Java实现平滑加权轮询算法--降权和提权
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
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
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 (WeightedRoundRobinAvailable.class){
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。
