Spring Cloud Ribbon的原理-负载均衡策略

 在前两篇《Spring Cloud Ribbon的原理》，《Spring Cloud Ribbon的原理-负载均衡器》中，整理了Ribbon如何通过负载均衡拦截器植入RestTemplate，以及调用负载均衡器获取服务列表，如何过滤，如何更新等的处理过程。

因为，负载均衡器最终是调用负载均衡策略的choose方法来选择一个服务，所以这一篇，整理Ribbon的负载均衡策略。

策略类

• RandomRule
• RoundRobinRule
• RetryRule
• WeightedResponseTimeRule
• ClientConfigEnabledRoundRobinRule
• BestAvailableRule
• PredicateBasedRule
• AvailabilityFilteringRule
• ZoneAvoidanceRule

类继承关系

RandomRule
随机选取负载均衡策略。
choose方法中，通过随机Random对象，在所有服务实例数量中随机找一个服务的索引号，然后从上线的服务中获取对应的服务。
这时候，很可能会有不在线的服务，就有可能从上线的服务中获取不到，那么休息会儿再获取知道随机获取到一个上线的服务为止。

public class RandomRule extends AbstractLoadBalancerRule {

    /**
     * Randomly choose from all living servers
     */
    @edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE")
    public Server choose(ILoadBalancer lb, Object key) {
        if (lb == null) {
            return null;
        }
        Server server = null;

        while (server == null) {
            if (Thread.interrupted()) {
                return null;
            }
            List<Server> upList = lb.getReachableServers();
            List<Server> allList = lb.getAllServers();

            int serverCount = allList.size();
            if (serverCount == 0) {
                /*
                 * No servers. End regardless of pass, because subsequent passes
                 * only get more restrictive.
                 */
                return null;
            }

            int index = chooseRandomInt(serverCount);
            server = upList.get(index);

            if (server == null) {
                /*
                 * The only time this should happen is if the server list were
                 * somehow trimmed. This is a transient condition. Retry after
                 * yielding.
                 */
                Thread.yield();
                continue;
            }

            if (server.isAlive()) {
                return (server);
            }

            // Shouldn't actually happen.. but must be transient or a bug.
            server = null;
            Thread.yield();
        }

        return server;

    }

    protected int chooseRandomInt(int serverCount) {
        return ThreadLocalRandom.current().nextInt(serverCount);
    }

    @Override
    public Server choose(Object key) {
        return choose(getLoadBalancer(), key);
    }

    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        // TODO Auto-generated method stub
        
    }

RoundRobinRule
线性轮询负载均衡策略。
choose方法中，通过incrementAndGetModulo方法以线性轮询方式获取服务。
在incrementAndGetModulo中，实际上在类中维护了一个原子性的nextServerCyclicCounter成员变量作为当前服务的索引号，每次在所有服务数量的限制下，就是将服务的索引号加1，到达服务数量限制时再从头开始。

public class RoundRobinRule extends AbstractLoadBalancerRule {

    private AtomicInteger nextServerCyclicCounter;
    private static final boolean AVAILABLE_ONLY_SERVERS = true;
    private static final boolean ALL_SERVERS = false;

    private static Logger log = LoggerFactory.getLogger(RoundRobinRule.class);

    public RoundRobinRule() {
        nextServerCyclicCounter = new AtomicInteger(0);
    }

    public RoundRobinRule(ILoadBalancer lb) {
        this();
        setLoadBalancer(lb);
    }

    public Server choose(ILoadBalancer lb, Object key) {
        if (lb == null) {
            log.warn("no load balancer");
            return null;
        }

        Server server = null;
        int count = 0;
        while (server == null && count++ < 10) {
            List<Server> reachableServers = lb.getReachableServers();
            List<Server> allServers = lb.getAllServers();
            int upCount = reachableServers.size();
            int serverCount = allServers.size();

            if ((upCount == 0) || (serverCount == 0)) {
                log.warn("No up servers available from load balancer: " + lb);
                return null;
            }

            int nextServerIndex = incrementAndGetModulo(serverCount);
            server = allServers.get(nextServerIndex);

            if (server == null) {
                /* Transient. */
                Thread.yield();
                continue;
            }

            if (server.isAlive() && (server.isReadyToServe())) {
                return (server);
            }

