前言
上一篇 讲了普通轮询、加权轮询的两种实现方式，重点讲了平滑加权轮询算法，并在文末留下了悬念： 节点出现分配失败时降低有效权重值；成功时提高有效权重值（但不能大于weight值） 。
本文在平滑加权轮询算法的基础上讲，还没弄懂的可以看上一篇文章。
现在来 模拟实现 ：平滑加权轮询算法的降权和提权
1.两个关键点
节点宕机时，降低有效权重值；
节点正常时，提高有效权重值（但不能大于weight值）；
注意：降低或提高权重都是针对 有效权重 。
2.代码实现
2.1.服务节点类
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package com.yty.loadbalancingalgorithm.wrr;
/** * String ip：负载IP * final Integer weight：权重，保存配置的权重 * Integer effectiveWeight：有效权重，轮询的过程权重可能变化 * Integer currentWeight：当前权重，比对该值大小获取节点 * 第一次加权轮询时：currentWeight = weight = effectiveWeight * 后面每次加权轮询时：currentWeight 的值都会不断变化，其他权重不变 * Boolean isAvailable：是否存活 */ public class ServerNode implements Comparable<ServerNode>{ private String ip; private final Integer weight; private Integer effectiveWeight; private Integer currentWeight; private Boolean isAvailable;
public ServerNode(String ip, Integer weight){ this (ip,weight, true ); } public ServerNode(String ip, Integer weight,Boolean isAvailable){ this .ip = ip; this .weight = weight; this .effectiveWeight = weight; this .currentWeight = weight; this .isAvailable = isAvailable; }
public String getIp() { return ip; }
public void setIp(String ip) { this .ip = ip; }
public Integer getWeight() { return weight; }
public Integer getEffectiveWeight() { return effectiveWeight; }
public void setEffectiveWeight(Integer effectiveWeight) { this .effectiveWeight = effectiveWeight; }
public Integer getCurrentWeight() { return currentWeight; }
public void setCurrentWeight(Integer currentWeight) { this .currentWeight = currentWeight; }
public Boolean isAvailable() { return isAvailable; } public void setIsAvailable(Boolean isAvailable){ this .isAvailable = isAvailable; }
// 每成功一次，恢复有效权重1，不超过配置的起始权重 public void onInvokeSuccess(){ if (effectiveWeight < weight) effectiveWeight++; } // 每失败一次，有效权重减少1，无底线的减少 public void onInvokeFault(){ effectiveWeight--; }
@Override public int compareTo(ServerNode node) { return currentWeight > node.currentWeight ? 1 : (currentWeight.equals(node.currentWeight) ? 0 : - 1 ); }
@Override public String toString() { return "{ip='" + ip + "', weight=" + weight + ", effectiveWeight=" + effectiveWeight + ", currentWeight=" + currentWeight + ", isAvailable=" + isAvailable + "}" ; } } |
2.2.平滑轮询算法降权和提权
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package com.yty.loadbalancingalgorithm.wrr;
import java.util.ArrayList; import java.util.List;
/** * 加权轮询算法：加入存活状态，降权使宕机权重降低，从而不会被选中 */ public class WeightedRoundRobinAvailable {
private static List<ServerNode> serverNodes = new ArrayList<>(); // 准备模拟数据 static { serverNodes.add( new ServerNode( "192.168.1.101" , 1 )); // 默认为true serverNodes.add( new ServerNode( "192.168.1.102" , 3 , false )); serverNodes.add( new ServerNode( "192.168.1.103" , 2 )); }
/** * 按照当前权重（currentWeight）最大值获取IP * @return ServerNode */ public ServerNode selectNode(){ if (serverNodes.size() <= 0 ) return null ; if (serverNodes.size() == 1 ) return (serverNodes.get( 0 ).isAvailable()) ? serverNodes.get( 0 ) : null ;
// 权重之和 Integer totalWeight = 0 ; ServerNode nodeOfMaxWeight = null ; // 保存轮询选中的节点信息 synchronized (serverNodes){ StringBuffer sb1 = new StringBuffer(); StringBuffer sb2 = new StringBuffer(); sb1.append(Thread.currentThread().getName()+ "==加权轮询--[当前权重]值的变化：" +printCurrentWeight(serverNodes)); // 有限权重总和可能发生变化 for (ServerNode serverNode : serverNodes){ totalWeight += serverNode.getEffectiveWeight(); }
// 选出当前权重最大的节点 ServerNode tempNodeOfMaxWeight = serverNodes.get( 0 ); for (ServerNode serverNode : serverNodes) { if (serverNode.isAvailable()) { serverNode.onInvokeSuccess(); //提权 sb2.append(Thread.currentThread().getName()+ "==[正常节点]：" +serverNode+ "\n" ); } else { serverNode.onInvokeFault(); //降权 sb2.append(Thread.currentThread().getName()+ "==[宕机节点]：" +serverNode+ "\n" ); }
tempNodeOfMaxWeight = tempNodeOfMaxWeight.compareTo(serverNode) > 0 ? tempNodeOfMaxWeight : serverNode; } // 必须new个新的节点实例来保存信息，否则引用指向同一个堆实例，后面的set操作将会修改节点信息 nodeOfMaxWeight = new ServerNode(tempNodeOfMaxWeight.getIp(),tempNodeOfMaxWeight.getWeight(),tempNodeOfMaxWeight.isAvailable()); nodeOfMaxWeight.setEffectiveWeight(tempNodeOfMaxWeight.getEffectiveWeight()); nodeOfMaxWeight.setCurrentWeight(tempNodeOfMaxWeight.getCurrentWeight());
// 调整当前权重比：按权重（effectiveWeight）的比例进行调整，确保请求分发合理。 tempNodeOfMaxWeight.setCurrentWeight(tempNodeOfMaxWeight.getCurrentWeight() - totalWeight); sb1.append( " -> " +printCurrentWeight(serverNodes));
serverNodes.forEach(serverNode -> serverNode.setCurrentWeight(serverNode.getCurrentWeight()+serverNode.getEffectiveWeight()));
sb1.append( " -> " +printCurrentWeight(serverNodes)); System.out.print(sb2); //所有节点的当前信息 System.out.println(sb1); //打印当前权重变化过程 } return nodeOfMaxWeight; }
// 格式化打印信息 private String printCurrentWeight(List<ServerNode> serverNodes){ StringBuffer stringBuffer = new StringBuffer( "[" ); serverNodes.forEach(node -> stringBuffer.append(node.getCurrentWeight()+ "," ) ); return stringBuffer.substring( 0 , stringBuffer.length() - 1 ) + "]" ; }
// 并发测试：两个线程循环获取节点 public static void main(String[] args) throws InterruptedException { // 循环次数 int loop = 18 ;
new Thread(() -> { WeightedRoundRobinAvailable weightedRoundRobin1 = new WeightedRoundRobinAvailable(); for ( int i= 1 ;i<=loop;i++){ ServerNode serverNode = weightedRoundRobin1.selectNode(); System.out.println(Thread.currentThread().getName()+ "==第" +i+ "次轮询选中[当前权重最大]的节点：" + serverNode + "\n" ); } }).start(); // new Thread(() -> { WeightedRoundRobinAvailable weightedRoundRobin2 = new WeightedRoundRobinAvailable(); for ( int i= 1 ;i<=loop;i++){ ServerNode serverNode = weightedRoundRobin2.selectNode(); System.out.println(Thread.currentThread().getName()+ "==第" +i+ "次轮询选中[当前权重最大]的节点：" + serverNode + "\n" ); } }).start();
