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# Resource optimization: Quick start with Drools Planner

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# Resource optimization: Quick start with Drools Planner

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## Cloud balancing tutorial

### 1. Problem statement

Suppose your company owns a number of cloud computers and needs to run a number of processes on those computers. Assign each process to a computer under the following 4 constraints.

Hard constraints which must be fulfilled:

• Every computer must be able to handle the minimal hardware requirements of the sum of its processes:

• The CPU power of a computer must be at least the sum of the CPU power required by the processes assigned to that computer.

• The RAM memory of a computer must be at least the sum of the RAM memory required by the processes assigned to that computer.

• The network bandwidth of a computer must be at least the sum of the network bandwidth required by the processes assigned to that computer.

Soft constraints which should be optimized:

• Each computer that has one or more processes assigned, incurs a maintenance cost (which is fixed per computer).

• Minimize the total maintenance cost.

How would you do that? This problem is a form of bin packing. Here's a simplified example where we assign 4 processes to 2 computers with 2 constraints (CPU and RAM) with a simple algorithm:

The simple algorithm used here is the First Fit Decreasing algorithm, which assigns the bigger processes first and assigns the smaller processes to the remaining space. As you can see, it's not optimal, because it does not leave enough room to assign the yellow process D.

Drools Planner does find the more optimal solution fast, by using additional, smarter algorithms. And it scales too: both in data (more processes, more computers) and constraints (more hardware requirements, other constraints). So let's take a look how we can use Planner for this.

### 2. Domain model diagram

Let's start by taking a look at the domain model. It's pretty simple:

• Computer: represents a computer with certain hardware (CPU power, RAM memory, network bandwidth) and maintenance cost.

• Process: represents a process with a demand. Needs to be assigned to a Computer by Drools Planner.

• CloudBalance: represents a problem. Contains every Computer and Process for a certain data set.

In the UML class diagram above, the Planner concepts are already annotated:

• Planning entity: the class (or classes) that changes during planning. In this example that's the class Process.

• Planning variable: the property (or properties) of a planning entity class that changes during planning. In this examples, that's the property computer on the class Process.

• Solution: the class that represents a data set and contains all planning entities. In this example that's the class CloudBalance.

### 3. Main method

Try it yourself. Download and configure the examples in your favorite IDE. Run org.drools.planner.examples.cloudbalancing.app.CloudBalancingHelloWorld. By default, it is configured to run for 120 seconds. It will execute this code:

CloudBalancingHelloWorld.java

```public class CloudBalancingHelloWorld {

public static void main(String[] args) {
// Build the Solver
SolverFactory solverFactory = new XmlSolverFactory(
"/org/drools/planner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
Solver solver = solverFactory.buildSolver();

// Load a problem with 400 computers and 1200 processes
CloudBalance unsolvedCloudBalance = new CloudBalancingGenerator().createCloudBalance(400, 1200);

// Solve the problem
solver.setPlanningProblem(unsolvedCloudBalance);
solver.solve();
CloudBalance solvedCloudBalance = (CloudBalance) solver.getBestSolution();

// Display the result
System.out.println("\nSolved cloudBalance with 400 computers and 1200 processes:\n"
+ toDisplayString(solvedCloudBalance));
}

...

}```

The code above does this:

• Build the Solver based on a solver configuration (in this case an XML file).

```        SolverFactory solverFactory = new XmlSolverFactory(
"/org/drools/planner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
Solver solver = solverFactory.buildSolver();```
• Load the problem. CloudBalancingGenerator generates a random problem: you'll replace this with a class that loads a real problem, for example from a database.

`        CloudBalance unsolvedCloudBalance = new CloudBalancingGenerator().createCloudBalance(400, 1200);`
• Solve the problem.

```        solver.setPlanningProblem(unsolvedCloudBalance);
solver.solve();
CloudBalance solvedCloudBalance = (CloudBalance) solver.getBestSolution();```
• Display the result.

```        System.out.println("\nSolved cloudBalance with 400 computers and 1200 processes:\n"
+ toDisplayString(solvedCloudBalance));```

The only non-obvious part is building the Solver. Let's examine that.

### 4. Solver configuration

Take a look at the solver configuration:

cloudBalancingSolverConfig.xml

```<?xml version="1.0" encoding="UTF-8"?>
<solver>
<!--<environmentMode>DEBUG</environmentMode>-->

<!-- Domain model configuration -->
<solutionClass>org.drools.planner.examples.cloudbalancing.domain.CloudBalance</solutionClass>
<planningEntityClass>org.drools.planner.examples.cloudbalancing.domain.CloudProcess</planningEntityClass>

<!-- Score configuration -->
<scoreDirectorFactory>
<scoreDefinitionType>HARD_AND_SOFT</scoreDefinitionType>
<simpleScoreCalculatorClass>org.drools.planner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>
<!--<scoreDrl>/org/drools/planner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
</scoreDirectorFactory>

<!-- Optimization algorithms configuration -->
<termination>
<maximumSecondsSpend>120</maximumSecondsSpend>
</termination>
<constructionHeuristic>
<constructionHeuristicType>FIRST_FIT_DECREASING</constructionHeuristicType>
<constructionHeuristicPickEarlyType>FIRST_LAST_STEP_SCORE_EQUAL_OR_IMPROVING</constructionHeuristicPickEarlyType>
</constructionHeuristic>
<localSearch>
<selector>
<selector>
<moveFactoryClass>org.drools.planner.core.move.generic.GenericChangeMoveFactory</moveFactoryClass>
</selector>
<selector>
<moveFactoryClass>org.drools.planner.core.move.generic.GenericSwapMoveFactory</moveFactoryClass>
</selector>
</selector>
<acceptor>
<planningEntityTabuSize>7</planningEntityTabuSize>
</acceptor>
<forager>
<minimalAcceptedSelection>1000</minimalAcceptedSelection>
</forager>
</localSearch>
</solver>```

This solver configuration consists out of 3 parts:

• Domain model configuration: What can Planner change? We need to make Planner aware of our domain classes:

```  <solutionClass>org.drools.planner.examples.cloudbalancing.domain.CloudBalance</solutionClass>
<planningEntityClass>org.drools.planner.examples.cloudbalancing.domain.CloudProcess</planningEntityClass>```
• Score configuration: How should Planner optimize the planning variables? Since we have hard and soft constraints, we use a HardAndSoftScore. But we also need to tell Planner how to calculate such the score, depending on our business requirements. Further down, we 'll look into 2 alternatives to calculate the score: using a simple Java implementation or using Drools DRL.

```  <scoreDirectorFactory>
<scoreDefinitionType>HARD_AND_SOFT</scoreDefinitionType>
<simpleScoreCalculatorClass>org.drools.planner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>
<!--<scoreDrl>/org/drools/planner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
</scoreDirectorFactory>```
• Optimization algorithms configuration: How should Planner optimize it? Don't worry about this for now: this is a good default configuration that works on most planning problems. It will already surpass human planners and most in-house implementations. Using the Planner benchmark toolkit, you can tweak it to get even better results.

```  <termination>
<maximumSecondsSpend>120</maximumSecondsSpend>
</termination>
<constructionHeuristic>
<constructionHeuristicType>FIRST_FIT_DECREASING</constructionHeuristicType>
<constructionHeuristicPickEarlyType>FIRST_LAST_STEP_SCORE_EQUAL_OR_IMPROVING</constructionHeuristicPickEarlyType>
</constructionHeuristic>
<localSearch>
<selector>
<selector>
<moveFactoryClass>org.drools.planner.core.move.generic.GenericChangeMoveFactory</moveFactoryClass>
</selector>
<selector>
<moveFactoryClass>org.drools.planner.core.move.generic.GenericSwapMoveFactory</moveFactoryClass>
</selector>
</selector>
<acceptor>
<planningEntityTabuSize>7</planningEntityTabuSize>
</acceptor>
<forager>
<minimalAcceptedSelection>1000</minimalAcceptedSelection>
</forager>
</localSearch>```

Let's examine the domain model classes and the score configuration.

### 5. Domain model implementation

#### 5.1. The class Computer

The class Computer is a POJO (Plain Old Java Object), nothing special. Usually, you'll have more of these kind of classes.

CloudComputer.java

```public class CloudComputer ... {

private int cpuPower;
private int memory;
private int networkBandwidth;
private int cost;

... // getters

}```

#### 5.2. The class Process

The class Process is a little bit special. We need to tell Planner that it can change the field computer, so we annotate the class with @PlanningEntity and the getter getComputer with @PlanningVariable:

CloudProcess.java

```@PlanningEntity(...)
public class CloudProcess ... {

private int requiredCpuPower;
private int requiredMemory;
private int requiredNetworkBandwidth;

private CloudComputer computer;

... // getters

@PlanningVariable(...)
@ValueRange(type = ValueRangeType.FROM_SOLUTION_PROPERTY, solutionProperty = "computerList")
public CloudComputer getComputer() {
return computer;
}

public void setComputer(CloudComputer computer) {
computer = computer;
}

public CloudProcess clone() {
CloudProcess clone = new CloudProcess();
clone.id = id;
clone.requiredCpuPower = requiredCpuPower;
clone.requiredMemory = requiredMemory;
clone.requiredNetworkBandwidth = requiredNetworkBandwidth;
clone.computer = computer;
return clone;
}

...

}```

The values that Planner can chose from for the field computer, are retrieved from a method on the Solution implementation: CloudBalance.getComputerList() which returns a list of all computers in the current data set. We tell Planner about this by using the annotation @ValueRange.

The method clone() is used by the class CloudBalance.

#### 5.3. The class CloudBalance

The class CloudBalance implements the Solution interface. It holds a list of all computers and processes. We need to tell Planner how to retrieve the collection of process which it can change, so we need to annotate the getter getProcessList with @PlanningEntityCollectionProperty.

The CloudBalance class also has a property score which is the Score of that Solution instance in it's current state:

CloudBalance.java

```public class CloudBalance ... implements Solution<HardAndSoftScore> {

private List<CloudComputer> computerList;

private List<CloudProcess> processList;

private HardAndSoftScore score;

public List<CloudComputer> getComputerList() {
return computerList;
}

@PlanningEntityCollectionProperty
public List<CloudProcess> getProcessList() {
return processList;
}

...

public HardAndSoftScore getScore() {
return score;
}

public void setScore(HardAndSoftScore score) {
this.score = score;
}

// ************************************************************************
// Complex methods
// ************************************************************************

public Collection<? extends Object> getProblemFacts() {
List<Object> facts = new ArrayList<Object>();
// Do not add the planning entity's (processList) because that will be done automatically
return facts;
}

/**
* Clone will only deep copy the {@link #processList}.
*/
public CloudBalance cloneSolution() {
CloudBalance clone = new CloudBalance();
clone.id = id;
clone.computerList = computerList;
List<CloudProcess> clonedProcessList = new ArrayList<CloudProcess>(
processList.size());
for (CloudProcess process : processList) {
CloudProcess clonedProcess = process.clone();
}
clone.processList = clonedProcessList;
clone.score = score;
return clone;
}

...

}```

The method getProblemFacts() is only needed for score calculation with Drools. It's not needed for the other score calculation types.

The method clone() is required. Planner uses it to make a clone of the best Solution in encounters during searching.

### 6. Score configuration

Planner will search for the Solution with the highest Score. We're using a HardAndSoftScore, which means Planner will look for the solution with no hard constraints broken (fulfill hardware requirements) and as little as possible soft constraints broken (minimize maintenance cost).

There are several ways to implement the score function:

• Simple Java

• Incremental Java

• Drools

Let's look at 2 different implementations:

#### 6.1. Simple Java score configuration

One way to define a score function is to implement the interface SimpleScoreCalculator in plain Java.

```  <scoreDirectorFactory>
<scoreDefinitionType>HARD_AND_SOFT</scoreDefinitionType>
<simpleScoreCalculatorClass>org.drools.planner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>
</scoreDirectorFactory>```

Just implement the method calculateScore(Solution) to return a DefaultHardAndSoftScore instance.

CloudBalancingSimpleScoreCalculator.java

```public class CloudBalancingSimpleScoreCalculator implements SimpleScoreCalculator<CloudBalance> {

/**
* A very simple implementation. The double loop can easily be removed by using Maps as shown in
*/
public HardAndSoftScore calculateScore(CloudBalance cloudBalance) {
int hardScore = 0;
int softScore = 0;
for (CloudComputer computer : cloudBalance.getComputerList()) {
int cpuPowerUsage = 0;
int memoryUsage = 0;
int networkBandwidthUsage = 0;
boolean used = false;

// Calculate usage
for (CloudProcess process : cloudBalance.getProcessList()) {
if (computer.equals(process.getComputer())) {
cpuPowerUsage += process.getRequiredCpuPower();
memoryUsage += process.getRequiredMemory();
networkBandwidthUsage += process.getRequiredNetworkBandwidth();
used = true;
}
}

// Hard constraints
int cpuPowerAvailable = computer.getCpuPower() - cpuPowerUsage;
if (cpuPowerAvailable < 0) {
hardScore += cpuPowerAvailable;
}
int memoryAvailable = computer.getMemory() - memoryUsage;
if (memoryAvailable < 0) {
hardScore += memoryAvailable;
}
int networkBandwidthAvailable = computer.getNetworkBandwidth() - networkBandwidthUsage;
if (networkBandwidthAvailable < 0) {
hardScore += networkBandwidthAvailable;
}

// Soft constraints
if (used) {
softScore -= computer.getCost();
}
}
return DefaultHardAndSoftScore.valueOf(hardScore, softScore);
}

}```

Even if we optimize the code above to use Maps to iterate through the processList only once, it is still slow because it doesn't do incremental score calculation. To fix that, either use an incremental Java score function or a Drools score function.

#### 6.2. Drools score configuration

To use the Drools rule engine as a score function, simply add a scoreDrl resource in the classpath:

```  <scoreDirectorFactory>
<scoreDefinitionType>HARD_AND_SOFT</scoreDefinitionType>
<scoreDrl>/org/drools/planner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>
</scoreDirectorFactory>```

First, we want to make sure that all computers have enough CPU, RAM and network bandwidth to support all their processes, so we make those hard constraints:

cloudBalancingScoreRules.drl - hard constraints

```...

import org.drools.planner.examples.cloudbalancing.domain.CloudBalance;
import org.drools.planner.examples.cloudbalancing.domain.CloudComputer;
import org.drools.planner.examples.cloudbalancing.domain.CloudProcess;

global HardAndSoftScoreHolder scoreHolder;

// ############################################################################
// Hard constraints
// ############################################################################

rule "requiredCpuPowerTotal"
when
\$computer : CloudComputer(\$cpuPower : cpuPower)
\$requiredCpuPowerTotal : Number(intValue > \$cpuPower) from accumulate(
CloudProcess(
computer == \$computer,
\$requiredCpuPower : requiredCpuPower),
sum(\$requiredCpuPower)
)
then
insertLogical(new IntConstraintOccurrence("requiredCpuPowerTotal", ConstraintType.NEGATIVE_HARD,
\$requiredCpuPowerTotal.intValue() - \$cpuPower,
\$computer));
end

rule "requiredMemoryTotal"
...
end

rule "requiredNetworkBandwidthTotal"
...
end

// ############################################################################
// Calculate hard score
// ############################################################################

// Accumulate hard constraints
rule "hardConstraintsBroken"
salience -1 // Do the other rules first (optional, for performance)
when
\$hardTotal : Number() from accumulate(
IntConstraintOccurrence(constraintType == ConstraintType.NEGATIVE_HARD, \$weight : weight),
sum(\$weight)
)
then
scoreHolder.setHardConstraintsBroken(\$hardTotal.intValue());
end```

Next, if those constraints are met, we want to minimize the maintenance cost, so we add that as a soft constraint:

cloudBalancingScoreRules.drl - soft constraints

```// ############################################################################
// Soft constraints
// ############################################################################

rule "computerCost"
when
\$computer : CloudComputer(\$cost : cost)
exists CloudProcess(computer == \$computer)
then
insertLogical(new IntConstraintOccurrence("computerCost", ConstraintType.NEGATIVE_SOFT,
\$cost,
\$computer));
end

// ############################################################################
// Calculate soft score
// ############################################################################

// Accumulate soft constraints
rule "softConstraintsBroken"
salience -1 // Do the other rules first (optional, for performance)
when
\$softTotal : Number() from accumulate(
IntConstraintOccurrence(constraintType == ConstraintType.NEGATIVE_SOFT, \$weight : weight),
sum(\$weight)
)
then
scoreHolder.setSoftConstraintsBroken(\$softTotal.intValue());
end```

If you use the Drools rule engine for score calculation, you can integrate with other Drools technologies, such as decision tables (XSL or web based), the Guvnor rule repository, ...

### 7. Beyond this tutorial

Now that this simple example works, you can go further. Try enriching the domain model and adding extra constraints:

• Each Process belongs to a Service. A computer can crash, so processes running the same service should be assigned to different computers.

• Each Computer is located in a Building. A building can burn down, so processes of the same services should be assigned to computers in different buildings.

For more information about Drools Planner, take a look at the reference manual.

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