            // Next.
            server = null;
        }

        if (count >= 10) {
            log.warn("No available alive servers after 10 tries from load balancer: "
                    + lb);
        }
        return server;
    }

    /**
     * Inspired by the implementation of {@link AtomicInteger#incrementAndGet()}.
     *
     * @param modulo The modulo to bound the value of the counter.
     * @return The next value.
     */
    private int incrementAndGetModulo(int modulo) {
        for (;;) {
            int current = nextServerCyclicCounter.get();
            int next = (current + 1) % modulo;
            if (nextServerCyclicCounter.compareAndSet(current, next))
                return next;
        }
    }

    @Override
    public Server choose(Object key) {
        return choose(getLoadBalancer(), key);
    }

    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
    }
}

WeightedResponseTimeRule
响应时间作为选取权重的负载均衡策略。其含义就是，响应时间越短的服务被选中的可能性大。继承自RoundRobinRule类。

public class WeightedResponseTimeRule extends RoundRobinRule {

    public static final IClientConfigKey<Integer> WEIGHT_TASK_TIMER_INTERVAL_CONFIG_KEY = new IClientConfigKey<Integer>() {
        @Override
        public String key() {
            return "ServerWeightTaskTimerInterval";
        }
        
        @Override
        public String toString() {
            return key();
        }

        @Override
        public Class<Integer> type() {
            return Integer.class;
        }
    };
    
    public static final int DEFAULT_TIMER_INTERVAL = 30 * 1000;
    
    private int serverWeightTaskTimerInterval = DEFAULT_TIMER_INTERVAL;

    private static final Logger logger = LoggerFactory.getLogger(WeightedResponseTimeRule.class);
    
    // holds the accumulated weight from index 0 to current index
    // for example, element at index 2 holds the sum of weight of servers from 0 to 2
    private volatile List<Double> accumulatedWeights = new ArrayList<Double>();
    

    private final Random random = new Random();

    protected Timer serverWeightTimer = null;

    protected AtomicBoolean serverWeightAssignmentInProgress = new AtomicBoolean(false);

    String name = "unknown";

    public WeightedResponseTimeRule() {
        super();
    }

    public WeightedResponseTimeRule(ILoadBalancer lb) {
        super(lb);
    }
    
    @Override
    public void setLoadBalancer(ILoadBalancer lb) {
        super.setLoadBalancer(lb);
        if (lb instanceof BaseLoadBalancer) {
            name = ((BaseLoadBalancer) lb).getName();
        }
        initialize(lb);
    }

    void initialize(ILoadBalancer lb) {        
        if (serverWeightTimer != null) {
            serverWeightTimer.cancel();
        }
        serverWeightTimer = new Timer("NFLoadBalancer-serverWeightTimer-"
                + name, true);
        serverWeightTimer.schedule(new DynamicServerWeightTask(), 0,
                serverWeightTaskTimerInterval);
        // do a initial run
        ServerWeight sw = new ServerWeight();
        sw.maintainWeights();

        Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {
            public void run() {
                logger
                        .info("Stopping NFLoadBalancer-serverWeightTimer-"
                                + name);
                serverWeightTimer.cancel();
            }
        }));
    }

    public void shutdown() {
        if (serverWeightTimer != null) {
            logger.info("Stopping NFLoadBalancer-serverWeightTimer-" + name);
            serverWeightTimer.cancel();
        }
    }

    List<Double> getAccumulatedWeights() {
        return Collections.unmodifiableList(accumulatedWeights);
    }

    @edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE")
    @Override
    public Server choose(ILoadBalancer lb, Object key) {
        if (lb == null) {
            return null;
        }
        Server server = null;

        while (server == null) {
            // get hold of the current reference in case it is changed from the other thread
            List<Double> currentWeights = accumulatedWeights;
            if (Thread.interrupted()) {
                return null;
            }
            List<Server> allList = lb.getAllServers();

            int serverCount = allList.size();

            if (serverCount == 0) {
                return null;
            }

            int serverIndex = 0;