//main 线程睡了一下，再偷偷把 所有宕机 拉起来：模拟服务器恢复正常 Thread.sleep( 5 ); for (ServerNode serverNode:serverNodes){ if (!serverNode.isAvailable()) serverNode.setIsAvailable( true ); } } } |
3.分析结果
执行结果：将执行结果的 前中后 四次抽出来分析
Thread-0==[正常节点]：{ip='192.168.1.101', weight=1, effectiveWeight=1, currentWeight=1, isAvailable=true}
Thread-0==[宕机节点]：{ip='192.168.1.102', weight=3, effectiveWeight=2, currentWeight=3, isAvailable=false}
Thread-0==[正常节点]：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=2, isAvailable=true}
Thread-0==加权轮询--[当前权重]值的变化：[1,3,2] -> [1,-3,2] -> [2,-1,4]
Thread-0==第1次轮询选中[当前权重最大]的节点：{ip='192.168.1.102', weight=3, effectiveWeight=2, currentWeight=3, isAvailable=false}
……
Thread-1==[正常节点]：{ip='192.168.1.101', weight=1, effectiveWeight=1, currentWeight=6, isAvailable=true}
Thread-1==[宕机节点]：{ip='192.168.1.102', weight=3, effectiveWeight=-7, currentWeight=-21, isAvailable=false}
Thread-1==[正常节点]：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=12, isAvailable=true}
Thread-1==加权轮询--[当前权重]值的变化：[6,-21,12] -> [6,-21,15] -> [7,-28,17]
Thread-1==第5次轮询选中[当前权重最大]的节点：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=12, isAvailable=true}
……
Thread-0==[正常节点]：{ip='192.168.1.101', weight=1, effectiveWeight=1, currentWeight=13, isAvailable=true}
Thread-0==[正常节点]：{ip='192.168.1.102', weight=3, effectiveWeight=3, currentWeight=-19, isAvailable=true}
Thread-0==[正常节点]：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=12, isAvailable=true}
Thread-0==加权轮询--[当前权重]值的变化：[13,-19,12] -> [7,-19,12] -> [8,-16,14]
Thread-0==第15次轮询选中[当前权重最大]的节点：{ip='192.168.1.101', weight=1, effectiveWeight=1, currentWeight=13, isAvailable=true}
……
Thread-1==[正常节点]：{ip='192.168.1.101', weight=1, effectiveWeight=1, currentWeight=2, isAvailable=true}
Thread-1==[正常节点]：{ip='192.168.1.102', weight=3, effectiveWeight=3, currentWeight=2, isAvailable=true}
Thread-1==[正常节点]：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=2, isAvailable=true}
Thread-1==加权轮询--[当前权重]值的变化：[2,2,2] -> [2,2,-4] -> [3,5,-2]
Thread-1==第18次轮询选中[当前权重最大]的节点：{ip='192.168.1.103', weight=2, effectiveWeight=2, currentWeight=2, isAvailable=true}
分析
一开始权重最高的节点虽然是宕机了，但是还是会被选中并返回；
[有效权重总和] 和 [当前权重总和]都减少了1，因为设置轮询到失败节点，都会自减1；
到第5次轮询时，当前权重已经变成了[7,-28,17]，可以看出宕机节点越往后当前权重越小，所以后面根本不会再选中宕机节点， 虽然没剔除故障节点，但却起到不分配宕机节点 ；
到第15次轮询时，有效权重已经恢复起始值，当前权重变为[8,-16,14]，当前权重只能慢慢恢复，并不是节点一正常就立即恢复宕机过的节点，起到对故障节点的缓冲恢复（故障过的节点可能还存在问题）；
最后1次轮询时，因为没有宕机节点，所以有效权重不变，当前权重已经恢复[3,5,-2]，如果再轮询一次，那就会访问到一开始故障的节点了。
4.结论
降权起到缓慢[剔除]宕机节点的效果；提权起到缓冲恢复宕机节点的效果。
对比上一篇文章可以看到：
当前权重（currentWeight）：针对的是节点的选择， 受有效权重影响 ，起到 缓慢[剔除] 宕机节点和 缓冲恢复 宕机节点的效果，当前权重最高就会被选择；
有效权重（effectiveWeight）：针对的是权重的变化，也即是 降权和提权 ，降权/提权只会直接操作有效权重；
权重（weight）：针对的是存储起始配置，限定有效权重的提权。
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原文链接：https://www.cnblogs.com/dennyLee2025/p/16138174.html
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