Copyright © 2008, 2009, 2010, 2011, 2012 Moreno Marzolla.

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queueing

This manual documents how to install and run the Queueing Toolbox. It corresponds to version 1.0.0 of the package.


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1 Summary

This document describes the queueing toolbox for GNU Octave (queueing in short). The queueing toolbox, previously known as qnetworks, is a collection of functions written in GNU Octave for analyzing queueing networks and Markov chains. Specifically, queueing contains functions for analyzing Jackson networks, open, closed or mixed product-form BCMP networks, and computation of performance bounds. The following algorithms have been implemented

queueing provides functions for analyzing the following kind of single-station queueing systems:

Functions for Markov chain analysis are also provided (discrete and continuous time Markov chains are supported):

The queueing toolbox is distributed under the terms of the GNU General Public License (GPL), version 3 or later (see Copying). You are encouraged to share this software with others, and make this package more useful by contributing additional functions and reporting problems. See Contributing Guidelines.

If you use the queueing toolbox in a technical paper, please cite it as:

Moreno Marzolla, The qnetworks Toolbox: A Software Package for Queueing Networks Analysis. Khalid Al-Begain, Dieter Fiems and William J. Knottenbelt, Editors, Proceedings 17th International Conference on Analytical and Stochastic Modeling Techniques and Applications (ASMTA 2010) Cardiff, UK, June 14–16, 2010, volume 6148 of Lecture Notes in Computer Science, Springer, pp. 102–116, ISBN 978-3-642-13567-5

If you use BibTeX, this is the citation block:

@inproceedings{queueing,
  author    = {Moreno Marzolla},
  title     = {The qnetworks Toolbox: A Software Package for Queueing 
               Networks Analysis},
  booktitle = {Analytical and Stochastic Modeling Techniques and 
               Applications, 17th International Conference, 
               ASMTA 2010, Cardiff, UK, June 14-16, 2010. Proceedings},
  editor    = {Khalid Al-Begain and Dieter Fiems and William J. Knottenbelt},
  year      = {2010},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {6148},
  pages     = {102--116},
  ee        = {http://dx.doi.org/10.1007/978-3-642-13568-2_8},
  isbn      = {978-3-642-13567-5}
}

An early draft of the paper above is available as Technical Report UBLCS-2010-04, February 2010, Department of Computer Science, University of Bologna, Italy.


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2 Installing the queueing toolbox


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2.1 Installation through Octave package management system

The most recent version of queueing is 1.0.0 and can be downloaded from Octave-forge

http://octave.sourceforge.net/

The package Web page is

http://www.moreno.marzolla.name/software/queueing/

To install queueing in the system-wide location, such that all functions are automatically available when Octave starts, you can use ‘pkg install’ command. At the Octave prompt, type the following:

     octave:1> pkg install queueing-1.0.0.tar.gz

(Note: you may need to start Octave as root in order to allow the installation to copy the files to the target locations). After this, all functions will be readily available each time Octave starts, without the need to tweak the search path. To uninstall queueing, use the ‘pkg uninstall queueing’ command.

If you do not have root access, you can do a local installation by issuing the following command at the Octave prompt:

     octave:1> pkg install -local queueing-1.0.0.tar.gz

This will install queueing within the user's home directory, and the package will be available to that user only. Note: Octave version 3.2.3 as shipped with Ubuntu 10.04 seems to ignore -local and always tries to install the package on the system directory.


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2.2 Manual installation

If you want to install queueing in a custom location, you can download the source tarball from the URL above, and unpack it somewhere:

     tar xfz queueing-1.0.0.tar.gz
     cd queueing-1.0.0/

Copy all .m files from the inst/ directory to some target location. Then, you can start Octave with the -p option to add the target location to the search path, so that Octave will find all queueing functions automatically:

     octave -p /path/to/queueing

For example, if all queueing m-files are in /usr/local/queueing, you can start Octave as follows:

     octave -p /usr/local/queueing

If you want, you can add the following line to ~/.octaverc:

     addpath("/path/to/queueing");

so that the path /usr/local/queueing is automatically added to the search path each time Octave is started, and you no longer need to specify the -p option on the command line.


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2.3 Content of the source distribution

The queueing package source code in the Subversion repository contains the following subdirectories (some of which are not included in the installation archive):

doc/
Documentation source. Most of the documentation is extracted from the comment blocks of individual function files from the inst/ directory.
inst/
This directory contains the m-files which implement the various Queueing Network algorithms provided by queueing. As a notational convention, the names of source files containing functions for Queueing Networks start with the ‘qn’ prefix; the name of source files containing functions for Continuous-Time Markov Chains (CTMSs) start with the ‘ctmc’ prefix, and the names of files containing functions for Discrete-Time Markov Chains (DTMCs) start with the ‘dtmc’ prefix.
test/
This directory contains the test functions used to invoke all tests on all function files.
scripts/
This directory contains some utility scripts mostly from GNU Octave, which extract the documentation from the specially-formatted comments in the m-files.
examples/
This directory contains examples which are automatically extracted from the ‘demo’ blocks of the function files.
devel/
This directory contains function files which are either not working properly, or need additional testing before they are moved to the inst/ directory.

The queueing package ships with a Makefile which can be used to produce the documentation (in PDF and HTML format), and automatically execute all function tests. Specifically, the following targets are defined:

all
Running ‘make’ (or ‘make all’) on the top-level directory builds the programs used to extract the documentation from the comments embedded in the m-files, and then produce the documentation in PDF and HTML format (doc/queueing.pdf and doc/queueing.html, respectively).
check
Running ‘make check’ will execute all tests contained in the m-files. If you modify the code of any function in the inst/ directory, you should run the tests to ensure that no errors have been introduced. You are also encouraged to contribute new tests, especially for functions which are not adequately validated.
clean
distclean
dist
The ‘make clean’, ‘make distclean’ and ‘make dist’ commands are used to clean up the source directory and prepare the distribution archive in compressed tar format.


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2.4 Using the queueing toolbox

You can use all functions by simply invoking their name with the appropriate parameters; the queueing package should display an error message in case of missing/wrong parameters. You can display the help text for any function using the help command. For example:

     octave:2> help qnmvablo

prints the documentation for the qnmvablo function. Additional information can be found in the queueing manual, which is available in PDF format in doc/queueing.pdf and in HTML format in doc/queueing.html.

Within GNU Octave, you can also run the test and demo blocks associated to the functions, using the test and demo commands respectively. To run all the tests of, say, the qnmvablo function:

     octave:3> test qnmvablo
     -| PASSES 4 out of 4 tests

To execute the demos of the qnclosed function, use the following:

     octave:4> demo qnclosed


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3 Introduction and Getting Started

In this chapter we give some usage examples of the queueing package. The reader is assumed to be familiar with Queueing Networks (although some basic terminology and notation will be given here). Additional usage examples are embedded in most of the function files; to display and execute the demos associated with function fname you can type demo fname at the Octave prompt. For example

     demo qnclosed

executes all demos (if any) for the qnclosed function.


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3.1 Analysis of Closed Networks

Let us consider a simple closed network with K=3 service centers. Each center is of type M/M/1–FCFS. We denote with S_i the average service time at center i, i=1, 2, 3. Let S_1 = 1.0, S_2 = 2.0 and S_3 = 0.8. The routing of jobs within the network is described with a routing probability matrix P. Specifically, a request completing service at center i is enqueued at center j with probability P_ij. Let us assume the following routing probability matrix:

         [ 0  0.3  0.7 ]
     P = [ 1  0    0   ]
         [ 1  0    0   ]

For example, according to matric P a job completing service at center 1 is routed to center 2 with probability 0.3, and is routed to center 3 with probability 0.7.

The network above can be analyzed with the qnclosed function; if there is just a single class of requests, as in the example above, qnclosed calls qnclosedsinglemva which implements the Mean Value Analysys (MVA) algorithm for single-class, product-form network.

qnclosed requires the following parameters:

N
Number of requests in the network (since we are considering a closed network, the number of requests is fixed)
S
Array of average service times at the centers: S(k) is the average service time at center k.
V
Array of visit ratios: V(k) is the average number of visits to center k.

As can be seen, we must compute the visit ratios (or visit counts) V_k for each center k. The visit counts satisfy the following equations:

     V_j = sum_i V_i P_ij

We can compute V_k from the routing probability matrix P_ij using the qnvisits function:

     P = [0 0.3 0.7; 1 0 0; 1 0 0];
     V = qnvisits(P)
        ⇒ V = 1.00000 0.30000 0.70000

We can check that the computed values satisfy the above equation by evaluating the following expression:

     V*P
          ⇒ ans = 1.00000 0.30000 0.70000

which is equal to V. Hence, we can analyze the network for a given population size N (for example, N=10) as follows:

     N = 10;
     S = [1 2 0.8];
     P = [0 0.3 0.7; 1 0 0; 1 0 0];
     V = qnvisits(P);
     [U R Q X] = qnclosed( N, S, V )
        ⇒ U = 0.99139 0.59483 0.55518
        ⇒ R = 7.4360  4.7531  1.7500
        ⇒ Q = 7.3719  1.4136  1.2144
        ⇒ X = 0.99139 0.29742 0.69397

The output of qnclosed includes the vector of utilizations U_k at center k, response time R_k, average number of customers Q_k and throughput X_k. In our example, the throughput of center 1 is X_1 = 0.99139, and the average number of requests in center 3 is Q_3 = 1.2144. The utilization of center 1 is U_1 = 0.99139, which is the higher value among the service centers. Tus, center 1 is the bottleneck device.

This network can also be analyzed with the qnsolve function. qnsolve can handle open, closed or mixed networks, and allows the network to be described in a very flexible way. First, let Q1, Q2 and Q3 be the variables describing the service centers. Each variable is instantiated with the qnmknode function.

     Q1 = qnmknode( "m/m/m-fcfs", 1 );
     Q2 = qnmknode( "m/m/m-fcfs", 2 );
     Q3 = qnmknode( "m/m/m-fcfs", 0.8 );

The first parameter of qnmknode is a string describing the type of the node. Here we use "m/m/m-fcfs" to denote a M/M/m–FCFS center. The second parameter gives the average service time. An optional third parameter can be used to specify the number m of service centers. If omitted, it is assumed m=1 (single-server node).

Now, the network can be analyzed as follows:

     N = 10;
     V = [1 0.3 0.7];
     [U R Q X] = qnsolve( "closed", N, { Q1, Q2, Q3 }, V )
        ⇒ U = 0.99139 0.59483 0.55518
        ⇒ R = 7.4360  4.7531  1.7500
        ⇒ Q = 7.3719  1.4136  1.2144
        ⇒ X = 0.99139 0.29742 0.69397

Of course, we get exactly the same results. Other functions can be used for closed networks, see Algorithms for Product-Form QNs.


Previous: Analysis of Closed Networks, Up: Getting Started

3.2 Analysis of Open Networks

Open networks can be analyzed in a similar way. Let us consider an open network with K=3 service centers, and routing probability matrix as follows:

         [ 0  0.3  0.5 ]
     P = [ 1  0    0   ]
         [ 1  0    0   ]

In this network, requests can leave the system from center 1 with probability (1-(0.3+0.5) = 0.2. We suppose that external jobs arrive at center 1 with rate \lambda_1 = 0.15; there are no arrivals at centers 2 and 3.

Similarly to closed networks, we first need to compute the visit counts V_k to center k. Again, we use the qnvisits function as follows:

     P = [0 0.3 0.5; 1 0 0; 1 0 0];
     lambda = [0.15 0 0];
     V = qnvisits(P, lambda)
        ⇒ V = 5.00000 1.50000 2.50000

where lambda(k) is the arrival rate at center k, and P is the routing matrix. The visit counts V_k for open networks satisfy the following equation:

     V_j = sum_i V_i P_ij

where P_0j is the probability of an external arrival to center j. This can be computed as:

Assuming the same service times as in the previous example, the network can be analyzed with the qnopen function, as follows:

     S = [1 2 0.8];
     [U R Q X] = qnopen( sum(lambda), S, V )
        ⇒ U = 0.75000 0.45000 0.30000
        ⇒ R = 4.0000  3.6364  1.1429
        ⇒ Q = 3.00000 0.81818 0.42857
        ⇒ X = 0.75000 0.22500 0.37500

The first parameter of the qnopen function is the (scalar) aggregate arrival rate.

