YARN-736. Add a multi-resource fair sharing metric. (sandyr via tucu)
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1496153 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Alejandro Abdelnur
parent
ca35235b04
commit
e60fbbcc2e
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@ -39,6 +39,8 @@ Release 2.2.0 - UNRELEASED
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YARN-866. Add test for class ResourceWeights. (ywskycn via tucu)
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YARN-736. Add a multi-resource fair sharing metric. (sandyr via tucu)
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OPTIMIZATIONS
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BUG FIXES
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@ -60,6 +60,39 @@ public class Resources {
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}
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};
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private static final Resource UNBOUNDED = new Resource() {
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@Override
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public int getMemory() {
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return Integer.MAX_VALUE;
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}
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@Override
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public void setMemory(int memory) {
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throw new RuntimeException("NONE cannot be modified!");
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}
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@Override
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public int getVirtualCores() {
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return Integer.MAX_VALUE;
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}
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@Override
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public void setVirtualCores(int cores) {
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throw new RuntimeException("NONE cannot be modified!");
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}
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@Override
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public int compareTo(Resource o) {
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int diff = 0 - o.getMemory();
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if (diff == 0) {
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diff = 0 - o.getVirtualCores();
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}
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return diff;
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}
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};
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public static Resource createResource(int memory) {
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return createResource(memory, (memory > 0) ? 1 : 0);
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@ -75,6 +108,10 @@ public class Resources {
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public static Resource none() {
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return NONE;
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}
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public static Resource unbounded() {
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return UNBOUNDED;
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}
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public static Resource clone(Resource res) {
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return createResource(res.getMemory(), res.getVirtualCores());
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@ -109,7 +109,12 @@ public class AppSchedulable extends Schedulable {
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@Override
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public Resource getMinShare() {
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return Resources.createResource(0);
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return Resources.none();
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}
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@Override
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public Resource getMaxShare() {
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return Resources.unbounded();
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}
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/**
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@ -93,6 +93,11 @@ public abstract class FSQueue extends Schedulable implements Queue {
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public Resource getMinShare() {
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return queueMgr.getMinResources(getName());
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}
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@Override
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public Resource getMaxShare() {
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return queueMgr.getMaxResources(getName());
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}
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@Override
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public long getStartTime() {
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@ -311,7 +311,7 @@ public class FairScheduler implements ResourceScheduler {
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* defined as being below half its fair share.
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*/
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boolean isStarvedForFairShare(FSLeafQueue sched) {
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Resource desiredFairShare = Resources.max(RESOURCE_CALCULATOR, clusterCapacity,
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Resource desiredFairShare = Resources.min(RESOURCE_CALCULATOR, clusterCapacity,
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Resources.multiply(sched.getFairShare(), .5), sched.getDemand());
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return Resources.lessThan(RESOURCE_CALCULATOR, clusterCapacity,
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sched.getResourceUsage(), desiredFairShare);
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@ -79,6 +79,8 @@ public abstract class Schedulable {
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/** Minimum Resource share assigned to the schedulable. */
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public abstract Resource getMinShare();
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/** Maximum Resource share assigned to the schedulable. */
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public abstract Resource getMaxShare();
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/** Job/queue weight in fair sharing. */
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public abstract ResourceWeights getWeights();
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@ -0,0 +1,172 @@
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/**
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies;
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import java.util.Collection;
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import org.apache.hadoop.yarn.api.records.Resource;
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import org.apache.hadoop.yarn.server.resourcemanager.resource.ResourceType;
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import org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.Schedulable;
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/**
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* Contains logic for computing the fair shares. A {@link Schedulable}'s fair
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* share is {@link Resource} it is entitled to, independent of the current
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* demands and allocations on the cluster. A {@link Schedulable} whose resource
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* consumption lies at or below its fair share will never have its containers
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* preempted.
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*/
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public class ComputeFairShares {
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private static final int COMPUTE_FAIR_SHARES_ITERATIONS = 25;
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/**
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* Given a set of Schedulables and a number of slots, compute their weighted
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* fair shares. The min and max shares and of the Schedulables are assumed to
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* be set beforehand. We compute the fairest possible allocation of shares to
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* the Schedulables that respects their min and max shares.
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*
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* To understand what this method does, we must first define what weighted
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* fair sharing means in the presence of min and max shares. If there
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* were no minimum or maximum shares, then weighted fair sharing would be
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* achieved if the ratio of slotsAssigned / weight was equal for each
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* Schedulable and all slots were assigned. Minimum and maximum shares add a
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* further twist - Some Schedulables may have a min share higher than their
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* assigned share or a max share lower than their assigned share.
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*
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* To deal with these possibilities, we define an assignment of slots as being
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* fair if there exists a ratio R such that: Schedulables S where S.minShare
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* > R * S.weight are given share S.minShare - Schedulables S where S.maxShare
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* < R * S.weight are given S.maxShare - All other Schedulables S are
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* assigned share R * S.weight - The sum of all the shares is totalSlots.
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*
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* We call R the weight-to-slots ratio because it converts a Schedulable's
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* weight to the number of slots it is assigned.
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*
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* We compute a fair allocation by finding a suitable weight-to-slot ratio R.
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* To do this, we use binary search. Given a ratio R, we compute the number of
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* slots that would be used in total with this ratio (the sum of the shares
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* computed using the conditions above). If this number of slots is less than
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* totalSlots, then R is too small and more slots could be assigned. If the
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* number of slots is more than totalSlots, then R is too large.
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*
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* We begin the binary search with a lower bound on R of 0 (which means that
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* all Schedulables are only given their minShare) and an upper bound computed
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* to be large enough that too many slots are given (by doubling R until we
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* use more than totalResources resources). The helper method
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* resourceUsedWithWeightToResourceRatio computes the total resources used with a
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* given value of R.
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*
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* The running time of this algorithm is linear in the number of Schedulables,
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* because resourceUsedWithWeightToResourceRatio is linear-time and the number of
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* iterations of binary search is a constant (dependent on desired precision).
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*/
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public static void computeShares(
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Collection<? extends Schedulable> schedulables, Resource totalResources,
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ResourceType type) {
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if (schedulables.isEmpty()) {
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return;
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}
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// Find an upper bound on R that we can use in our binary search. We start
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// at R = 1 and double it until we have either used all the resources or we
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// have met all Schedulables' max shares.
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int totalMaxShare = 0;
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for (Schedulable sched : schedulables) {
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int maxShare = getResourceValue(sched.getMaxShare(), type);
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if (maxShare == Integer.MAX_VALUE) {
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totalMaxShare = Integer.MAX_VALUE;
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break;
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} else {
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totalMaxShare += maxShare;
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}
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}
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int totalResource = Math.min(totalMaxShare,
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getResourceValue(totalResources, type));
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double rMax = 1.0;
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while (resourceUsedWithWeightToResourceRatio(rMax, schedulables, type)
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< totalResource) {
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rMax *= 2.0;
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}
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// Perform the binary search for up to COMPUTE_FAIR_SHARES_ITERATIONS steps
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double left = 0;
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double right = rMax;
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for (int i = 0; i < COMPUTE_FAIR_SHARES_ITERATIONS; i++) {
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double mid = (left + right) / 2.0;
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if (resourceUsedWithWeightToResourceRatio(mid, schedulables, type) <
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totalResource) {
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left = mid;
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} else {
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right = mid;
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}
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}
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// Set the fair shares based on the value of R we've converged to
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for (Schedulable sched : schedulables) {
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setResourceValue(computeShare(sched, right, type), sched.getFairShare(), type);
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}
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}
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/**
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* Compute the resources that would be used given a weight-to-resource ratio
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* w2rRatio, for use in the computeFairShares algorithm as described in #
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*/
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private static int resourceUsedWithWeightToResourceRatio(double w2rRatio,
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Collection<? extends Schedulable> schedulables, ResourceType type) {
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int resourcesTaken = 0;
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for (Schedulable sched : schedulables) {
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int share = computeShare(sched, w2rRatio, type);
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resourcesTaken += share;
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}
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return resourcesTaken;
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}
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/**
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* Compute the resources assigned to a Schedulable given a particular
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* weight-to-resource ratio w2rRatio.
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*/
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private static int computeShare(Schedulable sched, double w2rRatio,
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ResourceType type) {
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double share = sched.getWeights().getWeight(type) * w2rRatio;
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share = Math.max(share, getResourceValue(sched.getMinShare(), type));
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share = Math.min(share, getResourceValue(sched.getMaxShare(), type));
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return (int) share;
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}
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private static int getResourceValue(Resource resource, ResourceType type) {
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switch (type) {
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case MEMORY:
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return resource.getMemory();
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case CPU:
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return resource.getVirtualCores();
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default:
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throw new IllegalArgumentException("Invalid resource");
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}
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}
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private static void setResourceValue(int val, Resource resource, ResourceType type) {
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switch (type) {
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case MEMORY:
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resource.setMemory(val);
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break;
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case CPU:
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resource.setVirtualCores(val);
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break;
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default:
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throw new IllegalArgumentException("Invalid resource");
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}
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}
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}
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@ -64,13 +64,8 @@ public class DominantResourceFairnessPolicy extends SchedulingPolicy {
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@Override
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public void computeShares(Collection<? extends Schedulable> schedulables,
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Resource totalResources) {
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// TODO: For now, set all fair shares to 0, because, in the context of DRF,
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// it doesn't make sense to set a value for each resource. YARN-736 should
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// add in a sensible replacement.
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for (Schedulable schedulable : schedulables) {
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schedulable.setFairShare(Resources.none());
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for (ResourceType type : ResourceType.values()) {
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ComputeFairShares.computeShares(schedulables, totalResources, type);
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}
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}
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@ -116,120 +116,7 @@ public class FairSharePolicy extends SchedulingPolicy {
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@Override
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public void computeShares(Collection<? extends Schedulable> schedulables,
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Resource totalResources) {
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computeFairShares(schedulables, totalResources);
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}
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/**
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* Number of iterations for the binary search in computeFairShares. This is
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* equivalent to the number of bits of precision in the output. 25 iterations
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* gives precision better than 0.1 slots in clusters with one million slots.
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*/
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private static final int COMPUTE_FAIR_SHARES_ITERATIONS = 25;
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/**
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* Given a set of Schedulables and a number of slots, compute their weighted
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* fair shares. The min shares and demands of the Schedulables are assumed to
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* be set beforehand. We compute the fairest possible allocation of shares to
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* the Schedulables that respects their min shares and demands.
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*
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* To understand what this method does, we must first define what weighted
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* fair sharing means in the presence of minimum shares and demands. If there
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* were no minimum shares and every Schedulable had an infinite demand (i.e.
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* could launch infinitely many tasks), then weighted fair sharing would be
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* achieved if the ratio of slotsAssigned / weight was equal for each
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* Schedulable and all slots were assigned. Minimum shares and demands add two
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* further twists: - Some Schedulables may not have enough tasks to fill all
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* their share. - Some Schedulables may have a min share higher than their
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* assigned share.
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*
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* To deal with these possibilities, we define an assignment of slots as being
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* fair if there exists a ratio R such that: - Schedulables S where S.demand <
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* R * S.weight are assigned share S.demand - Schedulables S where S.minShare
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* > R * S.weight are given share S.minShare - All other Schedulables S are
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* assigned share R * S.weight - The sum of all the shares is totalSlots.
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*
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* We call R the weight-to-slots ratio because it converts a Schedulable's
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* weight to the number of slots it is assigned.
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*
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* We compute a fair allocation by finding a suitable weight-to-slot ratio R.
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* To do this, we use binary search. Given a ratio R, we compute the number of
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* slots that would be used in total with this ratio (the sum of the shares
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* computed using the conditions above). If this number of slots is less than
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* totalSlots, then R is too small and more slots could be assigned. If the
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* number of slots is more than totalSlots, then R is too large.
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*
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* We begin the binary search with a lower bound on R of 0 (which means that
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* all Schedulables are only given their minShare) and an upper bound computed
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* to be large enough that too many slots are given (by doubling R until we
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* either use more than totalSlots slots or we fulfill all jobs' demands). The
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* helper method slotsUsedWithWeightToSlotRatio computes the total number of
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* slots used with a given value of R.
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*
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* The running time of this algorithm is linear in the number of Schedulables,
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* because slotsUsedWithWeightToSlotRatio is linear-time and the number of
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* iterations of binary search is a constant (dependent on desired precision).
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*/
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public static void computeFairShares(
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Collection<? extends Schedulable> schedulables, Resource totalResources) {
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// Find an upper bound on R that we can use in our binary search. We start
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// at R = 1 and double it until we have either used totalSlots slots or we
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// have met all Schedulables' demands (if total demand < totalSlots).
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Resource totalDemand = Resources.createResource(0);
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for (Schedulable sched : schedulables) {
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Resources.addTo(totalDemand, sched.getDemand());
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}
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Resource cap = Resources.min(RESOURCE_CALCULATOR, null, totalDemand,
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totalResources);
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double rMax = 1.0;
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while (Resources.lessThan(RESOURCE_CALCULATOR, null,
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resUsedWithWeightToResRatio(rMax, schedulables),
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cap)) {
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rMax *= 2.0;
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}
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// Perform the binary search for up to COMPUTE_FAIR_SHARES_ITERATIONS steps
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double left = 0;
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double right = rMax;
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for (int i = 0; i < COMPUTE_FAIR_SHARES_ITERATIONS; i++) {
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double mid = (left + right) / 2.0;
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if (Resources.lessThan(RESOURCE_CALCULATOR, null,
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resUsedWithWeightToResRatio(mid, schedulables),
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cap)) {
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left = mid;
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} else {
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right = mid;
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}
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}
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// Set the fair shares based on the value of R we've converged to
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for (Schedulable sched : schedulables) {
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sched.setFairShare(computeShare(sched, right));
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}
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}
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/**
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* Compute the number of slots that would be used given a weight-to-slot ratio
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* w2sRatio, for use in the computeFairShares algorithm as described in #
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* {@link SchedulingAlgorithms#computeFairShares(Collection, double)}.
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*/
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private static Resource resUsedWithWeightToResRatio(double w2sRatio,
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Collection<? extends Schedulable> schedulables) {
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Resource slotsTaken = Resources.createResource(0);
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for (Schedulable sched : schedulables) {
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Resource share = computeShare(sched, w2sRatio);
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Resources.addTo(slotsTaken, share);
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}
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return slotsTaken;
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}
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/**
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* Compute the resources assigned to a Schedulable given a particular
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* res-to-slot ratio r2sRatio, for use in computeFairShares as described in #
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* {@link SchedulingAlgorithms#computeFairShares(Collection, double)}.
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*/
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private static Resource computeShare(Schedulable sched, double r2sRatio) {
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double share = sched.getWeights().getWeight(ResourceType.MEMORY) * r2sRatio;
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share = Math.max(share, sched.getMinShare().getMemory());
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share = Math.min(share, sched.getDemand().getMemory());
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return Resources.createResource((int) share);
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ComputeFairShares.computeShares(schedulables, totalResources, ResourceType.MEMORY);
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}
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@Override
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@ -73,9 +73,14 @@ public class FifoPolicy extends SchedulingPolicy {
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@Override
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public void computeShares(Collection<? extends Schedulable> schedulables,
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Resource totalResources) {
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for (Schedulable sched : schedulables) {
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sched.setFairShare(Resources.createResource(0));
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Schedulable earliest = null;
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for (Schedulable schedulable : schedulables) {
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if (earliest == null ||
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schedulable.getStartTime() < earliest.getStartTime()) {
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earliest = schedulable;
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}
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}
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earliest.setFairShare(Resources.clone(totalResources));
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}
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@Override
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@ -28,40 +28,49 @@ import org.apache.hadoop.yarn.util.resource.Resources;
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* Dummy implementation of Schedulable for unit testing.
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*/
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public class FakeSchedulable extends Schedulable {
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private Resource demand;
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private Resource usage;
|
||||
private Resource minShare;
|
||||
private Resource maxShare;
|
||||
private ResourceWeights weights;
|
||||
private Priority priority;
|
||||
private long startTime;
|
||||
|
||||
public FakeSchedulable() {
|
||||
this(0, 0, 1, 0, 0, 0);
|
||||
this(0, Integer.MAX_VALUE, 1, 0, 0, 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(int demand) {
|
||||
this(demand, 0, 1, 0, 0, 0);
|
||||
public FakeSchedulable(int minShare) {
|
||||
this(minShare, Integer.MAX_VALUE, 1, 0, 0, 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(int demand, int minShare) {
|
||||
this(demand, minShare, 1, 0, 0, 0);
|
||||
public FakeSchedulable(int minShare, int maxShare) {
|
||||
this(minShare, maxShare, 1, 0, 0, 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(int demand, int minShare, double memoryWeight) {
|
||||
this(demand, minShare, memoryWeight, 0, 0, 0);
|
||||
public FakeSchedulable(int minShare, double memoryWeight) {
|
||||
this(minShare, Integer.MAX_VALUE, memoryWeight, 0, 0, 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(int demand, int minShare, double weight, int fairShare, int usage,
|
||||
public FakeSchedulable(int minShare, int maxShare, double memoryWeight) {
|
||||
this(minShare, maxShare, memoryWeight, 0, 0, 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(int minShare, int maxShare, double weight, int fairShare, int usage,
|
||||
long startTime) {
|
||||
this(Resources.createResource(demand), Resources.createResource(minShare),
|
||||
new ResourceWeights((float)weight), Resources.createResource(fairShare),
|
||||
Resources.createResource(usage), startTime);
|
||||
this(Resources.createResource(minShare, 0), Resources.createResource(maxShare, 0),
|
||||
new ResourceWeights((float)weight), Resources.createResource(fairShare, 0),
|
||||
Resources.createResource(usage, 0), startTime);
|
||||
}
|
||||
|
||||
public FakeSchedulable(Resource demand, Resource minShare, ResourceWeights weight,
|
||||
Resource fairShare, Resource usage, long startTime) {
|
||||
this.demand = demand;
|
||||
public FakeSchedulable(Resource minShare, ResourceWeights weights) {
|
||||
this(minShare, Resources.createResource(Integer.MAX_VALUE, Integer.MAX_VALUE),
|
||||
weights, Resources.createResource(0, 0), Resources.createResource(0, 0), 0);
|
||||
}
|
||||
|
||||
public FakeSchedulable(Resource minShare, Resource maxShare,
|
||||
ResourceWeights weight, Resource fairShare, Resource usage, long startTime) {
|
||||
this.minShare = minShare;
|
||||
this.maxShare = maxShare;
|
||||
this.weights = weight;
|
||||
setFairShare(fairShare);
|
||||
this.usage = usage;
|
||||
|
|
@ -76,7 +85,7 @@ public class FakeSchedulable extends Schedulable {
|
|||
|
||||
@Override
|
||||
public Resource getDemand() {
|
||||
return demand;
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
|
|
@ -108,6 +117,11 @@ public class FakeSchedulable extends Schedulable {
|
|||
public Resource getMinShare() {
|
||||
return minShare;
|
||||
}
|
||||
|
||||
@Override
|
||||
public Resource getMaxShare() {
|
||||
return maxShare;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void updateDemand() {}
|
||||
|
|
|
|||
|
|
@ -23,8 +23,10 @@ import java.util.List;
|
|||
|
||||
import junit.framework.Assert;
|
||||
|
||||
import org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.FairSharePolicy;
|
||||
import org.apache.hadoop.yarn.util.resource.Resources;
|
||||
import org.apache.hadoop.yarn.server.resourcemanager.resource.ResourceType;
|
||||
import org.apache.hadoop.yarn.server.resourcemanager.resource.ResourceWeights;
|
||||
import org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.policies.ComputeFairShares;
|
||||
import org.junit.Before;
|
||||
import org.junit.Test;
|
||||
|
||||
|
|
@ -33,12 +35,10 @@ import org.junit.Test;
|
|||
*/
|
||||
public class TestComputeFairShares {
|
||||
private List<Schedulable> scheds;
|
||||
private SchedulingPolicy schedulingMode;
|
||||
|
||||
@Before
|
||||
public void setUp() throws Exception {
|
||||
scheds = new ArrayList<Schedulable>();
|
||||
schedulingMode = new FairSharePolicy();
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -47,13 +47,13 @@ public class TestComputeFairShares {
|
|||
*/
|
||||
@Test
|
||||
public void testEqualSharing() {
|
||||
scheds.add(new FakeSchedulable(100));
|
||||
scheds.add(new FakeSchedulable(50));
|
||||
scheds.add(new FakeSchedulable(30));
|
||||
scheds.add(new FakeSchedulable(20));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(40));
|
||||
verifyShares(10, 10, 10, 10);
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(40), ResourceType.MEMORY);
|
||||
verifyMemoryShares(10, 10, 10, 10);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -64,16 +64,17 @@ public class TestComputeFairShares {
|
|||
* so it only gets 11 slots. Pools 1 and 2 split the rest and get 13 each.
|
||||
*/
|
||||
@Test
|
||||
public void testLowDemands() {
|
||||
scheds.add(new FakeSchedulable(100));
|
||||
scheds.add(new FakeSchedulable(50));
|
||||
scheds.add(new FakeSchedulable(11));
|
||||
scheds.add(new FakeSchedulable(3));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(40));
|
||||
verifyShares(13, 13, 11, 3);
|
||||
public void testLowMaxShares() {
|
||||
scheds.add(new FakeSchedulable(0, 100));
|
||||
scheds.add(new FakeSchedulable(0, 50));
|
||||
scheds.add(new FakeSchedulable(0, 11));
|
||||
scheds.add(new FakeSchedulable(0, 3));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(40), ResourceType.MEMORY);
|
||||
verifyMemoryShares(13, 13, 11, 3);
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* In this test, some pools have minimum shares set. Pool 1 has a min share
|
||||
* of 20 so it gets 20 slots. Pool 2 also has a min share of 20, but its
|
||||
|
|
@ -84,13 +85,13 @@ public class TestComputeFairShares {
|
|||
*/
|
||||
@Test
|
||||
public void testMinShares() {
|
||||
scheds.add(new FakeSchedulable(100, 20));
|
||||
scheds.add(new FakeSchedulable(10, 20));
|
||||
scheds.add(new FakeSchedulable(10, 0));
|
||||
scheds.add(new FakeSchedulable(3, 2));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(40));
|
||||
verifyShares(20, 10, 7, 3);
|
||||
scheds.add(new FakeSchedulable(20));
|
||||
scheds.add(new FakeSchedulable(18));
|
||||
scheds.add(new FakeSchedulable(0));
|
||||
scheds.add(new FakeSchedulable(2));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(40), ResourceType.MEMORY);
|
||||
verifyMemoryShares(20, 18, 0, 2);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -99,15 +100,15 @@ public class TestComputeFairShares {
|
|||
*/
|
||||
@Test
|
||||
public void testWeightedSharing() {
|
||||
scheds.add(new FakeSchedulable(100, 0, 2.0));
|
||||
scheds.add(new FakeSchedulable(50, 0, 1.0));
|
||||
scheds.add(new FakeSchedulable(30, 0, 1.0));
|
||||
scheds.add(new FakeSchedulable(20, 0, 0.5));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(45));
|
||||
verifyShares(20, 10, 10, 5);
|
||||
scheds.add(new FakeSchedulable(0, 2.0));
|
||||
scheds.add(new FakeSchedulable(0, 1.0));
|
||||
scheds.add(new FakeSchedulable(0, 1.0));
|
||||
scheds.add(new FakeSchedulable(0, 0.5));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(45), ResourceType.MEMORY);
|
||||
verifyMemoryShares(20, 10, 10, 5);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Weighted sharing test where pools 1 and 2 are now given lower demands than
|
||||
* above. Pool 1 stops at 10 slots, leaving 35. If the remaining pools split
|
||||
|
|
@ -116,16 +117,17 @@ public class TestComputeFairShares {
|
|||
* the 24 slots left into a 1:0.5 ratio, getting 16 and 8 slots respectively.
|
||||
*/
|
||||
@Test
|
||||
public void testWeightedSharingWithLowDemands() {
|
||||
scheds.add(new FakeSchedulable(10, 0, 2.0));
|
||||
scheds.add(new FakeSchedulable(11, 0, 1.0));
|
||||
scheds.add(new FakeSchedulable(30, 0, 1.0));
|
||||
scheds.add(new FakeSchedulable(20, 0, 0.5));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(45));
|
||||
verifyShares(10, 11, 16, 8);
|
||||
public void testWeightedSharingWithMaxShares() {
|
||||
scheds.add(new FakeSchedulable(0, 10, 2.0));
|
||||
scheds.add(new FakeSchedulable(0, 11, 1.0));
|
||||
scheds.add(new FakeSchedulable(0, 30, 1.0));
|
||||
scheds.add(new FakeSchedulable(0, 20, 0.5));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(45), ResourceType.MEMORY);
|
||||
verifyMemoryShares(10, 11, 16, 8);
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Weighted fair sharing test with min shares. As in the min share test above,
|
||||
* pool 1 has a min share greater than its demand so it only gets its demand.
|
||||
|
|
@ -135,13 +137,13 @@ public class TestComputeFairShares {
|
|||
*/
|
||||
@Test
|
||||
public void testWeightedSharingWithMinShares() {
|
||||
scheds.add(new FakeSchedulable(10, 20, 2.0));
|
||||
scheds.add(new FakeSchedulable(11, 0, 1.0));
|
||||
scheds.add(new FakeSchedulable(30, 5, 1.0));
|
||||
scheds.add(new FakeSchedulable(20, 15, 0.5));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(45));
|
||||
verifyShares(10, 10, 10, 15);
|
||||
scheds.add(new FakeSchedulable(20, 2.0));
|
||||
scheds.add(new FakeSchedulable(0, 1.0));
|
||||
scheds.add(new FakeSchedulable(5, 1.0));
|
||||
scheds.add(new FakeSchedulable(15, 0.5));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(45), ResourceType.MEMORY);
|
||||
verifyMemoryShares(20, 5, 5, 15);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -151,28 +153,13 @@ public class TestComputeFairShares {
|
|||
@Test
|
||||
public void testLargeShares() {
|
||||
int million = 1000 * 1000;
|
||||
scheds.add(new FakeSchedulable(100 * million));
|
||||
scheds.add(new FakeSchedulable(50 * million));
|
||||
scheds.add(new FakeSchedulable(30 * million));
|
||||
scheds.add(new FakeSchedulable(20 * million));
|
||||
schedulingMode
|
||||
.computeShares(scheds,
|
||||
Resources.createResource(40 * million));
|
||||
verifyShares(10 * million, 10 * million, 10 * million, 10 * million);
|
||||
}
|
||||
|
||||
/**
|
||||
* Test that having a pool with 0 demand doesn't confuse the algorithm.
|
||||
*/
|
||||
@Test
|
||||
public void testZeroDemand() {
|
||||
scheds.add(new FakeSchedulable(100));
|
||||
scheds.add(new FakeSchedulable(50));
|
||||
scheds.add(new FakeSchedulable(30));
|
||||
scheds.add(new FakeSchedulable(0));
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(30));
|
||||
verifyShares(10, 10, 10, 0);
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
scheds.add(new FakeSchedulable());
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(40 * million), ResourceType.MEMORY);
|
||||
verifyMemoryShares(10 * million, 10 * million, 10 * million, 10 * million);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -180,18 +167,46 @@ public class TestComputeFairShares {
|
|||
*/
|
||||
@Test
|
||||
public void testEmptyList() {
|
||||
schedulingMode.computeShares(scheds,
|
||||
Resources.createResource(40));
|
||||
verifyShares();
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(40), ResourceType.MEMORY);
|
||||
verifyMemoryShares();
|
||||
}
|
||||
|
||||
/**
|
||||
* Test that CPU works as well as memory
|
||||
*/
|
||||
@Test
|
||||
public void testCPU() {
|
||||
scheds.add(new FakeSchedulable(Resources.createResource(0, 20),
|
||||
new ResourceWeights(2.0f)));
|
||||
scheds.add(new FakeSchedulable(Resources.createResource(0, 0),
|
||||
new ResourceWeights(1.0f)));
|
||||
scheds.add(new FakeSchedulable(Resources.createResource(0, 5),
|
||||
new ResourceWeights(1.0f)));
|
||||
scheds.add(new FakeSchedulable(Resources.createResource(0, 15),
|
||||
new ResourceWeights(0.5f)));
|
||||
ComputeFairShares.computeShares(scheds,
|
||||
Resources.createResource(0, 45), ResourceType.CPU);
|
||||
verifyCPUShares(20, 5, 5, 15);
|
||||
}
|
||||
|
||||
/**
|
||||
* Check that a given list of shares have been assigned to this.scheds.
|
||||
*/
|
||||
private void verifyShares(double... shares) {
|
||||
private void verifyMemoryShares(int... shares) {
|
||||
Assert.assertEquals(scheds.size(), shares.length);
|
||||
for (int i = 0; i < shares.length; i++) {
|
||||
Assert.assertEquals(shares[i], scheds.get(i).getFairShare().getMemory(), 0.01);
|
||||
Assert.assertEquals(shares[i], scheds.get(i).getFairShare().getMemory());
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Check that a given list of shares have been assigned to this.scheds.
|
||||
*/
|
||||
private void verifyCPUShares(int... shares) {
|
||||
Assert.assertEquals(scheds.size(), shares.length);
|
||||
for (int i = 0; i < shares.length; i++) {
|
||||
Assert.assertEquals(shares[i], scheds.get(i).getFairShare().getVirtualCores());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -374,11 +374,10 @@ public class TestFairScheduler {
|
|||
|
||||
Collection<FSLeafQueue> queues = scheduler.getQueueManager().getLeafQueues();
|
||||
assertEquals(3, queues.size());
|
||||
|
||||
|
||||
// Divided three ways - betwen the two queues and the default queue
|
||||
for (FSLeafQueue p : queues) {
|
||||
if (!p.getName().equals("root.default")) {
|
||||
assertEquals(5120, p.getFairShare().getMemory());
|
||||
}
|
||||
assertEquals(3414, p.getFairShare().getMemory());
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -393,7 +392,7 @@ public class TestFairScheduler {
|
|||
scheduler.handle(nodeEvent1);
|
||||
|
||||
// Have two queues which want entire cluster capacity
|
||||
createSchedulingRequest(10 * 1024, "queue1", "user1");
|
||||
createSchedulingRequest(10 * 1024, "default", "user1");
|
||||
createSchedulingRequest(10 * 1024, "parent.queue2", "user1");
|
||||
createSchedulingRequest(10 * 1024, "parent.queue3", "user1");
|
||||
|
||||
|
|
@ -401,9 +400,9 @@ public class TestFairScheduler {
|
|||
|
||||
QueueManager queueManager = scheduler.getQueueManager();
|
||||
Collection<FSLeafQueue> queues = queueManager.getLeafQueues();
|
||||
assertEquals(4, queues.size());
|
||||
assertEquals(3, queues.size());
|
||||
|
||||
FSLeafQueue queue1 = queueManager.getLeafQueue("queue1");
|
||||
FSLeafQueue queue1 = queueManager.getLeafQueue("default");
|
||||
FSLeafQueue queue2 = queueManager.getLeafQueue("parent.queue2");
|
||||
FSLeafQueue queue3 = queueManager.getLeafQueue("parent.queue3");
|
||||
assertEquals(capacity / 2, queue1.getFairShare().getMemory());
|
||||
|
|
|
|||
|
|
@ -64,8 +64,9 @@ public class TestDominantResourceFairnessPolicy {
|
|||
ResourceWeights weights, int minMemShare, int minCpuShare) {
|
||||
Resource usage = BuilderUtils.newResource(memUsage, cpuUsage);
|
||||
Resource minShare = BuilderUtils.newResource(minMemShare, minCpuShare);
|
||||
return new FakeSchedulable(Resources.none(), minShare, weights,
|
||||
Resources.none(), usage, 0l);
|
||||
return new FakeSchedulable(minShare,
|
||||
Resources.createResource(Integer.MAX_VALUE, Integer.MAX_VALUE),
|
||||
weights, Resources.none(), usage, 0l);
|
||||
}
|
||||
|
||||
@Test
|
||||
|
|
|
|||
|
|
@ -257,3 +257,39 @@ Allocation file format
|
|||
---
|
||||
|
||||
Note that for backwards compatibility with the original FairScheduler, "queue" elements can instead be named as "pool" elements.
|
||||
|
||||
* {Administration}
|
||||
|
||||
The fair scheduler provides support for administration at runtime through two mechanisms:
|
||||
|
||||
* It is possible to modify minimum shares, limits, weights, preemption timeouts
|
||||
and queue scheduling policies at runtime by editing the allocation file. The
|
||||
scheduler will reload this file 10-15 seconds after it sees that it was
|
||||
modified.
|
||||
|
||||
* Current applications, queues, and fair shares can be examined through the
|
||||
ResourceManager's web interface, at
|
||||
http://<ResourceManager URL>/cluster/scheduler.
|
||||
|
||||
The following fields can be seen for each queue on the web interface:
|
||||
|
||||
* Used Resources - The sum of resources allocated to containers within the queue.
|
||||
|
||||
* Num Active Applications - The number of applications in the queue that have
|
||||
received at least one container.
|
||||
|
||||
* Num Pending Applications - The number of applications in the queue that have
|
||||
not yet received any containers.
|
||||
|
||||
* Min Resources - The configured minimum resources that are guaranteed to the queue.
|
||||
|
||||
* Max Resources - The configured maximum resources that are allowed to the queue.
|
||||
|
||||
* Fair Share - The queue's fair share of resources. Queues may be allocated
|
||||
resources beyond their fair share when other queues aren't using them. A
|
||||
queue whose resource consumption lies at or below its fair share will never
|
||||
have its containers preempted.
|
||||
|
||||
In addition to the information that the ResourceManager normally displays
|
||||
about each application, the web interface includes the application's fair share.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue