负载均衡（Load Balancing）是聊聊一种计算机网络和服务器管理技术，旨在分配网络流量、种负载均请求或工作负载到多个服务器或资源，衡算以确保这些服务器能够高效、聊聊均匀地处理负载，种负载均并且能够提供更高的衡算性能、可用性和可扩展性。聊聊

这篇文章，种负载均我们聊聊六种通用的衡算负载均衡算法。

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1.轮询 (Round Robin)

轮询是聊聊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种负载均每一台服务器，而不关心服务器实际的衡算连接数和当前的系统负载。

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示例代码：

复制import java.util.List; import java.util.concurrent.atomic.AtomicInteger; publicclass RoundRobin { privatefinal List<String> servers; privatefinal 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); } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.

2.粘性轮询 (Sticky Round-Robin)

粘性轮询是聊聊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种负载均映射关系，确保来自同一客户端的衡算连续请求会被路由到同一个服务实例上。

它的亿华云特点是：

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

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示例代码：

复制import java.util.List; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.atomic.AtomicInteger; publicclass StickyRoundRobin { privatefinal List<String> servers; privatefinal AtomicInteger index = new AtomicInteger(0); privatefinal 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())); } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.

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。

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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：

复制private static Map<String, Integer> serverMap = new ConcurrentHashMap<>(); //记录服务器权重总和 privatestaticint 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null if (serverCount == 0) { returnnull; } //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null returnnull; } publicclass 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); } } @Override public 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; } returnnull; } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.

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的服务器租用请求总是被转发到同一台服务器。

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示例代码：

复制import java.util.List; import java.util.zip.CRC32; publicclass SourceIPHashLoadBalancer { privatefinal List<String> servers; public SourceIPHashLoadBalancer(List<String> servers) { this.servers = servers; } public String getServer(String clientIP) { if (servers.isEmpty()) { returnnull; } // 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(); } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.

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。

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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：

复制import java.util.List; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.atomic.AtomicInteger; publicclass LeastConnectionsLoadBalancer { privatefinal List<String> servers; privatefinal 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()) { returnnull; } // 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(); } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.

6.最快响应时间 (Least Response Time)

最快响应时间（Least Response Time，LRT）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。

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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：

复制import java.util.*; import java.util.concurrent.*; import java.util.concurrent.atomic.*; publicclass LeastResponseTimeLoadBalancer { privatefinal List<String> servers; privatefinal ConcurrentHashMap<String, ResponseTimeStats> serverStats; // 响应时间统计结构 staticclass ResponseTimeStats { privatefinal AtomicInteger totalRequests = new AtomicInteger(0); privatefinal AtomicLong totalResponseTime = new AtomicLong(0); privatevolatileboolean 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()) returnnull; 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; } }1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.

(责任编辑：IT科技)