statepr_aggr_large.m

clear node jobclass solver
 
model = Network('model');
 
node{1} = Delay(model, 'Delay');
node{2} = Queue(model, 'Queue1', SchedStrategy.PS);
node{3} = Queue(model, 'Queue2', SchedStrategy.PS);
 
node{3}.setNumServers(2);
 
N=[1,0,4,0];
jobclass{1} = ClosedClass(model, 'Class1', N(1), node{1}, 0);
jobclass{2} = ClosedClass(model, 'Class2', N(2), node{1}, 0);
jobclass{3} = ClosedClass(model, 'Class3', N(3), node{1}, 0);
jobclass{4} = ClosedClass(model, 'Class4', N(4), node{1}, 0);
 
node{1}.setService(jobclass{1}, Exp(1));
node{1}.setService(jobclass{2}, Exp(2));
node{1}.setService(jobclass{3}, Exp(1));
node{1}.setService(jobclass{4}, Exp(1));
 
node{2}.setService(jobclass{1}, Exp(3));
node{2}.setService(jobclass{2}, Exp(4));
node{2}.setService(jobclass{3}, Exp(5));
node{2}.setService(jobclass{4}, Exp(1));
 
node{3}.setService(jobclass{1}, Exp(1));
node{3}.setService(jobclass{2}, Exp(3));
node{3}.setService(jobclass{3}, Exp(5));
node{3}.setService(jobclass{4}, Exp(2));
 
K = length(jobclass);
P = cell(K,K);
 
P{1,1} = [0,1,0; 0,0,1; 0,0,0];
P{1,2} = [0,0,0; 0,0,0; 1,0,0];
P{1,3} = [0,0,0; 0,0,0; 0,0,0];
P{1,4} = [0,0,0; 0,0,0; 0,0,0];
 
P{2,1} = [0,0,0; 0,0,0; 1,0,0];
P{2,2} = [0,1,0; 0,0,1; 0,0,0];
P{2,3} = [0,0,0; 0,0,0; 0,0,0];
P{2,4} = [0,0,0; 0,0,0; 0,0,0];
 
P{3,1} = [0,0,0; 0,0,0; 0,0,0];
P{3,2} = [0,0,0; 0,0,0; 0,0,0];
P{3,3} = [0,1,0; 0,0,1; 0,0,0];
P{3,4} = [0,0,0; 0,0,0; 1,0,0];
 
P{4,1} = [0,0,0; 0,0,0; 0,0,0];
P{4,2} = [0,0,0; 0,0,0; 0,0,0];
P{4,3} = [0,0,0; 0,0,0; 1,0,0];
P{4,4} = [0,0,1; 0,0,0; 0,0,0];
 
%%pause
model.link(P);
 
M = model.getNumberOfStations();
K = model.getNumberOfClasses();
 
% This part illustrates the execution of different solvers
fprintf(1,'This example illustrates the calculation of probabilities via normalizing constants.\n')
 
 
solver={};
options = Solver.defaultOptions;
options.verbose=1;
options.seed=23000;
%options.samples=1e5;
 
n=[-1,-1,-1,-1;
    -1,-1,-1,-1;
    1, 0, 2, 1];% rows set to -1 are ignored in the calculation of the marginal probabilities
for i=1:M
    node{i}.setState(n(i,:));
end
state = model.getState;
 
solver = CTMC(model,options);
for i=M
    Pr = solver.getProbAggr(node{i});
    fprintf(1,'Station %d is in state %s with probability %d\n',i,mat2str(state{i}),Pr);
end
Pr_ctmc = Pr
 
solver = NC(model,options);
for i=M
    Pr = solver.getProbAggr(node{i});
    fprintf(1,'Station %d is in state %s with probability %d\n',i,mat2str(state{i}),Pr);
end
Pr_nc = Pr