Package org.cloudsimplus.testbeds

Class ExperimentRunner<T extends Experiment>

java.lang.Object
org.cloudsimplus.testbeds.ExperimentRunner<T>
Type Parameters:
T - the type of Experiment the runner will execute
All Implemented Interfaces:
java.lang.Runnable
public abstract class ExperimentRunner<T extends Experiment>
extends java.lang.Object
implements java.lang.Runnable
A base class to run a given experiment a defined number of times and collect statistics about the execution. The runner represents a testbed compounded of a set of experiments that it runs.
Since:
CloudSim Plus 1.0
Author:
Manoel Campos da Silva Filho

  • Constructor Summary

    Constructors 
    Constructor Description
    ExperimentRunner​(boolean antitheticVariatesTechnique)
    Creates an experiment runner, setting the base seed as the current time.
    ExperimentRunner​(boolean antitheticVariatesTechnique, long baseSeed)
    Creates an experiment runner with a given base seed.

  • Method Summary

    Modifier and Type Method Description
    int batchSizeCeil()  
    protected java.util.List<java.lang.Double> computeAntitheticMeans​(java.util.List<java.lang.Double> samples)
    Computes the antithetic means for the given samples if the "Antithetic Variates Technique" is to be applied.
    protected java.util.List<java.lang.Double> computeBatchMeans​(java.util.List<java.lang.Double> samples)
    Gets an list of samples and apply the "Batch Means Method" to reduce samples correlation, if the "Batch Means Method" is to be applied.
    protected double computeConfidenceErrorMargin​(org.apache.commons.math3.stat.descriptive.SummaryStatistics stats, double confidenceLevel)
    Computes the confidence interval error margin for a given set of samples in order to enable finding the interval lower and upper bound around a mean value.
    protected org.apache.commons.math3.stat.descriptive.SummaryStatistics computeFinalStatistics​(java.util.List<java.lang.Double> values)
    Creates a SummaryStatistics object from a list of Double values, allowing computation of statistics such as mean over these values.
    protected abstract T createExperiment​(int i)
    Creates an experiment to be run for the i'th time.
    protected abstract java.util.Map<java.lang.String,​java.util.List<java.lang.Double>> createMetricsMap()
    Creates a Map adding a List of values for each metric to be computed.
    ContinuousDistribution createRandomGen​(int experimentIndex)
    Creates a pseudo random number generator (PRNG) for a experiment run that generates uniform values between [0 and 1[.
    ContinuousDistribution createRandomGen​(int experimentIndex, double minInclusive, double maxExclusive)
    Creates a pseudo random number generator (PRNG) for a experiment run that generates uniform values between [min and max[.
    long getBaseSeed()
    Gets the seed to be used for the first executed experiment.
    long getExperimentsFinishTime()
    Time in seconds the experiments finished.
    long getExperimentsStartTime()
    Time in seconds the experiments started.
    int getNumberOfBatches()
    Gets the number of batches in which the simulation runs will be divided.
    int getSimulationRuns()
    Gets the number of times the experiment will be executed in order to get values such as means and standard deviations.
    int halfSimulationRuns()  
    boolean isApplyAntitheticVariatesTechnique()
    Checks if the "Antithetic Variates Technique" is to be applied to reduce results variance.
    boolean isApplyBatchMeansMethod()
    Checks if the "Batch Means Method" is to be applied to reduce correlation between the results for different experiment runs.
    boolean isToReuseSeedFromFirstHalfOfExperiments​(int currentExperimentIndex)  
    boolean isVerbose()
    Indicates if the runner will output execution logs or not.
    protected abstract void printFinalResults​(java.lang.String metricName, org.apache.commons.math3.stat.descriptive.SummaryStatistics stats)
    Prints final simulation results such as means, standard deviations and confidence intervals.
    protected abstract void printSimulationParameters()  
    void run()
    Setups and starts the execution of all experiments.
    ExperimentRunner setBaseSeed​(long baseSeed)  
    ExperimentRunner setNumberOfBatches​(int numberOfBatches)
    Sets the number of batches in which the simulation runs will be divided.
    protected ExperimentRunner setSimulationRuns​(int simulationRuns)  
    protected ExperimentRunner setSimulationRunsAsMultipleOfBatchNumber()
    Adjusts the current number of simulations to be equal to its closer multiple of the number of batches.
    protected abstract void setup()
    Setup experiment attributes considering the dependency between each other.
    ExperimentRunner setVerbose​(boolean verbose)
    Defines if the runner will output execution logs or not.
    boolean simulationRunsAndNumberOfBatchesAreCompatible()
    Checks if the number of simulation runs and the number of batches are compatible

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • ExperimentRunner

      public ExperimentRunner​(boolean antitheticVariatesTechnique)
      Creates an experiment runner, setting the base seed as the current time.
      Parameters:
      antitheticVariatesTechnique - indicates if it's to be applied the antithetic variates technique.

    • ExperimentRunner

      public ExperimentRunner​(boolean antitheticVariatesTechnique, long baseSeed)
      Creates an experiment runner with a given base seed.
      Parameters:
      antitheticVariatesTechnique - indicates if it's to be applied the antithetic variates technique.
      baseSeed - the seed to be used as base for each experiment seed

  • Method Details

    • setup

      protected abstract void setup()

      Setup experiment attributes considering the dependency between each other. The method is called by the run() method, just after all the attributes were set. By this way, it initializes internal attributes and validates other ones.

      NOTE: As a good practice, it is tried to reduce the number of parameters for the class constructor, as it tends to increase as the experiment code evolves. Accordingly, all the parameters have to be defined using the corresponding setters. By this way, it has to be avoided setting up attributes inside the constructor, once they can become invalid or out-of-date because dependency between parameters. The constructor has just to initialize objects to avoid NullPointerException. This way, one have to set all the parameters inside this method. For instance, if the constructor creates and Random Number Generator (PRNG) using a default seed but the method setSeed is called after the constructor, the PRNG will not be update to use the new seed.

    • batchSizeCeil

      public int batchSizeCeil()
      Returns:
      the batch size rounded by the Math.ceil(double) method.

    • simulationRunsAndNumberOfBatchesAreCompatible

      public boolean simulationRunsAndNumberOfBatchesAreCompatible()
      Checks if the number of simulation runs and the number of batches are compatible
      Returns:

    • isApplyBatchMeansMethod

      public boolean isApplyBatchMeansMethod()
      Checks if the "Batch Means Method" is to be applied to reduce correlation between the results for different experiment runs.
      Returns:

    • computeBatchMeans

      protected java.util.List<java.lang.Double> computeBatchMeans​(java.util.List<java.lang.Double> samples)
      Gets an list of samples and apply the "Batch Means Method" to reduce samples correlation, if the "Batch Means Method" is to be applied.
      Parameters:
      samples - the list with samples to apply the "Batch Means Method". Samples size is defined by the getSimulationRuns().
      Returns:
      the samples list after applying the "Batch Means Method", in case the method is enabled to be applied, which will reduce the array to the number of batches defined by getNumberOfBatches() (each value in the returned array will be the mean of every sample batch). Otherwise, returns the same given array

    • computeConfidenceErrorMargin

      protected double computeConfidenceErrorMargin​(org.apache.commons.math3.stat.descriptive.SummaryStatistics stats, double confidenceLevel)

      Computes the confidence interval error margin for a given set of samples in order to enable finding the interval lower and upper bound around a mean value. By this way, the confidence interval can be computed as [mean + errorMargin .. mean - errorMargin].

      To reduce the confidence interval by half, one have to execute the experiments 4 more times. This is called the "Replication Method" and just works when the samples are i.i.d. (independent and identically distributed). Thus, if you have correlation between samples of each simulation run, a different method such as a bias compensation, batch means or regenerative method has to be used.

      NOTE: How to compute the error margin is a little bit confusing. The Harry Perros' book states that if less than 30 samples are collected, the t-Distribution has to be used to that purpose. However, this article Wikipedia article says that if the standard deviation of the real population is known, it has to be used the z-value from the Standard Normal Distribution. Otherwise, it has to be used the t-value from the t-Distribution to calculate the critical value for defining the error margin (also called standard error). The book "Numeric Computation and Statistical Data Analysis on the Java Platform" confirms the last statement and such approach was followed.
      Parameters:
      stats - the statistic object with the values to compute the error margin of the confidence interval
      confidenceLevel - the confidence level, in the interval from ]0 to 1[, such as 0.95 to indicate 95% of confidence.
      Returns:
      the error margin to compute the lower and upper bound of the confidence interval
      See Also:
      Critical Values of the Student's t Distribution, t-Distribution, Harry Perros, "Computer Simulation Techniques: The definitive introduction!," 2009, Numeric Computation and Statistical Data Analysis on the Java Platform

    • isApplyAntitheticVariatesTechnique

      public boolean isApplyAntitheticVariatesTechnique()
      Checks if the "Antithetic Variates Technique" is to be applied to reduce results variance.
      Returns:
      See Also:
      Antithetic variates

    • getSimulationRuns

      public int getSimulationRuns()
      Gets the number of times the experiment will be executed in order to get values such as means and standard deviations. It has to be an even number if the "Antithetic Variates Technique" is to be used.
      Returns:

    • setSimulationRuns

      protected ExperimentRunner setSimulationRuns​(int simulationRuns)

    • setSimulationRunsAsMultipleOfBatchNumber

      protected ExperimentRunner setSimulationRunsAsMultipleOfBatchNumber()
      Adjusts the current number of simulations to be equal to its closer multiple of the number of batches.
      Returns:

    • getNumberOfBatches

      public int getNumberOfBatches()
      Gets the number of batches in which the simulation runs will be divided. If this number is greater than 1, the "Batch Means Method" is used to reduce the correlation between experiment runs.
      Returns:

    • setNumberOfBatches

      public final ExperimentRunner setNumberOfBatches​(int numberOfBatches)
      Sets the number of batches in which the simulation runs will be divided.
      Parameters:
      numberOfBatches - number of simulation run batches
      Returns:
      See Also:
      getNumberOfBatches()

    • getBaseSeed

      public long getBaseSeed()
      Gets the seed to be used for the first executed experiment. The seed for each subsequent experiment is this seed plus the index of the experiment.
      Returns:

    • createRandomGen

      public ContinuousDistribution createRandomGen​(int experimentIndex)
      Creates a pseudo random number generator (PRNG) for a experiment run that generates uniform values between [0 and 1[. If it is to apply the "Antithetic Variates Technique" to reduce results variance, the second half of experiments will used the seeds from the first half.
      Parameters:
      experimentIndex - index of the experiment run to create a PRNG
      Returns:
      the created PRNG
      See Also:
      UniformDistr.isApplyAntitheticVariates(), createRandomGen(int, double, double)

    • createRandomGen

      public ContinuousDistribution createRandomGen​(int experimentIndex, double minInclusive, double maxExclusive)
      Creates a pseudo random number generator (PRNG) for a experiment run that generates uniform values between [min and max[. If it is to apply the "Antithetic Variates Technique" to reduce results' variance, the second half of experiments will use the seeds from the first half.
      Parameters:
      experimentIndex - index of the experiment run to create a PRNG
      minInclusive - the minimum value the generator will return (inclusive)
      maxExclusive - the maximum value the generator will return (exclusive)
      Returns:
      the created PRNG
      See Also:
      UniformDistr.isApplyAntitheticVariates(), createRandomGen(int)

    • isToReuseSeedFromFirstHalfOfExperiments

      public boolean isToReuseSeedFromFirstHalfOfExperiments​(int currentExperimentIndex)

    • halfSimulationRuns

      public int halfSimulationRuns()
      Returns:
      the half of getSimulationRuns()

    • getExperimentsFinishTime

      public long getExperimentsFinishTime()
      Time in seconds the experiments finished.
      Returns:

    • getExperimentsStartTime

      public long getExperimentsStartTime()
      Time in seconds the experiments started.
      Returns:

    • run

      public void run()
      Setups and starts the execution of all experiments.
      Specified by:
      run in interface java.lang.Runnable

    • createMetricsMap

      protected abstract java.util.Map<java.lang.String,​java.util.List<java.lang.Double>> createMetricsMap()
      Creates a Map adding a List of values for each metric to be computed. The computation of final experiments results are performed on this map.

      Each key is the name of metric and each value is a List of Double containing the values collected for that metric, for each experiment run. These values will be then summarized to compute the final value for each metric.

      Returns:
      the populated metricsMap

    • createExperiment

      protected abstract T createExperiment​(int i)
      Creates an experiment to be run for the i'th time.
      Parameters:
      i - a number that identifies the experiment
      Returns:
      the created experiment

    • computeAntitheticMeans

      protected java.util.List<java.lang.Double> computeAntitheticMeans​(java.util.List<java.lang.Double> samples)

      Computes the antithetic means for the given samples if the "Antithetic Variates Technique" is to be applied. These values are the mean between the first half of samples with the second half. By this way, the resulting value is an array with half of the samples length.

      NOTE: To correctly compute the antithetic values the seeds from the first half of experiments must be used for the second half.

      Parameters:
      samples - the list of samples to compute the antithetic means from
      Returns:
      the computed antithetic means from the given samples if the "Antithetic Variates Technique" is to be applied, otherwise return the same given samples list.
      See Also:
      createRandomGen(int, double, double)

    • printSimulationParameters

      protected abstract void printSimulationParameters()

    • computeFinalStatistics

      protected org.apache.commons.math3.stat.descriptive.SummaryStatistics computeFinalStatistics​(java.util.List<java.lang.Double> values)
      Creates a SummaryStatistics object from a list of Double values, allowing computation of statistics such as mean over these values. The method also checks if the Antithetic Variates and the Batch Means techniques are enabled and then apply them over the given list of Doubles. These techniques are used for variance reduction.
      Parameters:
      values - the List of values to add to the SummaryStatistics object
      Returns:
      the SummaryStatistics object containing the double values, after applying the the techniques for variance reduction.

    • printFinalResults

      protected abstract void printFinalResults​(java.lang.String metricName, org.apache.commons.math3.stat.descriptive.SummaryStatistics stats)
      Prints final simulation results such as means, standard deviations and confidence intervals.
      Parameters:
      metricName - the name of the metric to be printed
      stats - the SummaryStatistics containing means of each experiment run that will be used to computed an overall mean and other statistics

    • setBaseSeed

      public final ExperimentRunner setBaseSeed​(long baseSeed)

    • isVerbose

      public boolean isVerbose()
      Indicates if the runner will output execution logs or not. This doesn't affect the verbosity of individual experiments executed. Each Experiment has its own verbose attribute.
      Returns:

    • setVerbose

      public ExperimentRunner setVerbose​(boolean verbose)
      Defines if the runner will output execution logs or not. This doesn't affect the verbosity of individual experiments executed. Each Experiment has its own verbose attribute.
      Parameters:
      verbose - true if results have to be output, false otherwise
      Returns: