mirror of https://github.com/grpc/grpc-java.git
implemented and tested static stride scheduler for weighted round robin load balancing policy (#10272)
This commit is contained in:
Tony An
GitHub
parent
361616ae7c
commit
0b53dd7304
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@ -44,10 +44,10 @@ import java.util.HashMap;
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import java.util.HashSet;
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import java.util.List;
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import java.util.Map;
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import java.util.PriorityQueue;
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import java.util.Random;
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import java.util.concurrent.ScheduledExecutorService;
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import java.util.concurrent.TimeUnit;
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import java.util.concurrent.atomic.AtomicInteger;
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import java.util.logging.Level;
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import java.util.logging.Logger;
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@ -120,7 +120,7 @@ final class WeightedRoundRobinLoadBalancer extends RoundRobinLoadBalancer {
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@Override
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public void run() {
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if (currentPicker != null && currentPicker instanceof WeightedRoundRobinPicker) {
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((WeightedRoundRobinPicker)currentPicker).updateWeight();
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((WeightedRoundRobinPicker) currentPicker).updateWeight();
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}
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weightUpdateTimer = syncContext.schedule(this, config.weightUpdatePeriodNanos,
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TimeUnit.NANOSECONDS, timeService);
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@ -258,7 +258,7 @@ final class WeightedRoundRobinLoadBalancer extends RoundRobinLoadBalancer {
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new HashMap<>();
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private final boolean enableOobLoadReport;
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private final float errorUtilizationPenalty;
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private volatile EdfScheduler scheduler;
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private volatile StaticStrideScheduler scheduler;
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WeightedRoundRobinPicker(List<Subchannel> list, boolean enableOobLoadReport,
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float errorUtilizationPenalty) {
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@ -279,7 +279,7 @@ final class WeightedRoundRobinLoadBalancer extends RoundRobinLoadBalancer {
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Subchannel subchannel = list.get(scheduler.pick());
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if (!enableOobLoadReport) {
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return PickResult.withSubchannel(subchannel,
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OrcaPerRequestUtil.getInstance().newOrcaClientStreamTracerFactory(
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OrcaPerRequestUtil.getInstance().newOrcaClientStreamTracerFactory(
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subchannelToReportListenerMap.getOrDefault(subchannel,
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((WrrSubchannel) subchannel).new OrcaReportListener(errorUtilizationPenalty))));
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} else {
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@ -288,26 +288,14 @@ final class WeightedRoundRobinLoadBalancer extends RoundRobinLoadBalancer {
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}
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private void updateWeight() {
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int weightedChannelCount = 0;
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double avgWeight = 0;
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for (Subchannel value : list) {
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double newWeight = ((WrrSubchannel) value).getWeight();
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if (newWeight > 0) {
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avgWeight += newWeight;
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weightedChannelCount++;
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}
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}
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EdfScheduler scheduler = new EdfScheduler(list.size(), random);
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if (weightedChannelCount >= 1) {
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avgWeight /= 1.0 * weightedChannelCount;
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} else {
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avgWeight = 1;
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}
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float[] newWeights = new float[list.size()];
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for (int i = 0; i < list.size(); i++) {
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WrrSubchannel subchannel = (WrrSubchannel) list.get(i);
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double newWeight = subchannel.getWeight();
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scheduler.add(i, newWeight > 0 ? newWeight : avgWeight);
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newWeights[i] = newWeight > 0 ? (float) newWeight : 0.0f;
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}
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StaticStrideScheduler scheduler = new StaticStrideScheduler(newWeights, random);
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this.scheduler = scheduler;
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}
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@ -340,111 +328,125 @@ final class WeightedRoundRobinLoadBalancer extends RoundRobinLoadBalancer {
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}
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}
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/**
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* The earliest deadline first implementation in which each object is
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* chosen deterministically and periodically with frequency proportional to its weight.
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*
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* <p>Specifically, each object added to chooser is given a deadline equal to the multiplicative
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* inverse of its weight. The place of each object in its deadline is tracked, and each call to
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* choose returns the object with the least remaining time in its deadline.
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* (Ties are broken by the order in which the children were added to the chooser.) The deadline
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* advances by the multiplicative inverse of the object's weight.
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* For example, if items A and B are added with weights 0.5 and 0.2, successive chooses return:
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/*
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* The Static Stride Scheduler is an implementation of an earliest deadline first (EDF) scheduler
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* in which each object's deadline is the multiplicative inverse of the object's weight.
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* <p>
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* The way in which this is implemented is through a static stride scheduler.
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* The Static Stride Scheduler works by iterating through the list of subchannel weights
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* and using modular arithmetic to proportionally distribute picks, favoring entries
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* with higher weights. It is based on the observation that the intended sequence generated
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* from an EDF scheduler is a periodic one that can be achieved through modular arithmetic.
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* The Static Stride Scheduler is more performant than other implementations of the EDF
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* Scheduler, as it removes the need for a priority queue (and thus mutex locks).
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* <p>
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* go/static-stride-scheduler
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* <p>
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*
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* <ul>
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* <li>In the first call, the deadlines are A=2 (1/0.5) and B=5 (1/0.2), so A is returned.
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* The deadline of A is updated to 4.
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* <li>Next, the remaining deadlines are A=4 and B=5, so A is returned. The deadline of A (2) is
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* updated to A=6.
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* <li>Remaining deadlines are A=6 and B=5, so B is returned. The deadline of B is updated with
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* with B=10.
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* <li>Remaining deadlines are A=6 and B=10, so A is returned. The deadline of A is updated with
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* A=8.
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* <li>Remaining deadlines are A=8 and B=10, so A is returned. The deadline of A is updated with
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* A=10.
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* <li>Remaining deadlines are A=10 and B=10, so A is returned. The deadline of A is updated
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* with A=12.
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* <li>Remaining deadlines are A=12 and B=10, so B is returned. The deadline of B is updated
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* with B=15.
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* <li>etc.
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* </ul>
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*
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* <p>In short: the entry with the highest weight is preferred.
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*
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* <ul>
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* <li>add() - O(lg n)
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* <li>pick() - O(lg n)
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* </ul>
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*
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* <li>nextSequence() - O(1)
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* <li>pick() - O(n)
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*/
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@VisibleForTesting
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static final class EdfScheduler {
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private final PriorityQueue<ObjectState> prioQueue;
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static final class StaticStrideScheduler {
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private final short[] scaledWeights;
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private final int sizeDivisor;
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private final AtomicInteger sequence;
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private static final int K_MAX_WEIGHT = 0xFFFF;
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/**
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* Weights below this value will be upped to this minimum weight.
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*/
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private static final double MINIMUM_WEIGHT = 0.0001;
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private final Object lock = new Object();
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private final Random random;
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/**
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* Use the item's deadline as the order in the priority queue. If the deadlines are the same,
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* use the index. Index should be unique.
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*/
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EdfScheduler(int initialCapacity, Random random) {
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this.prioQueue = new PriorityQueue<ObjectState>(initialCapacity, (o1, o2) -> {
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if (o1.deadline == o2.deadline) {
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return Integer.compare(o1.index, o2.index);
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} else {
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return Double.compare(o1.deadline, o2.deadline);
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StaticStrideScheduler(float[] weights, Random random) {
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checkArgument(weights.length >= 1, "Couldn't build scheduler: requires at least one weight");
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int numChannels = weights.length;
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int numWeightedChannels = 0;
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double sumWeight = 0;
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float maxWeight = 0;
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short meanWeight = 0;
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for (float weight : weights) {
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if (weight > 0) {
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sumWeight += weight;
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maxWeight = Math.max(weight, maxWeight);
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numWeightedChannels++;
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}
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});
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this.random = random;
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}
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double scalingFactor = K_MAX_WEIGHT / maxWeight;
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if (numWeightedChannels > 0) {
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meanWeight = (short) Math.round(scalingFactor * sumWeight / numWeightedChannels);
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} else {
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meanWeight = 1;
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}
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// scales weights s.t. max(weights) == K_MAX_WEIGHT, meanWeight is scaled accordingly
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short[] scaledWeights = new short[numChannels];
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for (int i = 0; i < numChannels; i++) {
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if (weights[i] <= 0) {
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scaledWeights[i] = meanWeight;
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} else {
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scaledWeights[i] = (short) Math.round(weights[i] * scalingFactor);
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}
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}
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this.scaledWeights = scaledWeights;
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this.sizeDivisor = numChannels;
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this.sequence = new AtomicInteger(random.nextInt());
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}
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/**
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* Adds the item in the scheduler. This is not thread safe.
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*
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* @param index The field {@link ObjectState#index} to be added
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* @param weight positive weight for the added object
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*/
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void add(int index, double weight) {
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checkArgument(weight > 0.0, "Weights need to be positive.");
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ObjectState state = new ObjectState(Math.max(weight, MINIMUM_WEIGHT), index);
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// Randomize the initial deadline.
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state.deadline = random.nextDouble() * (1 / state.weight);
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prioQueue.add(state);
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/** Returns the next sequence number and atomically increases sequence with wraparound. */
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private long nextSequence() {
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return Integer.toUnsignedLong(sequence.getAndIncrement());
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}
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/**
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* Picks the next WRR object.
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@VisibleForTesting
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long getSequence() {
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return Integer.toUnsignedLong(sequence.get());
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}
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/*
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* Selects index of next backend server.
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* <p>
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* A 2D array is compactly represented as a function of W(backend), where the row
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* represents the generation and the column represents the backend index:
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* X(backend,generation) | generation ∈ [0,kMaxWeight).
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* Each element in the conceptual array is a boolean indicating whether the backend at
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* this index should be picked now. If false, the counter is incremented again,
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* and the new element is checked. An atomically incremented counter keeps track of our
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* backend and generation through modular arithmetic within the pick() method.
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* <p>
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* Modular arithmetic allows us to evenly distribute picks and skips between
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* generations based on W(backend).
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* X(backend,generation) = (W(backend) * generation) % kMaxWeight >= kMaxWeight - W(backend)
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* If we have the same three backends with weights:
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* W(backend) = {2,3,6} scaled to max(W(backend)) = 6, then X(backend,generation) is:
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* <p>
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* B0 B1 B2
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* T T T
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* F F T
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* F T T
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* T F T
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* F T T
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* F F T
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* The sequence of picked backend indices is given by
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* walking across and down: {0,1,2,2,1,2,0,2,1,2,2}.
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* <p>
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* To reduce the variance and spread the wasted work among different picks,
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* an offset that varies per backend index is also included to the calculation.
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*/
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int pick() {
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synchronized (lock) {
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ObjectState minObject = prioQueue.remove();
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minObject.deadline += 1.0 / minObject.weight;
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prioQueue.add(minObject);
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return minObject.index;
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while (true) {
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long sequence = this.nextSequence();
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int backendIndex = (int) (sequence % this.sizeDivisor);
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long generation = sequence / this.sizeDivisor;
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int weight = Short.toUnsignedInt(this.scaledWeights[backendIndex]);
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long offset = (long) K_MAX_WEIGHT / 2 * backendIndex;
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if ((weight * generation + offset) % K_MAX_WEIGHT < K_MAX_WEIGHT - weight) {
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continue;
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}
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return backendIndex;
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}
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}
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}
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/** Holds the state of the object. */
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@VisibleForTesting
