umontreal.iro.lecuyer.examples.TimeShared Maven / Gradle / Ivy

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SSJ is a Java library for stochastic simulation, developed under the direction of Pierre L'Ecuyer, in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal. It provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting (elementary) statistics, and programming discrete-event simulations with both events and processes.

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import umontreal.iro.lecuyer.simevents.*;
import umontreal.iro.lecuyer.simprocs.*;
import umontreal.iro.lecuyer.rng.*;
import umontreal.iro.lecuyer.randvar.*;
import umontreal.iro.lecuyer.stat.Tally;
import java.io.*;

public class TimeShared {
   int nbTerminal   = 20;    // Number of terminals.
   double quantum;           // Quantum size.
   double overhead  = 0.001; // Amount of overhead (h).
   double meanThink = 5.0;   // Mean thinking time.
   double alpha     = 0.5;   // Parameters of the Weibull service times.
   double lambda    = 1.0;   //               ''
   double delta     = 0.0;   //               ''
   int N            = 1100;  // Total number of tasks to simulate.
   int N0           = 100;   // Number of tasks for warmup.
   int nbTasks;              // Number of tasks ended so far.

   RandomStream streamThink  = new MRG32k3a();
   RandomVariateGen genThink = new ExponentialGen (streamThink, 1.0/meanThink);
   RandomStream streamServ   = new MRG32k3a ("Gen. for service requirements");
   RandomVariateGen genServ = new WeibullGen (streamServ, alpha, lambda, delta);
   Resource server       = new Resource (1, "The server");
   Tally meanInRep       = new Tally ("Average for current run");
   Tally statDiff        = new Tally ("Diff. on mean response times");

   class Terminal extends SimProcess {
      public void actions() {
         double arrivTime;    // Arrival time of current request.
         double timeNeeded;   // Server's time still needed for it.
         while (nbTasks < N) {
            delay (genThink.nextDouble());
            arrivTime = Sim.time();
            timeNeeded = genServ.nextDouble();
            while (timeNeeded > quantum) {
               server.request (1);
               delay (quantum + overhead);
               timeNeeded -= quantum;
               server.release (1);
            }
            server.request (1);  // Here, timeNeeded <= quantum.
            delay (timeNeeded + overhead);
            server.release (1);
            nbTasks++;
            if (nbTasks > N0)
               meanInRep.add (Sim.time() - arrivTime);
                       // Take the observation if warmup is over.
         }
         Sim.stop();            // N tasks have now completed.
      }
   }

   private void simulOneRun() {
      SimProcess.init();
      server.init();
      meanInRep.init();
      nbTasks = 0;
      for (int i=1; i <= nbTerminal; i++)
         new Terminal().schedule (0.0);
      Sim.start();
   }

   // Simulate numRuns pairs of runs and prints a confidence interval
   // on the difference of perf. for quantum sizes q1 and q2.
   public void simulateConfigs (double numRuns, double q1, double q2) {
      double mean1;  // To memorize average for first configuration.
      for (int rep = 0; rep < numRuns; rep++) {
         quantum = q1;
         simulOneRun();
         mean1 = meanInRep.average();
         streamThink.resetStartSubstream();
         streamServ.resetStartSubstream();
         quantum = q2;
         simulOneRun();
         statDiff.add (mean1 - meanInRep.average());
         streamThink.resetNextSubstream();
         streamServ.resetNextSubstream();
      }
      statDiff.setConfidenceIntervalStudent();
      System.out.println (statDiff.report (0.9, 3));
   }

   public static void main (String[] args) { 
      new TimeShared().simulateConfigs (10, 0.1, 0.2);
   }
}