Chapter 2. Quick start

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 minimum hardware requirements of the sum of its processes:

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.

OptaPlanner 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.

2.1.2. Problem size

cb-0002comp-0006proc has   2 computers and    6 processes with a search space of      64.
cb-0003comp-0009proc has   3 computers and    9 processes with a search space of    10^4.
cb-0004comp-0012proc has   4 computers and   12 processes with a search space of    10^7.
cb-0100comp-0300proc has 100 computers and  300 processes with a search space of  10^600.
cb-0200comp-0600proc has 200 computers and  600 processes with a search space of 10^1380.
cb-0400comp-1200proc has 400 computers and 1200 processes with a search space of 10^3122.
cb-0800comp-2400proc has 800 computers and 2400 processes with a search space of 10^6967.

2.1.3. 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 OptaPlanner.
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.

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

Example 2.1. CloudBalancingHelloWorld.java

public class CloudBalancingHelloWorld {


    public static void main(String[] args) {

        // Build the Solver

        SolverFactory solverFactory = new XmlSolverFactory(

                "/org/optaplanner/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/optaplanner/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.

2.1.5. Solver configuration

Take a look at the solver configuration:

Example 2.2. cloudBalancingSolverConfig.xml


<?xml version="1.0" encoding="UTF-8"?>

<solver>

  <!--<environmentMode>FAST_ASSERT</environmentMode>-->



  <!-- Domain model configuration -->

  <solutionClass>org.optaplanner.examples.cloudbalancing.domain.CloudBalance</solutionClass>

  <planningEntityClass>org.optaplanner.examples.cloudbalancing.domain.CloudProcess</planningEntityClass>



  <!-- Score configuration -->

  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    <simpleScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>

    <!--<scoreDrl>/org/optaplanner/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>

    <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.optaplanner.examples.cloudbalancing.domain.CloudBalance</solutionClass>

  <planningEntityClass>org.optaplanner.examples.cloudbalancing.domain.CloudProcess</planningEntityClass>

Score configuration: How should Planner optimize the planning variables? Since we have hard and soft constraints, we use a HardSoftScore. 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_SOFT</scoreDefinitionType>

    <simpleScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>

    <!--<scoreDrl>/org/optaplanner/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>

    <acceptor>

      <planningEntityTabuSize>7</planningEntityTabuSize>

    </acceptor>

    <forager>

      <minimalAcceptedSelection>1000</minimalAcceptedSelection>

    </forager>

  </localSearch>

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

2.1.6. Domain model implementation

2.1.6.1. 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.

Example 2.3. CloudComputer.java

public class CloudComputer ... {


    private int cpuPower;

    private int memory;

    private int networkBandwidth;

    private int cost;


    ... // getters

}

2.1.6.2. 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:

Example 2.4. 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;

    }


    // ************************************************************************

    // Complex methods

    // ************************************************************************


    ...


}

The values that Planner can choose 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.

2.1.6.3. 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:

Example 2.5. CloudBalance.java

public class CloudBalance ... implements Solution<HardSoftScore> {


    private List<CloudComputer> computerList;


    private List<CloudProcess> processList;


    private HardSoftScore score;


    public List<CloudComputer> getComputerList() {

        return computerList;

    }


    @PlanningEntityCollectionProperty

    public List<CloudProcess> getProcessList() {

        return processList;

    }


    ...


    public HardSoftScore getScore() {

        return score;

    }


    public void setScore(HardSoftScore score) {

        this.score = score;

    }


    // ************************************************************************

    // Complex methods

    // ************************************************************************


    public Collection<? extends Object> getProblemFacts() {

        List<Object> facts = new ArrayList<Object>();

        facts.addAll(computerList);

        // Do not add the planning entity's (processList) because that will be done automatically

        return facts;

    }


    ...


}

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

2.1.7. Score configuration

Planner will search for the Solution with the highest Score. We're using a HardSoftScore, 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).

Of course, Planner needs to be told about these domain-specific score constraints. There are several ways to implement such a score function:

Simple Java
Incremental Java
Drools

Let's take a look look at 2 different implementations:

2.1.7.1. Simple Java score configuration

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


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    <simpleScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingSimpleScoreCalculator</simpleScoreCalculatorClass>

  </scoreDirectorFactory>

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

Example 2.6. 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

     * {@link CloudBalancingMapBasedSimpleScoreCalculator#calculateScore(CloudBalance)}.

     */

    public HardSoftScore 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 HardSoftScore.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. Let's take a look at the latter.

2.1.7.2. Drools score configuration

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


  <scoreDirectorFactory>

    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>

    <scoreDrl>/org/optaplanner/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 these hard constraints:

Example 2.7. cloudBalancingScoreRules.drl - hard constraints

...

import org.optaplanner.examples.cloudbalancing.domain.CloudBalance;
import org.optaplanner.examples.cloudbalancing.domain.CloudComputer;
import org.optaplanner.examples.cloudbalancing.domain.CloudProcess;

global HardSoftScoreHolder scoreHolder;

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

rule "requiredCpuPowerTotal"
    when
        $computer : CloudComputer($cpuPower : cpuPower)
        $requiredCpuPowerTotal : Number(intValue > $cpuPower) from accumulate(
            CloudProcess(
                computer == $computer,
                $requiredCpuPower : requiredCpuPower),
            sum($requiredCpuPower)
        )
    then
        scoreHolder.addHardConstraintMatch(kcontext, $cpuPower - $requiredCpuPowerTotal.intValue());
end

rule "requiredMemoryTotal"
    ...
end

rule "requiredNetworkBandwidthTotal"
    ...
end

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

Example 2.8. cloudBalancingScoreRules.drl - soft constraints

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

rule "computerCost"
    when
        $computer : CloudComputer($cost : cost)
        exists CloudProcess(computer == $computer)
    then
        scoreHolder.addSoftConstraintMatch(kcontext, - $cost);
end

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

2.1.8. Beyond this tutorial

Now that this simple example works, try going further. Enrich the domain model and add extra constraints such as these:

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.