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static class ObjectState {
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private final double weight;
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private final int index;
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private volatile double deadline;
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ObjectState(double weight, int index) {
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this.weight = weight;
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this.index = index;
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}
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}
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static final class WeightedRoundRobinLoadBalancerConfig {
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final long blackoutPeriodNanos;
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final long weightExpirationPeriodNanos;
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@ -52,7 +52,7 @@ import io.grpc.SynchronizationContext;
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import io.grpc.internal.FakeClock;
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import io.grpc.services.InternalCallMetricRecorder;
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import io.grpc.services.MetricReport;
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import io.grpc.xds.WeightedRoundRobinLoadBalancer.EdfScheduler;
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import io.grpc.xds.WeightedRoundRobinLoadBalancer.StaticStrideScheduler;
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import io.grpc.xds.WeightedRoundRobinLoadBalancer.WeightedRoundRobinLoadBalancerConfig;
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import io.grpc.xds.WeightedRoundRobinLoadBalancer.WeightedRoundRobinPicker;
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import io.grpc.xds.WeightedRoundRobinLoadBalancer.WrrSubchannel;
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@ -175,7 +175,7 @@ public class WeightedRoundRobinLoadBalancerTest {
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}
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});
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wrr = new WeightedRoundRobinLoadBalancer(helper, fakeClock.getDeadlineTicker(),
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new FakeRandom());
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new FakeRandom(0));
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}
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@Test
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@ -220,7 +220,7 @@ public class WeightedRoundRobinLoadBalancerTest {
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0.2, 0, 0.1, 1, 0, new HashMap<>(), new HashMap<>()));
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assertThat(fakeClock.forwardTime(11, TimeUnit.SECONDS)).isEqualTo(1);
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assertThat(weightedPicker.pickSubchannel(mockArgs)
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.getSubchannel()).isEqualTo(weightedSubchannel1);
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.getSubchannel()).isEqualTo(weightedSubchannel1);
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assertThat(fakeClock.getPendingTasks().size()).isEqualTo(1);
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weightedConfig = WeightedRoundRobinLoadBalancerConfig.newBuilder()
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.setWeightUpdatePeriodNanos(500_000_000L) //.5s
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@ -338,7 +338,7 @@ public class WeightedRoundRobinLoadBalancerTest {
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}
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@Test
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public void pickByWeight_LargeWeight() {
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public void pickByWeight_largeWeight() {
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MetricReport report1 = InternalCallMetricRecorder.createMetricReport(
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0.1, 0, 0.1, 999, 0, new HashMap<>(), new HashMap<>());
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MetricReport report2 = InternalCallMetricRecorder.createMetricReport(
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@ -593,6 +593,7 @@ public class WeightedRoundRobinLoadBalancerTest {
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assertThat(fakeClock.forwardTime(500, TimeUnit.MILLISECONDS)).isEqualTo(1);
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assertThat(weightedPicker.pickSubchannel(mockArgs)
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.getSubchannel()).isEqualTo(weightedSubchannel2);
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}
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@Test
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@ -750,12 +751,12 @@ public class WeightedRoundRobinLoadBalancerTest {
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}
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assertThat(pickCount.size()).isEqualTo(3);
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assertThat(Math.abs(pickCount.get(weightedSubchannel1) / 1000.0 - 4.0 / 9))
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.isAtMost(0.001);
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.isAtMost(0.002);
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assertThat(Math.abs(pickCount.get(weightedSubchannel2) / 1000.0 - 2.0 / 9))
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.isAtMost(0.001);
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.isAtMost(0.002);
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// subchannel3's weight is average of subchannel1 and subchannel2
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assertThat(Math.abs(pickCount.get(weightedSubchannel3) / 1000.0 - 3.0 / 9))
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.isAtMost(0.001);
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.isAtMost(0.002);
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}
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@Test
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@ -821,37 +822,6 @@ public class WeightedRoundRobinLoadBalancerTest {
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.isAtMost(0.001);
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}
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@Test
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public void edfScheduler() {
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Random random = new Random();
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double totalWeight = 0;
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int capacity = random.nextInt(10) + 1;
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double[] weights = new double[capacity];
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EdfScheduler scheduler = new EdfScheduler(capacity, random);
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for (int i = 0; i < capacity; i++) {
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weights[i] = random.nextDouble();
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scheduler.add(i, weights[i]);
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totalWeight += weights[i];
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}
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Map<Integer, Integer> pickCount = new HashMap<>();
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for (int i = 0; i < 1000; i++) {
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int result = scheduler.pick();
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pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
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}
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for (int i = 0; i < capacity; i++) {
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assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight) )
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.isAtMost(0.01);
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}
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}
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@Test
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public void edsScheduler_sameWeight() {
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EdfScheduler scheduler = new EdfScheduler(2, new FakeRandom());
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scheduler.add(0, 0.5);
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scheduler.add(1, 0.5);
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assertThat(scheduler.pick()).isEqualTo(0);
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}
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@Test(expected = NullPointerException.class)
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public void wrrConfig_TimeValueNonNull() {
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WeightedRoundRobinLoadBalancerConfig.newBuilder().setBlackoutPeriodNanos((Long) null);
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@ -862,6 +832,267 @@ public class WeightedRoundRobinLoadBalancerTest {
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WeightedRoundRobinLoadBalancerConfig.newBuilder().setEnableOobLoadReport((Boolean) null);
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}
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@Test(expected = IllegalArgumentException.class)
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public void emptyWeights() {
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float[] weights = {};
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Random random = new Random();
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StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
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sss.pick();
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}
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@Test
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public void testPicksEqualsWeights() {
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float[] weights = {1.0f, 2.0f, 3.0f};
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Random random = new Random();
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StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
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int[] expectedPicks = new int[] {1, 2, 3};
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int[] picks = new int[3];
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for (int i = 0; i < 6; i++) {
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picks[sss.pick()] += 1;
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}
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assertThat(picks).isEqualTo(expectedPicks);
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}
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@Test
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public void testContainsZeroWeightUseMean() {
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float[] weights = {3.0f, 0.0f, 1.0f};
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Random random = new Random();
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StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
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int[] expectedPicks = new int[] {3, 2, 1};
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int[] picks = new int[3];
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for (int i = 0; i < 6; i++) {
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picks[sss.pick()] += 1;
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}
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assertThat(picks).isEqualTo(expectedPicks);
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}
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@Test
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public void testContainsNegativeWeightUseMean() {
|
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float[] weights = {3.0f, -1.0f, 1.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
int[] expectedPicks = new int[] {3, 2, 1};
|
||||
int[] picks = new int[3];
|
||||
for (int i = 0; i < 6; i++) {
|
||||
picks[sss.pick()] += 1;
|
||||
}
|
||||
assertThat(picks).isEqualTo(expectedPicks);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testAllSameWeights() {
|
||||
float[] weights = {1.0f, 1.0f, 1.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
int[] expectedPicks = new int[] {2, 2, 2};
|
||||
int[] picks = new int[3];
|
||||
for (int i = 0; i < 6; i++) {
|
||||
picks[sss.pick()] += 1;
|
||||
}
|
||||
assertThat(picks).isEqualTo(expectedPicks);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testAllZeroWeightsUseOne() {
|
||||
float[] weights = {0.0f, 0.0f, 0.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
int[] expectedPicks = new int[] {2, 2, 2};
|
||||
int[] picks = new int[3];
|
||||
for (int i = 0; i < 6; i++) {
|
||||
picks[sss.pick()] += 1;
|
||||
}
|
||||
assertThat(picks).isEqualTo(expectedPicks);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testAllInvalidWeightsUseOne() {
|
||||
float[] weights = {-3.1f, -0.0f, 0.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
int[] expectedPicks = new int[] {2, 2, 2};
|
||||
int[] picks = new int[3];
|
||||
for (int i = 0; i < 6; i++) {
|
||||
picks[sss.pick()] += 1;
|
||||
}
|
||||
assertThat(picks).isEqualTo(expectedPicks);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testLargestWeightIndexPickedEveryGeneration() {
|
||||
float[] weights = {1.0f, 2.0f, 3.0f};
|
||||
int largestWeightIndex = 2;
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
int largestWeightPickCount = 0;
|
||||
int kMaxWeight = 65535;
|
||||
for (int i = 0; i < largestWeightIndex * kMaxWeight; i++) {
|
||||
if (sss.pick() == largestWeightIndex) {
|
||||
largestWeightPickCount += 1;
|
||||
}
|
||||
}
|
||||
assertThat(largestWeightPickCount).isEqualTo(kMaxWeight);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testStaticStrideSchedulerNonIntegers1() {
|
||||
float[] weights = {2.0f, (float) (10.0 / 3.0), 1.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 2 + 10.0 / 3.0 + 1.0;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 3; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.01);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testStaticStrideSchedulerNonIntegers2() {
|
||||
float[] weights = {0.5f, 0.3f, 1.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 1.8;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 3; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.01);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testTwoWeights() {
|
||||
float[] weights = {1.0f, 2.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 3;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 2; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.01);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testManyWeights() {
|
||||
float[] weights = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 15;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 5; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.0011);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testManyComplexWeights() {
|
||||
float[] weights = {1.2f, 2.4f, 222.56f, 1.1f, 15.0f, 226342.0f, 5123.0f, 532.2f};
|
||||
Random random = new Random();
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 1.2 + 2.4 + 222.56 + 15.0 + 226342.0 + 5123.0 + 0.0001;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 8; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.01);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testDeterministicPicks() {
|
||||
float[] weights = {2.0f, 3.0f, 6.0f};
|
||||
Random random = new FakeRandom(0);
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
assertThat(sss.getSequence()).isEqualTo(0);
|
||||
assertThat(sss.pick()).isEqualTo(1);
|
||||
assertThat(sss.getSequence()).isEqualTo(2);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(3);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(6);
|
||||
assertThat(sss.pick()).isEqualTo(0);
|
||||
assertThat(sss.getSequence()).isEqualTo(7);
|
||||
assertThat(sss.pick()).isEqualTo(1);
|
||||
assertThat(sss.getSequence()).isEqualTo(8);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(9);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testImmediateWraparound() {
|
||||
float[] weights = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
|
||||
Random random = new FakeRandom(-1);
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 15;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 5; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.001);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testWraparound() {
|
||||
float[] weights = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f};
|
||||
Random random = new FakeRandom(-500);
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
double totalWeight = 15;
|
||||
Map<Integer, Integer> pickCount = new HashMap<>();
|
||||
for (int i = 0; i < 1000; i++) {
|
||||
int result = sss.pick();
|
||||
pickCount.put(result, pickCount.getOrDefault(result, 0) + 1);
|
||||
}
|
||||
for (int i = 0; i < 5; i++) {
|
||||
assertThat(Math.abs(pickCount.getOrDefault(i, 0) / 1000.0 - weights[i] / totalWeight))
|
||||
.isAtMost(0.0011);
|
||||
}
|
||||
}
|
||||
|
||||
@Test
|
||||
public void testDeterministicWraparound() {
|
||||
float[] weights = {2.0f, 3.0f, 6.0f};
|
||||
Random random = new FakeRandom(-1);
|
||||
StaticStrideScheduler sss = new StaticStrideScheduler(weights, random);
|
||||
assertThat(sss.getSequence()).isEqualTo(0xFFFF_FFFFL);
|
||||
assertThat(sss.pick()).isEqualTo(1);
|
||||
assertThat(sss.getSequence()).isEqualTo(2);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(3);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(6);
|
||||
assertThat(sss.pick()).isEqualTo(0);
|
||||
assertThat(sss.getSequence()).isEqualTo(7);
|
||||
assertThat(sss.pick()).isEqualTo(1);
|
||||
assertThat(sss.getSequence()).isEqualTo(8);
|
||||
assertThat(sss.pick()).isEqualTo(2);
|
||||
assertThat(sss.getSequence()).isEqualTo(9);
|
||||
}
|
||||
|
||||
private static class FakeSocketAddress extends SocketAddress {
|
||||
final String name;
|
||||
|
||||
|
|
@ -875,10 +1106,16 @@ public class WeightedRoundRobinLoadBalancerTest {
|
|||
}
|
||||
|
||||
private static class FakeRandom extends Random {
|
||||
private int nextInt;
|
||||
|
||||
public FakeRandom(int nextInt) {
|
||||
this.nextInt = nextInt;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double nextDouble() {
|
||||
public int nextInt() {
|
||||
// return constant value to disable init deadline randomization in the scheduler
|
||||
return 0.322023;
|
||||
return nextInt;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue