/**
* 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.hbase.master.balancer;
import org.apache.commons.lang.mutable.MutableInt;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.commons.math.stat.descriptive.DescriptiveStatistics;
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.ClusterStatus;
import org.apache.hadoop.hbase.HRegionInfo;
import org.apache.hadoop.hbase.ServerLoad;
import org.apache.hadoop.hbase.RegionLoad;
import org.apache.hadoop.hbase.ServerName;
import org.apache.hadoop.hbase.master.MasterServices;
import org.apache.hadoop.hbase.master.RegionPlan;
import org.apache.hadoop.hbase.util.Bytes;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Random;
/**
* <p>This is a best effort load balancer. Given a Cost function F(C) => x It will
* randomly try and mutate the cluster to Cprime. If F(Cprime) < F(C) then the
* new cluster state becomes the plan. It includes costs functions to compute the cost of:</p>
* <ul>
* <li>Region Load</li>
* <li>Table Load</li>
* <li>Data Locality</li>
* <li>Memstore Sizes</li>
* <li>Storefile Sizes</li>
* </ul>
*
*
* <p>Every cost function returns a number between 0 and 1 inclusive; where 0 is the lowest cost
* best solution, and 1 is the highest possible cost and the worst solution. The computed costs are
* scaled by their respective multipliers:</p>
*
* <ul>
* <li>hbase.master.balancer.stochastic.regionLoadCost</li>
* <li>hbase.master.balancer.stochastic.moveCost</li>
* <li>hbase.master.balancer.stochastic.tableLoadCost</li>
* <li>hbase.master.balancer.stochastic.localityCost</li>
* <li>hbase.master.balancer.stochastic.memstoreSizeCost</li>
* <li>hbase.master.balancer.stochastic.storefileSizeCost</li>
* </ul>
*
* <p>In addition to the above configurations, the balancer can be tuned by the following
* configuration values:</p>
* <ul>
* <li>hbase.master.balancer.stochastic.maxMoveRegions which
* controls what the max number of regions that can be moved in a single invocation of this
* balancer.</li>
* <li>hbase.master.balancer.stochastic.stepsPerRegion is the coefficient by which the number of
* regions is multiplied to try and get the number of times the balancer will
* mutate all servers.</li>
* <li>hbase.master.balancer.stochastic.maxSteps which controls the maximum number of times that
* the balancer will try and mutate all the servers. The balancer will use the minimum of this
* value and the above computation.</li>
* </ul>
*
* <p>This balancer is best used with hbase.master.loadbalance.bytable set to false
* so that the balancer gets the full picture of all loads on the cluster.</p>
*/
@InterfaceAudience.Private
public class StochasticLoadBalancer extends BaseLoadBalancer {
private static final String STOREFILE_SIZE_COST_KEY =
"hbase.master.balancer.stochastic.storefileSizeCost";
private static final String MEMSTORE_SIZE_COST_KEY =
"hbase.master.balancer.stochastic.memstoreSizeCost";
private static final String WRITE_REQUEST_COST_KEY =
"hbase.master.balancer.stochastic.writeRequestCost";
private static final String READ_REQUEST_COST_KEY =
"hbase.master.balancer.stochastic.readRequestCost";
private static final String LOCALITY_COST_KEY = "hbase.master.balancer.stochastic.localityCost";
private static final String TABLE_LOAD_COST_KEY =
"hbase.master.balancer.stochastic.tableLoadCost";
private static final String MOVE_COST_KEY = "hbase.master.balancer.stochastic.moveCost";
private static final String REGION_LOAD_COST_KEY =
"hbase.master.balancer.stochastic.regionLoadCost";
private static final String STEPS_PER_REGION_KEY =
"hbase.master.balancer.stochastic.stepsPerRegion";
private static final String MAX_STEPS_KEY = "hbase.master.balancer.stochastic.maxSteps";
private static final String MAX_MOVES_KEY = "hbase.master.balancer.stochastic.maxMoveRegions";
private static final String KEEP_REGION_LOADS = "hbase.master.balancer.stochastic.numRegionLoadsToRemember";
private static final Random RANDOM = new Random(System.currentTimeMillis());
private static final Log LOG = LogFactory.getLog(StochasticLoadBalancer.class);
private final RegionLocationFinder regionFinder = new RegionLocationFinder();
private ClusterStatus clusterStatus = null;
private Map<String, List<RegionLoad>> loads = new HashMap<String, List<RegionLoad>>();
// values are defaults
private int maxSteps = 15000;
private int stepsPerRegion = 110;
private int maxMoves = 600;
private int numRegionLoadsToRemember = 15;
private float loadMultiplier = 55;
private float moveCostMultiplier = 5;
private float tableMultiplier = 5;
private float localityMultiplier = 5;
private float readRequestMultiplier = 0;
private float writeRequestMultiplier = 0;
private float memStoreSizeMultiplier = 5;
private float storeFileSizeMultiplier = 5;
@Override
public void setConf(Configuration conf) {
super.setConf(conf);
regionFinder.setConf(conf);
maxSteps = conf.getInt(MAX_STEPS_KEY, maxSteps);
maxMoves = conf.getInt(MAX_MOVES_KEY, maxMoves);
stepsPerRegion = conf.getInt(STEPS_PER_REGION_KEY, stepsPerRegion);
numRegionLoadsToRemember = conf.getInt(KEEP_REGION_LOADS, numRegionLoadsToRemember);
// Load multiplier should be the greatest as it is the most general way to balance data.
loadMultiplier = conf.getFloat(REGION_LOAD_COST_KEY, loadMultiplier);
// Move cost multiplier should be the same cost or higer than the rest of the costs to ensure
// that two costs must get better to justify a move cost.
moveCostMultiplier = conf.getFloat(MOVE_COST_KEY, moveCostMultiplier);
// These are the added costs so that the stochastic load balancer can get a little bit smarter
// about where to move regions.
tableMultiplier = conf.getFloat(TABLE_LOAD_COST_KEY, tableMultiplier);
localityMultiplier = conf.getFloat(LOCALITY_COST_KEY, localityMultiplier);
memStoreSizeMultiplier = conf.getFloat(MEMSTORE_SIZE_COST_KEY, memStoreSizeMultiplier);
storeFileSizeMultiplier = conf.getFloat(STOREFILE_SIZE_COST_KEY, storeFileSizeMultiplier);
readRequestMultiplier = conf.getFloat(READ_REQUEST_COST_KEY, readRequestMultiplier);
writeRequestMultiplier = conf.getFloat(WRITE_REQUEST_COST_KEY, writeRequestMultiplier);
}
@Override
public void setClusterStatus(ClusterStatus st) {
super.setClusterStatus(st);
regionFinder.setClusterStatus(st);
this.clusterStatus = st;
updateRegionLoad();
}
@Override
public void setMasterServices(MasterServices masterServices) {
super.setMasterServices(masterServices);
this.services = masterServices;
this.regionFinder.setServices(masterServices);
}
/**
* Given the cluster state this will try and approach an optimal balance. This
* should always approach the optimal state given enough steps.
*/
@Override
public List<RegionPlan> balanceCluster(Map<ServerName, List<HRegionInfo>> clusterState) {
// No need to balance a one node cluster.
if (clusterState.size() <= 1) {
LOG.debug("Skipping load balance as cluster has only one node.");
return null;
}
long startTime = System.currentTimeMillis();
// Keep track of servers to iterate through them.
List<ServerName> servers = new ArrayList<ServerName>(clusterState.keySet());
Map<HRegionInfo, ServerName> initialRegionMapping = createRegionMapping(clusterState);
double currentCost, newCost, initCost;
currentCost = newCost = initCost = computeCost(initialRegionMapping, clusterState);
int computedMaxSteps =
Math.min(this.maxSteps, (initialRegionMapping.size() * this.stepsPerRegion));
// Perform a stochastic walk to see if we can get a good fit.
for (int step = 0; step < computedMaxSteps; step++) {
// try and perform a mutation
for (ServerName leftServer : servers) {
// What server are we going to be swapping regions with ?
ServerName rightServer = pickOtherServer(leftServer, servers);
if (rightServer == null) {
continue;
}
// Get the regions.
List<HRegionInfo> leftRegionList = clusterState.get(leftServer);
List<HRegionInfo> rightRegionList = clusterState.get(rightServer);
// Pick what regions to swap around.
// If we get a null for one then this isn't a swap just a move
HRegionInfo lRegion = pickRandomRegion(leftRegionList, 0);
HRegionInfo rRegion = pickRandomRegion(rightRegionList, 0.5);
// We randomly picked to do nothing.
if (lRegion == null && rRegion == null) {
continue;
}
if (rRegion != null) {
leftRegionList.add(rRegion);
}
if (lRegion != null) {
rightRegionList.add(lRegion);
}
newCost = computeCost(initialRegionMapping, clusterState);
// Should this be kept?
if (newCost < currentCost) {
currentCost = newCost;
} else {
// Put things back the way they were before.
if (rRegion != null) {
leftRegionList.remove(rRegion);
rightRegionList.add(rRegion);
}
if (lRegion != null) {
rightRegionList.remove(lRegion);
leftRegionList.add(lRegion);
}
}
}
}
long endTime = System.currentTimeMillis();
if (initCost > currentCost) {
List<RegionPlan> plans = createRegionPlans(initialRegionMapping, clusterState);
LOG.debug("Finished computing new laod balance plan. Computation took "
+ (endTime - startTime) + "ms to try " + computedMaxSteps
+ " different iterations. Found a solution that moves " + plans.size()
+ " regions; Going from a computed cost of " + initCost + " to a new cost of "
+ currentCost);
return plans;
}
LOG.debug("Could not find a better load balance plan. Tried " + computedMaxSteps
+ " different configurations in " + (endTime - startTime)
+ "ms, and did not find anything with a computed cost less than " + initCost);
return null;
}
/**
* Create all of the RegionPlan's needed to move from the initial cluster state to the desired
* state.
*
* @param initialRegionMapping Initial mapping of Region to Server
* @param clusterState The desired mapping of ServerName to Regions
* @return List of RegionPlan's that represent the moves needed to get to desired final state.
*/
private List<RegionPlan> createRegionPlans(Map<HRegionInfo, ServerName> initialRegionMapping,
Map<ServerName, List<HRegionInfo>> clusterState) {
List<RegionPlan> plans = new LinkedList<RegionPlan>();
for (Entry<ServerName, List<HRegionInfo>> entry : clusterState.entrySet()) {
ServerName newServer = entry.getKey();
for (HRegionInfo region : entry.getValue()) {
ServerName initialServer = initialRegionMapping.get(region);
if (!newServer.equals(initialServer)) {
LOG.trace("Moving Region " + region.getEncodedName() + " from server "
+ initialServer.getHostname() + " to " + newServer.getHostname());
RegionPlan rp = new RegionPlan(region, initialServer, newServer);
plans.add(rp);
}
}
}
return plans;
}
/**
* Create a map that will represent the initial location of regions on a
* {@link ServerName}
*
* @param clusterState starting state of the cluster and regions.
* @return A map of {@link HRegionInfo} to the {@link ServerName} that is
* currently hosting that region
*/
private Map<HRegionInfo, ServerName> createRegionMapping(
Map<ServerName, List<HRegionInfo>> clusterState) {
Map<HRegionInfo, ServerName> mapping = new HashMap<HRegionInfo, ServerName>();
for (Entry<ServerName, List<HRegionInfo>> entry : clusterState.entrySet()) {
for (HRegionInfo region : entry.getValue()) {
mapping.put(region, entry.getKey());
}
}
return mapping;
}
/** Store the current region loads. */
private synchronized void updateRegionLoad() {
//We create a new hashmap so that regions that are no longer there are removed.
//However we temporarily need the old loads so we can use them to keep the rolling average.
Map<String, List<RegionLoad>> oldLoads = loads;
loads = new HashMap<String, List<RegionLoad>>();
for (ServerName sn : clusterStatus.getServers()) {
ServerLoad sl = clusterStatus.getLoad(sn);
if (sl == null) continue;
for (Entry<byte[], RegionLoad> entry : sl.getRegionsLoad().entrySet()) {
List<RegionLoad> rLoads = oldLoads.get(Bytes.toString(entry.getKey()));
if (rLoads != null) {
//We're only going to keep 15. So if there are that many already take the last 14
if (rLoads.size() >= numRegionLoadsToRemember) {
int numToRemove = 1 + (rLoads.size() - numRegionLoadsToRemember);
rLoads = rLoads.subList(numToRemove, rLoads.size());
}
} else {
//There was nothing there
rLoads = new ArrayList<RegionLoad>();
}
rLoads.add(entry.getValue());
loads.put(Bytes.toString(entry.getKey()), rLoads);
}
}
}
/**
* From a list of regions pick a random one. Null can be returned which
* {@link StochasticLoadBalancer#balanceCluster(Map)} recognize as signal to try a region move
* rather than swap.
*
* @param regions list of regions.
* @param chanceOfNoSwap Chance that this will decide to try a move rather
* than a swap.
* @return a random {@link HRegionInfo} or null if an asymmetrical move is
* suggested.
*/
private HRegionInfo pickRandomRegion(List<HRegionInfo> regions, double chanceOfNoSwap) {
//Check to see if this is just a move.
if (regions.isEmpty() || RANDOM.nextFloat() < chanceOfNoSwap) {
//signal a move only.
return null;
}
int count = 0;
HRegionInfo r = null;
//We will try and find a region up to 10 times. If we always
while (count < 10 && r == null ) {
count++;
r = regions.get(RANDOM.nextInt(regions.size()));
// If this is a special region we always try not to move it.
// so clear out r. try again
if (r.isMetaRegion()) {
r = null;
}
}
if (r != null) {
regions.remove(r);
}
return r;
}
/**
* Given a server we will want to switch regions with another server. This
* function picks a random server from the list.
*
* @param server Current Server. This server will never be the return value.
* @param allServers list of all server from which to pick
* @return random server. Null if no other servers were found.
*/
private ServerName pickOtherServer(ServerName server, List<ServerName> allServers) {
ServerName s = null;
int count = 0;
while (count < 100 && (s == null || s.equals(server))) {
count++;
s = allServers.get(RANDOM.nextInt(allServers.size()));
}
// If nothing but the current server was found return null.
return (s == null || s.equals(server)) ? null : s;
}
/**
* This is the main cost function. It will compute a cost associated with a proposed cluster
* state. All different costs will be combined with their multipliers to produce a double cost.
*
* @param initialRegionMapping Map of where the regions started.
* @param clusterState Map of ServerName to list of regions.
* @return a double of a cost associated with the proposed
*/
protected double computeCost(Map<HRegionInfo, ServerName> initialRegionMapping,
Map<ServerName, List<HRegionInfo>> clusterState) {
double moveCost = moveCostMultiplier * computeMoveCost(initialRegionMapping, clusterState);
double regionCountSkewCost = loadMultiplier * computeSkewLoadCost(clusterState);
double tableSkewCost = tableMultiplier * computeTableSkewLoadCost(clusterState);
double localityCost =
localityMultiplier * computeDataLocalityCost(initialRegionMapping, clusterState);
double memstoreSizeCost =
memStoreSizeMultiplier
* computeRegionLoadCost(clusterState, RegionLoadCostType.MEMSTORE_SIZE);
double storefileSizeCost =
storeFileSizeMultiplier
* computeRegionLoadCost(clusterState, RegionLoadCostType.STOREFILE_SIZE);
double readRequestCost =
readRequestMultiplier
* computeRegionLoadCost(clusterState, RegionLoadCostType.READ_REQUEST);
double writeRequestCost =
writeRequestMultiplier
* computeRegionLoadCost(clusterState, RegionLoadCostType.WRITE_REQUEST);
double total =
moveCost + regionCountSkewCost + tableSkewCost + localityCost + memstoreSizeCost
+ storefileSizeCost + readRequestCost + writeRequestCost;
LOG.trace("Computed weights for a potential balancing total = " + total + " moveCost = "
+ moveCost + " regionCountSkewCost = " + regionCountSkewCost + " tableSkewCost = "
+ tableSkewCost + " localityCost = " + localityCost + " memstoreSizeCost = "
+ memstoreSizeCost + " storefileSizeCost = " + storefileSizeCost);
return total;
}
/**
* Given the starting state of the regions and a potential ending state
* compute cost based upon the number of regions that have moved.
*
* @param initialRegionMapping The starting location of regions.
* @param clusterState The potential new cluster state.
* @return The cost. Between 0 and 1.
*/
double computeMoveCost(Map<HRegionInfo, ServerName> initialRegionMapping,
Map<ServerName, List<HRegionInfo>> clusterState) {
float moveCost = 0;
for (Entry<ServerName, List<HRegionInfo>> entry : clusterState.entrySet()) {
for (HRegionInfo region : entry.getValue()) {
if (initialRegionMapping.get(region) != entry.getKey()) {
moveCost += 1;
}
}
}
//Don't let this single balance move more than the max moves.
//This allows better scaling to accurately represent the actual cost of a move.
if (moveCost > maxMoves) {
return 10000; //return a number much greater than any of the other cost functions
}
return scale(0, Math.min(maxMoves, initialRegionMapping.size()), moveCost);
}
/**
* Compute the cost of a potential cluster state from skew in number of
* regions on a cluster
*
* @param clusterState The proposed cluster state
* @return The cost of region load imbalance.
*/
double computeSkewLoadCost(Map<ServerName, List<HRegionInfo>> clusterState) {
DescriptiveStatistics stats = new DescriptiveStatistics();
for (List<HRegionInfo> regions : clusterState.values()) {
int size = regions.size();
stats.addValue(size);
}
return costFromStats(stats);
}
/**
* Compute the cost of a potential cluster configuration based upon how evenly
* distributed tables are.
*
* @param clusterState Proposed cluster state.
* @return Cost of imbalance in table.
*/
double computeTableSkewLoadCost(Map<ServerName, List<HRegionInfo>> clusterState) {
Map<String, MutableInt> tableRegionsTotal = new HashMap<String, MutableInt>();
Map<String, MutableInt> tableRegionsOnCurrentServer = new HashMap<String, MutableInt>();
Map<String, Integer> tableCostSeenSoFar = new HashMap<String, Integer>();
// Go through everything per server
for (Entry<ServerName, List<HRegionInfo>> entry : clusterState.entrySet()) {
tableRegionsOnCurrentServer.clear();
// For all of the regions count how many are from each table
for (HRegionInfo region : entry.getValue()) {
String tableName = region.getTableNameAsString();
// See if this table already has a count on this server
MutableInt regionsOnServerCount = tableRegionsOnCurrentServer.get(tableName);
// If this is the first time we've seen this table on this server
// create a new mutable int.
if (regionsOnServerCount == null) {
regionsOnServerCount = new MutableInt(0);
tableRegionsOnCurrentServer.put(tableName, regionsOnServerCount);
}
// Increment the count of how many regions from this table are host on
// this server
regionsOnServerCount.increment();
// Now count the number of regions in this table.
MutableInt totalCount = tableRegionsTotal.get(tableName);
// If this is the first region from this table create a new counter for
// this table.
if (totalCount == null) {
totalCount = new MutableInt(0);
tableRegionsTotal.put(tableName, totalCount);
}
totalCount.increment();
}
// Now go through all of the tables we have seen and keep the max number
// of regions of this table a single region server is hosting.
for (Entry<String, MutableInt> currentServerEntry: tableRegionsOnCurrentServer.entrySet()) {
String tableName = currentServerEntry.getKey();
Integer thisCount = currentServerEntry.getValue().toInteger();
Integer maxCountSoFar = tableCostSeenSoFar.get(tableName);
if (maxCountSoFar == null || thisCount.compareTo(maxCountSoFar) > 0) {
tableCostSeenSoFar.put(tableName, thisCount);
}
}
}
double max = 0;
double min = 0;
double value = 0;
// Compute the min, value, and max.
for (Entry<String, MutableInt> currentEntry : tableRegionsTotal.entrySet()) {
max += tableRegionsTotal.get(currentEntry.getKey()).doubleValue();
min += tableRegionsTotal.get(currentEntry.getKey()).doubleValue() / clusterState.size();
value += tableCostSeenSoFar.get(currentEntry.getKey()).doubleValue();
}
return scale(min, max, value);
}
/**
* Compute a cost of a potential cluster configuration based upon where
* {@link org.apache.hadoop.hbase.regionserver.StoreFile}s are located.
*
* @param initialRegionMapping - not used
* @param clusterState The state of the cluster
* @return A cost between 0 and 1. 0 Means all regions are on the sever with
* the most local store files.
*/
double computeDataLocalityCost(Map<HRegionInfo, ServerName> initialRegionMapping,
Map<ServerName, List<HRegionInfo>> clusterState) {
double max = 0;
double cost = 0;
// If there's no master so there's no way anything else works.
if (this.services == null) return cost;
for (Entry<ServerName, List<HRegionInfo>> entry : clusterState.entrySet()) {
ServerName sn = entry.getKey();
for (HRegionInfo region : entry.getValue()) {
max += 1;
List<ServerName> dataOnServers = regionFinder.getTopBlockLocations(region);
// If we can't find where the data is getTopBlock returns null.
// so count that as being the best possible.
if (dataOnServers == null) {
continue;
}
int index = dataOnServers.indexOf(sn);
if (index < 0) {
cost += 1;
} else {
cost += (double) index / (double) dataOnServers.size();
}
}
}
return scale(0, max, cost);
}
/** The cost's that can be derived from RegionLoad */
private enum RegionLoadCostType {
READ_REQUEST, WRITE_REQUEST, MEMSTORE_SIZE, STOREFILE_SIZE
}
/**
* Compute the cost of the current cluster state due to some RegionLoadCost type
*
* @param clusterState the cluster
* @param costType what type of cost to consider
* @return the scaled cost.
*/
private double computeRegionLoadCost(Map<ServerName, List<HRegionInfo>> clusterState,
RegionLoadCostType costType) {
if (this.clusterStatus == null || this.loads == null || this.loads.size() == 0) return 0;
DescriptiveStatistics stats = new DescriptiveStatistics();
// For every server look at the cost of each region
for (List<HRegionInfo> regions : clusterState.values()) {
long cost = 0; //Cost this server has from RegionLoad
// For each region
for (HRegionInfo region : regions) {
// Try and get the region using the regionNameAsString
List<RegionLoad> rl = loads.get(region.getRegionNameAsString());
// That could have failed if the RegionLoad is using the other regionName
if (rl == null) {
// Try getting the region load using encoded name.
rl = loads.get(region.getEncodedName());
}
// Now if we found a region load get the type of cost that was requested.
if (rl != null) {
cost += getRegionLoadCost(rl, costType);
}
}
// Add the total cost to the stats.
stats.addValue(cost);
}
// No return the scaled cost from data held in the stats object.
return costFromStats(stats);
}
/**
* Get the un-scaled cost from a RegionLoad
*
* @param regionLoadList the Region load List
* @param type The type of cost to extract
* @return the double representing the cost
*/
private double getRegionLoadCost(List<RegionLoad> regionLoadList, RegionLoadCostType type) {
double cost = 0;
int size = regionLoadList.size();
for(int i =0; i< size; i++) {
RegionLoad rl = regionLoadList.get(i);
double toAdd = 0;
switch (type) {
case READ_REQUEST:
toAdd = rl.getReadRequestsCount();
break;
case WRITE_REQUEST:
toAdd = rl.getWriteRequestsCount();
break;
case MEMSTORE_SIZE:
toAdd = rl.getMemStoreSizeMB();
break;
case STOREFILE_SIZE:
toAdd = rl.getStorefileSizeMB();
break;
default:
assert false : "RegionLoad cost type not supported.";
return 0;
}
if (cost == 0) {
cost = toAdd;
} else {
cost = (.5 * cost) + (.5 * toAdd);
}
}
return cost;
}
/**
* Function to compute a scaled cost using {@link DescriptiveStatistics}. It
* assumes that this is a zero sum set of costs. It assumes that the worst case
* possible is all of the elements in one region server and the rest having 0.
*
* @param stats the costs
* @return a scaled set of costs.
*/
double costFromStats(DescriptiveStatistics stats) {
double totalCost = 0;
double mean = stats.getMean();
//Compute max as if all region servers had 0 and one had the sum of all costs. This must be
// a zero sum cost for this to make sense.
double max = ((stats.getN() - 1) * stats.getMean()) + (stats.getSum() - stats.getMean());
for (double n : stats.getValues()) {
totalCost += Math.abs(mean - n);
}
return scale(0, max, totalCost);
}
/**
* Scale the value between 0 and 1.
*
* @param min Min value
* @param max The Max value
* @param value The value to be scaled.
* @return The scaled value.
*/
private double scale(double min, double max, double value) {
if (max == 0 || value == 0) {
return 0;
}
return Math.max(0d, Math.min(1d, (value - min) / max));
}
}