| /** |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| package org.apache.hadoop.yarn.server.resourcemanager.resource; |
| |
| import org.apache.hadoop.yarn.api.records.Resource; |
| |
| /** |
| * A {@link ResourceCalculator} which uses the concept of |
| * <em>dominant resource</em> to compare multi-dimensional resources. |
| * |
| * Essentially the idea is that the in a multi-resource environment, |
| * the resource allocation should be determined by the dominant share |
| * of an entity (user or queue), which is the maximum share that the |
| * entity has been allocated of any resource. |
| * |
| * In a nutshell, it seeks to maximize the minimum dominant share across |
| * all entities. |
| * |
| * For example, if user A runs CPU-heavy tasks and user B runs |
| * memory-heavy tasks, it attempts to equalize CPU share of user A |
| * with Memory-share of user B. |
| * |
| * In the single resource case, it reduces to max-min fairness for that resource. |
| * |
| * See the Dominant Resource Fairness paper for more details: |
| * www.cs.berkeley.edu/~matei/papers/2011/nsdi_drf.pdf |
| */ |
| public class DominantResourceCalculator extends ResourceCalculator { |
| |
| @Override |
| public int compare(Resource clusterResource, Resource lhs, Resource rhs) { |
| |
| if (lhs.equals(rhs)) { |
| return 0; |
| } |
| |
| float l = getResourceAsValue(clusterResource, lhs, true); |
| float r = getResourceAsValue(clusterResource, rhs, true); |
| |
| if (l < r) { |
| return -1; |
| } else if (l > r) { |
| return 1; |
| } else { |
| l = getResourceAsValue(clusterResource, lhs, false); |
| r = getResourceAsValue(clusterResource, rhs, false); |
| if (l < r) { |
| return -1; |
| } else if (l > r) { |
| return 1; |
| } |
| } |
| |
| return 0; |
| } |
| |
| /** |
| * Use 'dominant' for now since we only have 2 resources - gives us a slight |
| * performance boost. |
| * |
| * Once we add more resources, we'll need a more complicated (and slightly |
| * less performant algorithm). |
| */ |
| protected float getResourceAsValue( |
| Resource clusterResource, Resource resource, boolean dominant) { |
| // Just use 'dominant' resource |
| return (dominant) ? |
| Math.max( |
| (float)resource.getMemory() / clusterResource.getMemory(), |
| (float)resource.getVirtualCores() / clusterResource.getVirtualCores() |
| ) |
| : |
| Math.min( |
| (float)resource.getMemory() / clusterResource.getMemory(), |
| (float)resource.getVirtualCores() / clusterResource.getVirtualCores() |
| ); |
| } |
| |
| @Override |
| public int computeAvailableContainers(Resource available, Resource required) { |
| return Math.min( |
| available.getMemory() / required.getMemory(), |
| available.getVirtualCores() / required.getVirtualCores()); |
| } |
| |
| @Override |
| public float divide(Resource clusterResource, |
| Resource numerator, Resource denominator) { |
| return |
| getResourceAsValue(clusterResource, numerator, true) / |
| getResourceAsValue(clusterResource, denominator, true); |
| } |
| |
| @Override |
| public float ratio(Resource a, Resource b) { |
| return Math.max( |
| (float)a.getMemory()/b.getMemory(), |
| (float)a.getVirtualCores()/b.getVirtualCores() |
| ); |
| } |
| |
| @Override |
| public Resource divideAndCeil(Resource numerator, int denominator) { |
| return Resources.createResource( |
| divideAndCeil(numerator.getMemory(), denominator), |
| divideAndCeil(numerator.getVirtualCores(), denominator) |
| ); |
| } |
| |
| @Override |
| public Resource normalize(Resource r, Resource minimumResource, |
| Resource maximumResource, Resource stepFactor) { |
| int normalizedMemory = Math.min( |
| roundUp( |
| Math.max(r.getMemory(), minimumResource.getMemory()), |
| stepFactor.getMemory()), |
| maximumResource.getMemory()); |
| int normalizedCores = Math.min( |
| roundUp( |
| Math.max(r.getVirtualCores(), minimumResource.getVirtualCores()), |
| stepFactor.getVirtualCores()), |
| maximumResource.getVirtualCores()); |
| return Resources.createResource(normalizedMemory, |
| normalizedCores); |
| } |
| |
| @Override |
| public Resource roundUp(Resource r, Resource stepFactor) { |
| return Resources.createResource( |
| roundUp(r.getMemory(), stepFactor.getMemory()), |
| roundUp(r.getVirtualCores(), stepFactor.getVirtualCores()) |
| ); |
| } |
| |
| @Override |
| public Resource roundDown(Resource r, Resource stepFactor) { |
| return Resources.createResource( |
| roundDown(r.getMemory(), stepFactor.getMemory()), |
| roundDown(r.getVirtualCores(), stepFactor.getVirtualCores()) |
| ); |
| } |
| |
| @Override |
| public Resource multiplyAndNormalizeUp(Resource r, double by, |
| Resource stepFactor) { |
| return Resources.createResource( |
| roundUp( |
| (int)Math.ceil(r.getMemory() * by), stepFactor.getMemory()), |
| roundUp( |
| (int)Math.ceil(r.getVirtualCores() * by), |
| stepFactor.getVirtualCores()) |
| ); |
| } |
| |
| @Override |
| public Resource multiplyAndNormalizeDown(Resource r, double by, |
| Resource stepFactor) { |
| return Resources.createResource( |
| roundDown( |
| (int)(r.getMemory() * by), |
| stepFactor.getMemory() |
| ), |
| roundDown( |
| (int)(r.getVirtualCores() * by), |
| stepFactor.getVirtualCores() |
| ) |
| ); |
| } |
| |
| } |