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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 four constraints.
The following hard constraints must be fulfilled:
Every computer must be able to handle the minimum 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.
The following soft constraints 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.
This problem is a form of bin packing. The following is a simplified example, where 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 fast by using additional, smarter algorithms. It also scales: both in data (more processes, more computers) and constraints (more hardware requirements, other constraints). So see how Planner can be used in this scenario.
Table 2.1. Cloud Balancing Problem Size
Problem Size | Computers | Processes | Search Space |
---|---|---|---|
2computers-6processes | 2 | 6 | 64 |
3computers-9processes | 3 | 9 | 10^4 |
4computers-012processes | 4 | 12 | 10^7 |
100computers-300processes | 100 | 300 | 10^600 |
200computers-600processes | 200 | 600 | 10^1380 |
400computers-1200processes | 400 | 1200 | 10^3122 |
800computers-2400processes | 800 | 2400 | 10^6967 |
Beginning with the domain model:
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 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, it is the class
Process
.
Planning variable: the property (or properties) of a planning entity class that changes during planning.
In this example, it is the property computer
on the class
Process
.
Solution: the class that represents a data set and contains all planning entities. In this example that
is the class CloudBalance
.
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:
Example 2.1. CloudBalancingHelloWorld.java
public class CloudBalancingHelloWorld {
public static void main(String[] args) {
// Build the Solver
SolverFactory solverFactory = SolverFactory.createFromXmlResource(
"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.solve(unsolvedCloudBalance);
CloudBalance solvedCloudBalance = (CloudBalance) solver.getBestSolution();
// Display the result
System.out.println("\nSolved cloudBalance with 400 computers and 1200 processes:\n"
+ toDisplayString(solvedCloudBalance));
}
...
}
The code example does the following:
Build the Solver
based on a solver configuration (in this case an XML file from the
classpath).
SolverFactory solverFactory = SolverFactory.createFromXmlResource(
"org/optaplanner/examples/cloudbalancing/solver/cloudBalancingSolverConfig.xml");
Solver solver = 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.
solver.solve(unsolvedCloudBalance);
CloudBalance solvedCloudBalance = (CloudBalance) solver.getBestSolution();
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:
Example 2.2. cloudBalancingSolverConfig.xml
<?xml version="1.0" encoding="UTF-8"?>
<solver>
<!-- Domain model configuration -->
<scanAnnotatedClasses/>
<!-- Score configuration -->
<scoreDirectorFactory>
<scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
<easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
<!--<scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
<initializingScoreTrend>ONLY_DOWN</initializingScoreTrend>
</scoreDirectorFactory>
<!-- Optimization algorithms configuration -->
<termination>
<secondsSpentLimit>60</secondsSpentLimit>
</termination>
</solver>
This solver configuration consists of three parts:
Domain model configuration: What can Planner change? We need to make
Planner aware of our domain classes. In this configuration, it will automatically scan all classes in your
classpath (for an @PlanningEntity
or @PlanningSolution
annotation):
<scanAnnotatedClasses/>
Score configuration: How should Planner optimize the planning
variables? What is our goal? Since we have hard and soft constraints, we use a
HardSoftScore
. But we also need to tell Planner how to calculate the score, depending on
our business requirements. Further down, we will look into two alternatives to calculate the score: using a
simple Java implementation, or using Drools DRL.
<scoreDirectorFactory>
<scoreDefinitionType>HARD_SOFT</scoreDefinitionType>
<easyScoreCalculatorClass>org.optaplanner.examples.cloudbalancing.solver.score.CloudBalancingEasyScoreCalculator</easyScoreCalculatorClass>
<!--<scoreDrl>org/optaplanner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl</scoreDrl>-->
<initializingScoreTrend>ONLY_DOWN</initializingScoreTrend>
</scoreDirectorFactory>
Optimization algorithms configuration: How should Planner optimize it and for how long? In this case, we terminate it after 60 seconds and use the default optimization algorithms (because no explicit optimization algorithms are configured). The default algorithms should already easily surpass human planners and most in-house implementations. Use the Benchmarker to power tweak it to get even better results.
<termination>
<secondsSpentLimit>60</secondsSpentLimit>
</termination>
Let's examine the domain model classes and the score configuration.
The Computer
class is a POJO (Plain Old Java Object). Usually, you will have more of
this kind of classes.
Example 2.3. CloudComputer.java
public class CloudComputer ... {
private int cpuPower;
private int memory;
private int networkBandwidth;
private int cost;
... // getters
}
The Process
class is particularly important. 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(valueRangeProviderRefs = {"computerRange"})
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. The valueRangeProviderRefs
property is used to pass this information to the Planner.
Instead of getter annotations, it is also possible to use field annotations.
The CloudBalance
class implements the Solution
interface. It holds
a list of all computers and processes. We need to tell Planner how to retrieve the collection of processes that
it can change, therefore we must annotate the getter getProcessList
with
@PlanningEntityCollectionProperty
.
The CloudBalance
class also has a property score
, which is the
Score
of that Solution
instance in its current state:
Example 2.5. CloudBalance.java
@PlanningSolution
public class CloudBalance ... implements Solution<HardSoftScore> {
private List<CloudComputer> computerList;
private List<CloudProcess> processList;
private HardSoftScore score;
@ValueRangeProvider(id = "computerRange")
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 getProblemFacts()
method is only needed for score calculation with Drools. It is
not needed for the other score calculation types.
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:
Easy Java
Incremental Java
Drools
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.
Example 2.6. CloudBalancingEasyScoreCalculator.java
public class CloudBalancingEasyScoreCalculator implements EasyScoreCalculator<CloudBalance> {
/**
* A very simple implementation. The double loop can easily be removed by using Maps as shown in
* {@link CloudBalancingMapBasedEasyScoreCalculator#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 Map
s to iterate through the
processList
only once, it is still slow because it does not
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.
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 KIE Workbench, ...
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 might crash, so
processes running the same service should be assigned to different computers.
Each Computer
is located in a Building
. A building might burn
down, so processes of the same services should be assigned to computers in different buildings.