JBoss.orgCommunity Documentation

Chapter 2. Quick Start

2.1. Cloud Balancing Tutorial
2.1.1. Problem Description
2.1.2. Value Proposition
2.1.3. Problem Size
2.1.4. Domain Model Design
2.1.5. Main Method
2.1.6. Solver Configuration
2.1.7. Domain Model Implementation
2.1.8. Score Configuration
2.1.9. Beyond this Tutorial

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.

The following hard constraints must be fulfilled:

The following soft constraints should be optimized:

This problem is a form of bin packing. The following is a simplified example, in which we assign four processes to two computers with two 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 is not optimal, as it does not leave enough room to assign the yellow process D.

Planner does find the more optimal solution by using additional, smarter algorithms. It also scales: both in data (more processes, more computers) and constraints (more hardware requirements, other constraints). So let's see how Planner can be used in this scenario.

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


The code example does the following:

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

            SolverFactory<CloudBalance> solverFactory = SolverFactory.createFromXmlResource(
                    "org/optaplanner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
            Solver solver<CloudBalance> = solverFactory.buildSolver();
  • Load the problem. CloudBalancingGenerator generates a random problem: you will 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.

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

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

The only complicated part is building the Solver, as detailed in the next section.

Take a look at the solver configuration:


This solver configuration consists of three parts:

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

Planner will search for the Solution with the highest Score. This example uses 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:

Let's take a look at two different implementations:

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

  <scoreDirectorFactory>
    <scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
    <easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
  </scoreDirectorFactory>

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


Even if we optimize the code above to use Maps to iterate through the processList only once, it is still slow because it does not do incremental score calculation. To fix that, either use incremental Java score calculation or Drools score calculation. Let's take a look at the latter.

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:


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


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 KIE Workbench, ...