擼一擼Spring Cloud Ribbon的原理-負載均衡策略

在前兩篇《擼一擼Spring Cloud Ribbon的原理》，《擼一擼Spring Cloud Ribbon的原理-負載均衡器》中，整理了Ribbon如何經過負載均衡攔截器植入RestTemplate，以及調用負載均衡器獲取服務列表，如何過濾，如何更新等的處理過程。

由於，負載均衡器最終是調用負載均衡策略的choose方法來選擇一個服務，因此這一篇，整理Ribbon的負載均衡策略。

 

策略類

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

 

他們的位置在：

ribbon-loadbalancer-2.2.2.jar

com.netflix.loadbalancer

 

類繼承關係

RandomRule

隨機選取負載均衡策略。

choose方法中，經過隨機Random對象，在全部服務實例數量中隨機找一個服務的索引號，而後從上線的服務中獲取對應的服務。

這時候，極可能會有不在線的服務，就有可能從上線的服務中獲取不到，那麼休息會兒再獲取知道隨機獲取到一個上線的服務爲止。

public class RandomRule extends AbstractLoadBalancerRule {
    Random rand;

    public RandomRule() {
        rand = new Random();
    }

    @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 = rand.nextInt(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;

    }

    //略
}

 

RoundRobinRule

線性輪詢負載均衡策略。

choose方法中，經過incrementAndGetModulo方法以線性輪詢方式獲取服務。

在incrementAndGetModulo中，實際上在類中維護了一個原子性的nextServerCyclicCounter成員變量做爲當前服務的索引號，每次在全部服務數量的限制下，就是將服務的索引號加1，到達服務數量限制時再從頭開始。

public class RoundRobinRule extends AbstractLoadBalancerRule {

    private AtomicInteger nextServerCyclicCounter;

    // 略

    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;
    }

    private int incrementAndGetModulo(int modulo) {
        for (;;) {
            int current = nextServerCyclicCounter.get();
            int next = (current + 1) % modulo;
            if (nextServerCyclicCounter.compareAndSet(current, next))
                return next;
        }
    }
    
    // 略
}

 

WeightedResponseTimeRule

響應時間做爲選取權重的負載均衡策略。

其含義就是，響應時間越短的服務被選中的可能性大。

繼承自RoundRobinRule類。

public class WeightedResponseTimeRule extends RoundRobinRule {

    // 略
    
    // 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>();

    // 略

    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();
            }
        }));
    }

    // 略

    @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) {
                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;
    }
    
    // 略

}

既然是按照響應時間權重來選擇服務，那麼先整理一下權重算法是怎麼作的。

觀察initialize方法，啓動了定時器定時執行DynamicServerWeightTask的run來調用計算服務權重，計算權重是經過內部類ServerWeight的maintainWeights方法來進行。

整理一下maintainWeights方法的邏輯，裏面有兩個for循環，第一個for循環拿到全部服務的總響應時間，第二個for循環計算每一個服務的權重以及總權重。

第一個for循環。

假設有4個服務，每一個服務的響應時間（ms）：

A: 200

B: 500

C: 30

D: 1200

總響應時間：

200+500+30+1200=1930ms

接下來第二個for循環，計算每一個服務的權重。

服務的權重=總響應時間-服務自身的響應時間：

A: 1930-200=1730

B: 1930-500=1430

C: 1930-30=1900

D: 1930-1200=730

總權重：

1730+1430+1900+730=5790

響應時間及權重計算結果示意圖：

 

結果就是響應時間越短的服務，它的權重就越大。

 

再看一下choose方法。

重點在while循環的第3個if這裏。

首先若是斷定沒有服務或者權重還沒計算出來時，會採用父類RoundRobinRule以線性輪詢的方式選擇服務器。

有服務，有權重計算結果後，就是以總權重值爲限制，拿到一個隨機數，而後看隨機數落到哪一個區間，就選擇對應的服務。

因此選取服務的結論就是：

響應時間越短的服務，它的權重就越大，被選中的可能性就越大。

 

RetryRule

這個策略默認就是用RoundRobinRule策略選取服務，固然能夠經過配置，在構造RetryRule的時候傳進想要的策略。

爲了應對在有可能出現沒法選取出服務的狀況，好比瞬時斷線的狀況，那麼就要提供一種重試機制，在最大重試時間的限定下重複嘗試選取服務，直到選取出一個服務或者超時。

最大重試時間maxRetryMillis是可配置的。

public class RetryRule extends AbstractLoadBalancerRule {
    IRule subRule = new RoundRobinRule();
    long maxRetryMillis = 500;

    public RetryRule() {
    }

    public RetryRule(IRule subRule) {
        this.subRule = (subRule != null) ? subRule : new RoundRobinRule();
    }

    public RetryRule(IRule subRule, long maxRetryMillis) {
        this.subRule = (subRule != null) ? subRule : new RoundRobinRule();
        this.maxRetryMillis = (maxRetryMillis > 0) ? maxRetryMillis : 500;
    }

    // 略

    public void setMaxRetryMillis(long maxRetryMillis) {
        if (maxRetryMillis > 0) {
            this.maxRetryMillis = maxRetryMillis;
        } else {
            this.maxRetryMillis = 500;
        }
    }

    // 略

    /*
     * Loop if necessary. Note that the time CAN be exceeded depending on the
     * subRule, because we're not spawning additional threads and returning
     * early.
     */
    public Server choose(ILoadBalancer lb, Object key) {
        long requestTime = System.currentTimeMillis();
        long deadline = requestTime + maxRetryMillis;

        Server answer = null;

        answer = subRule.choose(key);

        if (((answer == null) || (!answer.isAlive()))
                && (System.currentTimeMillis() < deadline)) {

            InterruptTask task = new InterruptTask(deadline
                    - System.currentTimeMillis());

            while (!Thread.interrupted()) {
                answer = subRule.choose(key);

                if (((answer == null) || (!answer.isAlive()))
                        && (System.currentTimeMillis() < deadline)) {
                    /* pause and retry hoping it's transient */
                    Thread.yield();
                } else {
                    break;
                }
            }

            task.cancel();
        }

        if ((answer == null) || (!answer.isAlive())) {
            return null;
        } else {
            return answer;
        }
    }
        
    // 略
    
}

 

ClientConfigEnabledRoundRobinRule

該策略自己很簡單，就是默認經過RoundRobinRule策略選取服務，策略還不能自定義。

這個策略的的目的就是經過繼承該類，而且經過對choose的重寫，來實現更多的功能，繼承後保底是有RoundRobinRule策略。

public class ClientConfigEnabledRoundRobinRule extends AbstractLoadBalancerRule {

    RoundRobinRule roundRobinRule = new RoundRobinRule();

    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        roundRobinRule = new RoundRobinRule();
    }

    @Override
    public void setLoadBalancer(ILoadBalancer lb) {
        super.setLoadBalancer(lb);
        roundRobinRule.setLoadBalancer(lb);
    }
    
    @Override
    public Server choose(Object key) {
        if (roundRobinRule != null) {
            return roundRobinRule.choose(key);
        } else {
            throw new IllegalArgumentException(
                    "This class has not been initialized with the RoundRobinRule class");
        }
    }

}

 

BestAvailableRule

繼承自上面的ClientConfigEnabledRoundRobinRule，重寫choose方法。

從全部的服務中，從那些沒有斷開的服務中，找到，到目前爲止，請求數量最小的服務。

public class BestAvailableRule extends ClientConfigEnabledRoundRobinRule {

    private LoadBalancerStats loadBalancerStats;
    
    @Override
    public Server choose(Object key) {
        if (loadBalancerStats == null) {
            return super.choose(key);
        }
        List<Server> serverList = getLoadBalancer().getAllServers();
        int minimalConcurrentConnections = Integer.MAX_VALUE;
        long currentTime = System.currentTimeMillis();
        Server chosen = null;
        for (Server server: serverList) {
            ServerStats serverStats = loadBalancerStats.getSingleServerStat(server);
            if (!serverStats.isCircuitBreakerTripped(currentTime)) {
                int concurrentConnections = serverStats.getActiveRequestsCount(currentTime);
                if (concurrentConnections < minimalConcurrentConnections) {
                    minimalConcurrentConnections = concurrentConnections;
                    chosen = server;
                }
            }
        }
        if (chosen == null) {
            return super.choose(key);
        } else {
            return chosen;
        }
    }

    @Override
    public void setLoadBalancer(ILoadBalancer lb) {
        super.setLoadBalancer(lb);
        if (lb instanceof AbstractLoadBalancer) {
            loadBalancerStats = ((AbstractLoadBalancer) lb).getLoadBalancerStats();            
        }
    }
}

 

PredicateBasedRule

這是一個抽象類，提供一個choose的模板，經過調用AbstractServerPredicate實現類的過濾方法來過濾出目標的服務，再經過輪詢方法選出一個服務。

PredicateBasedRule的子類ZoneAvoidanceRule使用了該模板選擇服務。

public abstract class PredicateBasedRule extends ClientConfigEnabledRoundRobinRule {
   
    public abstract AbstractServerPredicate getPredicate();
        
    @Override
    public Server choose(Object key) {
        ILoadBalancer lb = getLoadBalancer();
        Optional<Server> server = getPredicate().chooseRoundRobinAfterFiltering(lb.getAllServers(), key);
        if (server.isPresent()) {
            return server.get();
        } else {
            return null;
        }       
    }
}

 

AvailabilityFilteringRule

按可用性進行過濾服務的負載均衡策略。

繼承自PredicateBasedRule，可是重寫了choose方法。

public class AvailabilityFilteringRule extends PredicateBasedRule {    

    private AbstractServerPredicate predicate;
    
    // 略

    @Override
    public Server choose(Object key) {
        int count = 0;
        Server server = roundRobinRule.choose(key);
        while (count++ <= 10) {
            if (predicate.apply(new PredicateKey(server))) {
                return server;
            }
            server = roundRobinRule.choose(key);
        }
        return super.choose(key);
    }

    // 略
}

先用線性輪詢策略選出一個服務，經過predicate對象判斷是否符合可用性要求，符合的就做爲目標服務。這裏的作法就是先選擇後過濾。

若是嘗試了十次都沒有可用的服務，調用父類PredicateBasedRule的choose。

在構造函數中會看到AvailabilityFilteringRule用的是AvailabilityPredicate對象對服務進行斷定。

public class AvailabilityFilteringRule extends PredicateBasedRule {    

    private AbstractServerPredicate predicate;
    
    public AvailabilityFilteringRule() {
        super();
        predicate = CompositePredicate.withPredicate(new AvailabilityPredicate(this, null))
                .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                .build();
    }
    
    
    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        predicate = CompositePredicate.withPredicate(new AvailabilityPredicate(this, clientConfig))
                    .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                    .build();
    }

    // 略
}

 在AvailabilityPredicate中，經過shouldSkipServer函數斷定服務狀態。

public class AvailabilityPredicate extends  AbstractServerPredicate {
        
    // 略
    
    @Override
    public boolean apply(@Nullable PredicateKey input) {
        LoadBalancerStats stats = getLBStats();
        if (stats == null) {
            return true;
        }
        return !shouldSkipServer(stats.getSingleServerStat(input.getServer()));
    }
    
    
    private boolean shouldSkipServer(ServerStats stats) {        
        if ((CIRCUIT_BREAKER_FILTERING.get() && stats.isCircuitBreakerTripped()) || stats.getActiveRequestsCount() >= activeConnectionsLimit.get()) {
            return true;
        }
        return false;
    }

}

斷定規則：

如下兩項有一項成立，就表示該服務不可用，不能使用該服務。

1.

配置項niws.loadbalancer.availabilityFilteringRule.filterCircuitTripped爲true（未配置則默認爲true），而且已經觸發斷路。

2.

服務的活動請求數 > 配置項niws.loadbalancer.availabilityFilteringRule.activeConnectionsLimit（未配則默認爲Interge.MAX_VALUE）。

 

在AvailabilityFilteringRule的choose中沒法選出服務的狀況下，會調用父類PredicateBasedRule的choose，PredicateBasedRule採用先過濾後線性輪行方法選擇服務，不過，用來斷定的predicate仍是AvailabilityPredicate，因此過濾用的斷定規則和上面是同樣的。

 

ZoneAvoidanceRule

能夠發如今ZoneAvoidanceRule自己並無重寫choose方法，用的仍是抽象父類PredicateBasedRule的choose。

上面記錄過，PredicateBasedRule的choose自己使用的是predicate進行過濾。

看一下ZoneAvoidanceRule定義的predicate。

public class ZoneAvoidanceRule extends PredicateBasedRule {

    // 略
    
    private CompositePredicate compositePredicate;
    
    public ZoneAvoidanceRule() {
        super();
        ZoneAvoidancePredicate zonePredicate = new ZoneAvoidancePredicate(this);
        AvailabilityPredicate availabilityPredicate = new AvailabilityPredicate(this);
        compositePredicate = createCompositePredicate(zonePredicate, availabilityPredicate);
    }
    
    private CompositePredicate createCompositePredicate(ZoneAvoidancePredicate p1, AvailabilityPredicate p2) {
        return CompositePredicate.withPredicates(p1, p2)
                             .addFallbackPredicate(p2)
                             .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                             .build();
        
    }
    
    
    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        ZoneAvoidancePredicate zonePredicate = new ZoneAvoidancePredicate(this, clientConfig);
        AvailabilityPredicate availabilityPredicate = new AvailabilityPredicate(this, clientConfig);
        compositePredicate = createCompositePredicate(zonePredicate, availabilityPredicate);
    }

    // 略

    @Override
    public AbstractServerPredicate getPredicate() {
        return compositePredicate;
    }    
}

在這裏的predicate主要用了兩個，ZoneAvoidancePredicate，AvailabilityPredicate，都被複合進了CompositePredicate。

PredicateBasedRule的choose會去調用CompositePredicate的getEligibleServers來獲取符合要求的服務。

而在getEligibleServers內部會對全部服務逐一調用複合進來的predicate（ZoneAvoidancePredicate，AvailabilityPredicate）的getEligibleServers。

public class CompositePredicate extends AbstractServerPredicate {

    private AbstractServerPredicate delegate;
    
    private List<AbstractServerPredicate> fallbacks = Lists.newArrayList();
        
    private int minimalFilteredServers = 1;
    
    private float minimalFilteredPercentage = 0;    
    
    @Override
    public boolean apply(@Nullable PredicateKey input) {
        return delegate.apply(input);
    }

    // 略

    /**
     * Get the filtered servers from primary predicate, and if the number of the filtered servers
     * are not enough, trying the fallback predicates  
     */
    @Override
    public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
        List<Server> result = super.getEligibleServers(servers, loadBalancerKey);
        Iterator<AbstractServerPredicate> i = fallbacks.iterator();
        while (!(result.size() >= minimalFilteredServers && result.size() > (int) (servers.size() * minimalFilteredPercentage))
                && i.hasNext()) {
            AbstractServerPredicate predicate = i.next();
            result = predicate.getEligibleServers(servers, loadBalancerKey);
        }
        return result;
    }
}

 調用的複合進來的predicate的getEligibleServers方法是定義在抽象類AbstractServerPredicate中。

能夠看到，最終仍是調用各predicate的apply方法進行過濾。

public abstract class AbstractServerPredicate implements Predicate<PredicateKey> {
    
    protected IRule rule;
    private volatile LoadBalancerStats lbStats;
    
    private final Random random = new Random();
    
    private final AtomicInteger nextIndex = new AtomicInteger();

    // 略

    /**
     * Get servers filtered by this predicate from list of servers. Load balancer key
     * is presumed to be null. 
     * 
     * @see #getEligibleServers(List, Object)
     * 
     */
    public List<Server> getEligibleServers(List<Server> servers) {
        return getEligibleServers(servers, null);
    }
 
    /**
     * Get servers filtered by this predicate from list of servers. 
     */
    public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
        if (loadBalancerKey == null) {
            return ImmutableList.copyOf(Iterables.filter(servers, this.getServerOnlyPredicate()));            
        } else {
            List<Server> results = Lists.newArrayList();
            for (Server server: servers) {
                if (this.apply(new PredicateKey(loadBalancerKey, server))) {
                    results.add(server);
                }
            }
            return results;            
        }
    }

    // 略
}

AvailabilityPredicate過濾時的斷定規則在上面的AvailabilityFilteringRule中已經寫過。

如今主要看一下ZoneAvoidancePredicate的Apply方法。

public class ZoneAvoidancePredicate extends  AbstractServerPredicate {

    // 略

    @Override
    public boolean apply(@Nullable PredicateKey input) {
        if (!ENABLED.get()) {
            return true;
        }
        String serverZone = input.getServer().getZone();
        if (serverZone == null) {
            // there is no zone information from the server, we do not want to filter
            // out this server
            return true;
        }
        LoadBalancerStats lbStats = getLBStats();
        if (lbStats == null) {
            // no stats available, do not filter
            return true;
        }
        if (lbStats.getAvailableZones().size() <= 1) {
            // only one zone is available, do not filter
            return true;
        }
        Map<String, ZoneSnapshot> zoneSnapshot = ZoneAvoidanceRule.createSnapshot(lbStats);
        if (!zoneSnapshot.keySet().contains(serverZone)) {
            // The server zone is unknown to the load balancer, do not filter it out 
            return true;
        }
        logger.debug("Zone snapshots: {}", zoneSnapshot);
        Set<String> availableZones = ZoneAvoidanceRule.getAvailableZones(zoneSnapshot, triggeringLoad.get(), triggeringBlackoutPercentage.get());
        logger.debug("Available zones: {}", availableZones);
        if (availableZones != null) {
            return availableZones.contains(input.getServer().getZone());
        } else {
            return false;
        }
    }    
}

比較清楚的是，這裏獲取了可用區域，看一下每一個服務是否都在可用區中，在的就做爲符合ZoneAvoidancePredicate要求的服務。

得到可用區域能夠看一下ZoneAvoidanceRule的getAvailableZones方法。

 

End