负载均衡算法

     负载均衡是分布式系统的常见功能，具有算法成熟，配置灵活，对系统性能有较大影响等特点。下面是目前比较流行的几种算法:

     a.Nginx负载均衡算法

     总体思路:

1. 计算权重总和和找出最大权重，然后最大权重的被选择
2. 被选择后最大权重减去权重总和，可以使得不会总选上最大权重，使得负载更加平滑。
3. 自身权重加上和原始权重等比例的权重来模拟随机性。

    

private static ServerForTest chooseServer(List<ServerForTest> servers) {

		double totalWeight = 0;
		double maxWeight = Double.NEGATIVE_INFINITY;

		ServerForTest chosen = null;

		for (ServerForTest server : servers) {

			server.setCurrentWeight(server.getWeight()
					+ server.getCurrentWeight());

			double currentWeight = server.getCurrentWeight();
			totalWeight += currentWeight;
			
			if (currentWeight > maxWeight) {
				maxWeight = currentWeight;
				chosen = server;
			}
		}

		chosen.setCurrentWeight(chosen.getCurrentWeight() - totalWeight);
		// System.out.println("chosen : " + chosen.getAddress());
		return chosen;

}

       b.一致性hash负载均衡算法

       算法门槛较高，要对hash算法有较深的理解。一致性hash一般不能严格按照权重比例, 因为和虚拟节点hash之后的位置和每一个请求的hash之后的位置都有关系。如果不是动态增减服务器数目，一般不建议使用一致性hash的负载均衡策略。

     

// Redis一致性hash相关算法
	// 构建hash环
	private static void insertVirtualNode2(TreeMap<Long, ServerForTest> map,
			int index, ServerForTest server) {

		StringBuilder keyBuilder = new StringBuilder(64);
		keyBuilder.append(server.getAddress() + "_" + index);

		int count = Double.valueOf(allNodeNum * server.getRatio()).intValue();

		// System.out.println("index = " + index + ",count = " + count);

		for (int j = 0; j < count; j++) {
			keyBuilder.append("_" + j);
			long hash = MurmurHash.INSTANCE.hash(keyBuilder.toString());
			map.put(hash, server);
		}

	}

	// Redis一致性hash相关算法
	// 一致性hash获取对应的key
	private static ServerForTest forServer2(
			TreeMap<Long, ServerForTest> nodeMap, Random random) {
        
                
                long hash = MurmurHash.INSTANCE.hash(toKey(random));
		SortedMap<Long, ServerForTest> tail = nodeMap.tailMap(hash);

		if (tail.isEmpty()) {
			return nodeMap.get(nodeMap.firstKey());
		}

		return tail.get(tail.firstKey());


        
        ｝

 

       c.轮询

      分为顺序轮询和权重轮询，顺序轮询点意义是从第一个按顺序遍历到最后一个，到最后一个然后再重新开始遍历，不断循环。顺序轮询算法如下:

    

private static Map<String, AtomicInteger> chooseServerForTest(int times) {

		List<ServerForTest> servers = new ArrayList<ServerForTest>(3);

		ServerForTest server1 = new ServerForTest("www.baidu.com", 1);
		servers.add(server1);
		ServerForTest server2 = new ServerForTest("www.taobao.com", 3);
		servers.add(server2);
		ServerForTest server3 = new ServerForTest("www.qq.com", 6);
		servers.add(server3);

		Map<String, AtomicInteger> result = initialResult(servers);

		int size = servers.size();
		for (int i = 0; i < times; i++) {
			ServerForTest server = servers.get(sequenceNum.getAndIncrement()
					% size);
			result.get(server.getAddress()).getAndIncrement();
		}

		return result;

}

        d.加权随机

        每次负载产生一个随机数，如果权重比随机数大，则被选择。

 

private static ServerForTest RandomLoadBalanceTest(
			List<ServerForTest> servers, Random random) {

		boolean sameWeight = true;
		int size = servers.size();
		double totalWeight = 0;

		for (int i = 0; i < size; i++) {
			totalWeight += servers.get(i).getWeight();
			if (i != 0
					&& Double.compare(servers.get(i).getWeight(),
							servers.get(i - 1).getWeight()) != 0) 
                        {
				sameWeight = false;
			}

		}

		ServerForTest serverGet = null;

		if (!sameWeight && totalWeight > 0) {
			double offset = random.nextDouble() * totalWeight;
			for (ServerForTest server : servers) {
				double currentWeight = server.getWeight();
				if (currentWeight > offset) {
					serverGet = server;
					break;
				} else {
					offset -= currentWeight;
				}
			}
		}

		if (serverGet == null) {
			serverGet = servers.get(random.nextInt(size));
		}

		return serverGet;

	}

    

         参考资料链接:

 

        Nginx负载均衡算法

        http://blog.sina.com.cn/s/blog_9c3ba23d01010rof.html

        dubbo负载均衡算法

        https://blog.youkuaiyun.com/qq_26562641/article/details/50392142