Package org.cloudsimplus.testbeds

Class ExperimentRunner<T extends Experiment>

java.lang.Object
org.cloudsimplus.testbeds.AbstractExperiment
org.cloudsimplus.testbeds.ExperimentRunner<T>
Type Parameters:
T - the type of Experiment the runner will execute
All Implemented Interfaces:
Runnable
public abstract class ExperimentRunner<T extends Experiment>
extends AbstractExperiment
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

  • Field Details

    • CONFIDENCE_LEVEL

      public static final double CONFIDENCE_LEVEL
      The confidence level for computing confidence interval.
      See Also:
      Constant Field Values

  • 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

    • ExperimentRunner

      public ExperimentRunner​(boolean antitheticVariatesTechnique, long baseSeed, boolean parallel)
      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

    • setupInternal

      protected abstract void setupInternal()

      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 List<Double> computeBatchMeans​(List<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 getBatchesNumber() (each value in the returned array will be the mean of every sample batch). Otherwise, returns the same given array

    • 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:

    • getBatchesNumber

      public int getBatchesNumber()
      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:

    • setBatchesNumber

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

    • 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 <T extends StatisticalDistribution> T createRandomGen​(int experimentIndex, Function<Long,​T> randomGenCreator)
      Uses the provided Function to create a pseudo random number generator (PRNG) for a experiment run. The kind and parameters for this PRNG is defined internally by the given Function. This method calls that Function just providing the seed to be used for the current experiment run. 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
      randomGenCreator - a Function that receives a seed generated by the runner and returns a new instance of some PRNG
      Returns:
      the created PRNG with the seed provided by the runner
      See Also:
      UniformDistr.isApplyAntitheticVariates(), createRandomGen(int, double, double)

    • 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()

    • getExperimentsExecutionTimeSecs

      public long getExperimentsExecutionTimeSecs()
      Time in seconds the experiments took to finish.
      Returns:

    • getExperimentsStartTimeSecs

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

    • run

      public void run()
      Setups and starts the execution of all experiments sequentially or in parallel.

    • createExperimentInternal

      protected abstract T createExperimentInternal​(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
      See Also:
      createExperiment(int)

    • computeAndPrintFinalResults

      protected org.apache.commons.math3.stat.descriptive.SummaryStatistics computeAndPrintFinalResults​(Map.Entry<String,​List<Double>> metricEntry)
      Computes and prints final simulation results, including mean, standard deviations and confidence intervals for a given metric computed across all simulation runs.
      Parameters:
      metricEntry - a map entry represented by the name of the metric and its list of values across multiple simulation runs
      Returns:
      the computed SummaryStatistics from the provided values for the metric

    • computeFinalStatistics

      protected final org.apache.commons.math3.stat.descriptive.SummaryStatistics computeFinalStatistics​(List<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.

    • confidenceErrorMargin

      protected double confidenceErrorMargin​(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

    • addMetricValue

      protected final void addMetricValue​(String metricName, double value)
      Add a value to a given metric inside the metricsMap.

      This method must be called for each metric inside the experiment finish listener. The listener can be set inside the runner's createExperimentInternal(int).

      See Also:
      Experiment.setAfterExperimentFinish(Consumer)

    • getMetricValues

      protected final List<Double> getMetricValues​(String metricName)

    • computeAntitheticMeans

      protected List<Double> computeAntitheticMeans​(List<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()

    • setBaseSeed

      public final ExperimentRunner setBaseSeed​(long baseSeed)

    • setFirstExperimentCreated

      public void setFirstExperimentCreated​(int firstExperimentCreated)

    • getFirstExperimentCreated

      public int getFirstExperimentCreated()

    • setDescription

      public ExperimentRunner setDescription​(String description)
      Sets a description for this experiment which is shown when it starts. It's also used to generate a caption for the Latex table.
      Parameters:
      description - the description to set
      See Also:
      enableLatexResultsTableGeneration()

    • setResultsTableId

      public ExperimentRunner setResultsTableId​(String resultsTableId)
      An id used to identify the experiment results table generated in formats such as Latex for computed metrics.
      Parameters:
      resultsTableId - the name to set
      See Also:
      enableLatexResultsTableGeneration()

    • enableLatexResultsTableGeneration

      public ExperimentRunner enableLatexResultsTableGeneration()
      Enables the generation of a result table in Latex format for computed metrics
      Returns: