This function computes steady-state performance measures of closed queueing networks using the Mean Value Analysis (MVA) algorithm. The qneneing network is allowed to contain fixed-capacity centers, delay centers or general load-dependent centers. Multiple request classes are supported.
This function dispatches the computation to one of
qncsemva
, qncsmvald
or qncmmva
.
S(k)
is the average service time of center k, and this function
calls qncsmva
which supports load-independent
service centers. If S is a matrix, S(k,i)
is the
average service time at center k when i=1, …, N
jobs are present; in this case, the network is analyzed with the
qncmmvald
function.
qncmmva
function.
See also: qncsmva, qncsmvald, qncmmva.
The following code
P = [0 0.3 0.7; 1 0 0; 1 0 0]; # Transition probability matrix S = [1 0.6 0.2]; # Average service times m = ones(size(S)); # All centers are single-server Z = 2; # External delay N = 15; # Maximum population to consider V = qncsvisits(P); # Compute number of visits X_bsb_lower = X_bsb_upper = X_ab_lower = X_ab_upper = X_mva = zeros(1,N); for n=1:N [X_bsb_lower(n) X_bsb_upper(n)] = qncsbsb(n, S, V, m, Z); [X_ab_lower(n) X_ab_upper(n)] = qncsaba(n, S, V, m, Z); [U R Q X] = qnclosed( n, S, V, m, Z ); X_mva(n) = X(1)/V(1); endfor close all; plot(1:N, X_ab_lower,"g;Asymptotic Bounds;", ... 1:N, X_bsb_lower,"k;Balanced System Bounds;", ... 1:N, X_mva,"b;MVA;", "linewidth", 2, ... 1:N, X_bsb_upper,"k", 1:N, X_ab_upper,"g" ); axis([1,N,0,1]); legend("location","southeast"); legend("boxoff"); xlabel("Number of Requests n"); ylabel("System Throughput X(n)");
Produces the following figure
Figure 1 |
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Package: queueing