            // last one in the list is the sum of all weights
            double maxTotalWeight = currentWeights.size() == 0 ? 0 : currentWeights.get(currentWeights.size() - 1); 
            // No server has been hit yet and total weight is not initialized
            // fallback to use round robin
            if (maxTotalWeight < 0.001d || serverCount != currentWeights.size()) {
                server =  super.choose(getLoadBalancer(), key);
                if(server == null) {
                    return server;
                }
            } else {
                // generate a random weight between 0 (inclusive) to maxTotalWeight (exclusive)
                double randomWeight = random.nextDouble() * maxTotalWeight;
                // pick the server index based on the randomIndex
                int n = 0;
                for (Double d : currentWeights) {
                    if (d >= randomWeight) {
                        serverIndex = n;
                        break;
                    } else {
                        n++;
                    }
                }

                server = allList.get(serverIndex);
            }

            if (server == null) {
                /* Transient. */
                Thread.yield();
                continue;
            }

            if (server.isAlive()) {
                return (server);
            }

            // Next.
            server = null;
        }
        return server;
    }

    class DynamicServerWeightTask extends TimerTask {
        public void run() {
            ServerWeight serverWeight = new ServerWeight();
            try {
                serverWeight.maintainWeights();
            } catch (Exception e) {
                logger.error("Error running DynamicServerWeightTask for {}", name, e);
            }
        }
    }

    class ServerWeight {

        public void maintainWeights() {
            ILoadBalancer lb = getLoadBalancer();
            if (lb == null) {
                return;
            }
            
            if (!serverWeightAssignmentInProgress.compareAndSet(false,  true))  {
                return; 
            }
            
            try {
                logger.info("Weight adjusting job started");
                AbstractLoadBalancer nlb = (AbstractLoadBalancer) lb;
                LoadBalancerStats stats = nlb.getLoadBalancerStats();
                if (stats == null) {
                    // no statistics, nothing to do
                    return;
                }
                double totalResponseTime = 0;
                // find maximal 95% response time
                for (Server server : nlb.getAllServers()) {
                    // this will automatically load the stats if not in cache
                    ServerStats ss = stats.getSingleServerStat(server);
                    totalResponseTime += ss.getResponseTimeAvg();
                }
                // weight for each server is (sum of responseTime of all servers - responseTime)
                // so that the longer the response time, the less the weight and the less likely to be chosen
                Double weightSoFar = 0.0;
                
                // create new list and hot swap the reference
                List<Double> finalWeights = new ArrayList<Double>();
                for (Server server : nlb.getAllServers()) {
                    ServerStats ss = stats.getSingleServerStat(server);
                    double weight = totalResponseTime - ss.getResponseTimeAvg();
                    weightSoFar += weight;
                    finalWeights.add(weightSoFar);   
                }
                setWeights(finalWeights);
            } catch (Exception e) {
                logger.error("Error calculating server weights", e);
            } finally {
                serverWeightAssignmentInProgress.set(false);
            }

        }
    }

    void setWeights(List<Double> weights) {
        this.accumulatedWeights = weights;
    }

    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        super.initWithNiwsConfig(clientConfig);
        serverWeightTaskTimerInterval = clientConfig.get(WEIGHT_TASK_TIMER_INTERVAL_CONFIG_KEY, DEFAULT_TIMER_INTERVAL);
    }

}

既然是按照响应时间权重来选择服务，那么先整理一下权重算法是怎么做的。
观察initialize方法，启动了定时器定时执行DynamicServerWeightTask的run来调用计算服务权重，计算权重是通过内部类ServerWeight的maintainWeights方法来进行。
整理一下maintainWeights方法的逻辑，里面有两个for循环，第一个for循环拿到所有服务的总响应时间，第二个for循环计算每个服务的权重以及总权重。
第一个for循环。
假设有4个服务，每个服务的响应时间（ms）：

A: 200
B: 500
C: 30
D: 1200

总响应时间：
200+500+30+1200=1930ms
接下来第二个for循环，计算每个服务的权重。
服务的权重=总响应时间-服务自身的响应时间：
A: 1930-200=1730
B: 1930-500=1430
C: 1930-30=1900
D: 1930-1200=730

总权重：
1730+1430+1900+730=5790
结果就是响应时间越短的服务，它的权重就越大。

再看一下choose方法。重点在while循环的第3个if这里。首先如果判定没有服务或者权重还没计算出来时，会采用父类RoundRobinRule以线性轮询的方式选择服务器。
有服务，有权重计算结果后，就是以总权重值为限制，拿到一个随机数，然后看随机数落到哪个区间，就选择对应的服务。
所以选取服务的结论就是：响应时间越短的服务，它的权重就越大，被选中的可能性就越大。

 还有其他的负载均衡选择策略，下面就不一一列举了。