DRF算法主要目的是按按照主资源进行公平分配
考虑一个有9个cpu和18GB的系统，有两个用户：用户A的每个任务都请求（1CPU，4GB）资源；用户B的每个任务都请求（3CPU，1GB）资源。如何为这种情况构建一个公平分配策略？
假设A任务分配X个实例，B任务分配Y个实例，有
A任务每个实例需要的cup占总资源的比例为1/9，内存占总资源的比例为4/18，4/18>1/9,所以A的主资源为内存，同理可得B的主资源为CPU，现在让A任务分配的主资源内存和B任务分配的主资源CPU 公平，则由：
通过三个方程，解得X=3，Y=2
即A类任务可以启动3个 ，占用资源为(3,12)
B类任务可以启动2个，占用资源为(6,2)
hadoop对DRF的应用
hadoop 2.7 中使用DominantResourceCalculator实现了cup、memory这两种主资源的公平调度
代码如下
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* to you under the Apache License, Version 2.0 (the
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*
* http://www.apache.org/licenses/LICENSE-2.0
*
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package org.apache.hadoop.yarn.util.resource;
import org.apache.hadoop.classification.InterfaceAudience.Private;
import org.apache.hadoop.classification.InterfaceStability.Unstable;
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
*/
@Private
@Unstable
public class DominantResourceCalculator extends ResourceCalculator {
@Override
public int compare(Resource clusterResource, Resource lhs, Resource rhs) {
if (lhs.equals(rhs)) {
return 0;
}
if (isInvalidDivisor(clusterResource)) {
//除数为0，cup和memory一大一小
if ((lhs.getMemory() < rhs.getMemory() && lhs.getVirtualCores() > rhs
.getVirtualCores())
|| (lhs.getMemory() > rhs.getMemory() && lhs.getVirtualCores() < rhs
.getVirtualCores())) {
return 0;
} else if (lhs.getMemory() > rhs.getMemory()
|| lhs.getVirtualCores() > rhs.getVirtualCores()) {
return 1;
} else if (lhs.getMemory() < rhs.getMemory()
|| lhs.getVirtualCores() < rhs.getVirtualCores()) {
return -1;
}
}
//比较主资源
float l = getResourceAsValue(clusterResource, lhs, true);
float r = getResourceAsValue(clusterResource, rhs, true);
if (l < r) {
return -1;
} else if (l > r) {
return 1;
} else {
//主资源相等，比较副资源占比
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 boolean isInvalidDivisor(Resource r) {
if (r.getMemory() == 0.0f || r.getVirtualCores() == 0.0f) {
return true;
}
return false;
}
@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()
)
);
}
}
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