3model = Network(
'model');
5node{1} = Delay(model,
'Delay');
6node{2} = Queue(model,
'Queue2', SchedStrategy.PS);
8jobclass{1} = ClosedClass(model,
'Class1', 1, node{1}, 0);
10servProc1 = Exp(1/0.1);
11node{1}.setService(
jobclass{1}, servProc1);
12servProc2 = Erlang.fitMeanAndSCV(1,1/3);
13node{2}.setService(
jobclass{1}, servProc2);
15M = model.getNumberOfStations();
16K = model.getNumberOfClasses();
23solver = JMT(model,
'seed',23000);
24RDsim = solver.getCdfRespT();
26for i=1:model.getNumberOfStations
27 for c=1:model.getNumberOfClasses
28% plot(FC{i,c}(:,2),FC{i,c}(:,1)); hold all;
29 AvgRespTfromCDFSim(i,c) = diff(RDsim{i,c}(:,1))
'*RDsim{i,c}(2:end,2); %mean
30 PowerMoment2_R(i,c) = diff(RDsim{i,c}(:,1))'*(RDsim{i,c}(2:end,2).^2);
31 Variance_R(i,c) = PowerMoment2_R(i,c)-AvgRespTfromCDFSim(i,c)^2; %variance
32 SqCoeffOfVariationRespTfromCDFSim(i,c) = (Variance_R(i,c))/AvgRespTfromCDFSim(i,c)^2; %scv
37RDfluid = solver.getCdfRespT();
39for i=1:model.getNumberOfStations
40 for c=1:model.getNumberOfClasses
41% plot(FC{i,c}(:,2),FC{i,c}(:,1)); hold all;
42 AvgRespTfromCDFFluid(i,c) = diff(RDfluid{i,c}(:,1))
'*RDfluid{i,c}(2:end,2); %mean
43 PowerMoment2_R(i,c) = diff(RDfluid{i,c}(:,1))'*(RDfluid{i,c}(2:end,2).^2);
44 Variance_R(i,c) = PowerMoment2_R(i,c)-AvgRespTfromCDFFluid(i,c)^2; %variance
45 SqCoeffOfVariationRespTfromCDFFluid(i,c) = (Variance_R(i,c))/AvgRespTfromCDFFluid(i,c)^2; %scv
49disp('Since there
is a single job, mean and squared coefficient of variation');
50disp('of response times are close, up to fluid approximation precision, those');
51fprintf(1,'of the service time distribution.\n\n');
52AvgRespTfromTheory = [servProc1.getMean; servProc2.getMean]
55SqCoeffOfVariationRespTfromTheory = [servProc1.getSCV; servProc2.getSCV]
56SqCoeffOfVariationRespTfromCDFSim
57SqCoeffOfVariationRespTfromCDFFluid