//默认返回nullreturn null;
}public class WeightRandomLoadBalancer implements LoadBalancer {private List<String> servers = new ArrayList<>();private Map<String, Integer> weightMap = new HashMap<>();public WeightRandomLoadBalancer(Map<String, Integer> servers) {this.servers.addAll(servers.keySet());for (String server : servers.keySet()) {int weight = servers.get(server);weightMap.put(server, weight);}}@Overridepublic String chooseServer() {int weightSum = weightMap.values().stream().reduce(Integer::sum).orElse(0);int randomWeight = ThreadLocalRandom.current().nextInt(weightSum) + 1;for (String server : servers) {int weight = weightMap.get(server);if (randomWeight <= weight) {return server;}randomWeight -= weight;}return null;}
}

4 源地址哈希法 (Hash)

源地址哈希法是一种基于客户端 IP 地址的负载均衡算法，通过哈希函数将客户端IP映射到特定的服务器，确保来自同一IP的请求总是被转发到同一台服务器。

示例代码：

import java.util.List;
import java.util.zip.CRC32;public class SourceIPHashLoadBalancer {private final List<String> servers;public SourceIPHashLoadBalancer(List<String> servers) {this.servers = servers;}public String getServer(String clientIP) {if (servers.isEmpty()) {return null;}// 计算IP的哈希值long hash = calculateHash(clientIP);// 取模确定服务器索引int index = (int) (hash % servers.size());return servers.get(Math.abs(index));}private long calculateHash(String ip) {CRC32 crc32 = new CRC32();crc32.update(ip.getBytes());return crc32.getValue();}
}

5 最少连接 (Least Connections)

最少连接算法是一种动态负载均衡策略，它会将新请求分配给当前连接数最少的服务器，以实现更均衡的服务器负载分配。

它的特点是:

实时监控：跟踪每台服务器的活跃连接数
动态决策：新请求总是分配给当前连接数最少的服务器
自适应：自动适应不同请求处理能力的服务器

示例代码：

import java.util.List;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicInteger;public class LeastConnectionsLoadBalancer {private final List<String> servers;private final ConcurrentHashMap<String, AtomicInteger> connectionCounts;public LeastConnectionsLoadBalancer(List<String> servers) {this.servers = servers;this.connectionCounts = new ConcurrentHashMap<>();servers.forEach(server -> connectionCounts.put(server, new AtomicInteger(0)));}public String getServer() {if (servers.isEmpty()) {return null;}// 找出连接数最少的服务器String selectedServer = servers.get(0);int minConnections = connectionCounts.get(selectedServer).get();for (String server : servers) {int currentConnections = connectionCounts.get(server).get();if (currentConnections < minConnections) {minConnections = currentConnections;selectedServer = server;}}// 增加选中服务器的连接数connectionCounts.get(selectedServer).incrementAndGet();return selectedServer;}public void releaseConnection(String server) {connectionCounts.get(server).decrementAndGet();}
}

6 最快响应时间 (Least Response Time)

最快响应时间（Least Response Time，LRT）是一种智能动态负载均衡算法，它通过选择当前响应时间最短的服务器来处理新请求，从而优化整体系统性能。

LRT 算法基于以下核心判断标准：

实时性能监控：持续跟踪每台服务器的历史响应时间
动态路由决策：新请求总是分配给响应最快的可用服务器
自适应学习：根据服务器性能变化自动调整流量分配

示例代码：

import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.*;public class LeastResponseTimeLoadBalancer {private final List<String> servers;private final ConcurrentHashMap<String, ResponseTimeStats> serverStats;// 响应时间统计结构static class ResponseTimeStats {private final AtomicInteger totalRequests = new AtomicInteger(0);private final AtomicLong totalResponseTime = new AtomicLong(0);private volatile boolean isHealthy = true;public void recordResponseTime(long responseTimeMs) {totalRequests.incrementAndGet();totalResponseTime.addAndGet(responseTimeMs);}public double getAverageResponseTime() {int requests = totalRequests.get();return requests == 0 ? 0 : (double)totalResponseTime.get() / requests;}}public LeastResponseTimeLoadBalancer(List<String> servers) {this.servers = new CopyOnWriteArrayList<>(servers);this.serverStats = new ConcurrentHashMap<>();servers.forEach(server -> serverStats.put(server, new ResponseTimeStats()));}public String getServer() {if (servers.isEmpty()) return null;return servers.stream().filter(server -> serverStats.get(server).isHealthy).min(Comparator.comparingDouble(server -> serverStats.get(server).getAverageResponseTime())).orElse(null);}public void updateResponseTime(String server, long responseTimeMs) {ResponseTimeStats stats = serverStats.get(server);if (stats != null) {stats.recordResponseTime(responseTimeMs);}}public void markServerDown(String server) {ResponseTimeStats stats = serverStats.get(server);if (stats != null) stats.isHealthy = false;}public void markServerUp(String server) {ResponseTimeStats stats = serverStats.get(server);if (stats != null) stats.isHealthy = true;}
}

查看全文

http://www.sczhlp.com/news/57047/