Again, it is possible to use the qnsolve high-level function:

     Q1 = qnmknode( "m/m/m-fcfs", 1 );
     Q2 = qnmknode( "m/m/m-fcfs", 2 );
     Q3 = qnmknode( "m/m/m-fcfs", 0.8 );
     lambda = [0.15 0 0];
     [U R Q X] = qnsolve( "open", sum(lambda), { Q1, Q2, Q3 }, V )
        ⇒ U = 0.75000 0.45000 0.30000
        ⇒ R = 4.0000  3.6364  1.1429
        ⇒ Q = 3.00000 0.81818 0.42857
        ⇒ X = 0.75000 0.22500 0.37500


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4 Markov Chains


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4.1 Discrete-Time Markov Chains


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4.1.1 Stationary Probability

— Function File: p = dtmc (P)
— Function File: p = dtmc (P, n, p0)

With a single argument, compute the steady-state probability vector p(1), ..., p(N) for a Discrete-Time Markov Chain given the N \times N transition probability matrix P. With three arguments, compute the probability vector p(1), ..., p(N) after n steps, given initial probability vector p0 at time 0.

INPUTS

P
P(i,j) is the transition probability from state i to state j. P must be an irreducible stochastic matrix, which means that the sum of each row must be 1 (\sum_j=1^N P_i j = 1), and the rank of P must be equal to its dimension.
n
Step at which to compute the transient probability
p0
p0(i) is the probability that at step 0 the system is in state i.

OUTPUTS

p
If this function is invoked with a single argument, p(i) is the steady-state probability that the system is in state i. p satisfies the equations p = p\bf P and \sum_i=1^N p_i = 1. If this function is invoked with three arguments, p(i) is the marginal probability that the system is in state i at step n, given the initial probabilities p0(i) that the initial state is i.


Previous: DTMC Stationary Probability, Up: Discrete-Time Markov Chains

4.1.2 First Passage Times

The First Passage Time M_i j is defined as the average number of transitions needed to visit state j for the first time, starting from state i. Matrix \bf M satisfies the property that

                ___
               \
     M_ij = 1 + >   P_ij * M_kj
               /___
               k!=j

— Function File: M = dtmc_fpt (P)
— Function File: m = dtmc_fpt (P, i, j)

If called with a single argument, computes the mean first passage times M(i,j), that are the average number of transitions before state j is reached, starting from state i, for all 1 \leq i, j \leq N. If called with three arguments, returns the single value m = M(i,j).

INPUTS

P
P(i,j) is the transition probability from state i to state j. P must be an irreducible stochastic matrix, which means that the sum of each row must be 1 (\sum_j=1^N P_i j = 1), and the rank of P must be equal to its dimension.
i
Initial state.
j
Destination state. If j is a vector, returns the mean first passage time to any state in j.

OUTPUTS

M
If this function is called with a single argument, the result M(i,j) is the average number of transitions before state j is reached for the first time, starting from state i.
m
If this function is called with three arguments, the result m is the average number of transitions before state j is visited for the first time, starting from state i.


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4.2 Continuous-Time Markov Chains


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4.2.1 Stationary Probability

— Function File: p = ctmc (Q)
— Function File: p = ctmc (Q, t. q0)

With a single argument, compute the stationary state occupancy probability vector p(1), ..., p(N) for a Continuous-Time Markov Chain with infinitesimal generator matrix Q of size N \times N. With three arguments, compute the state occupancy probabilities p(1), ..., p(N) at time t, given initial state occupancy probabilities p0 at time 0.

INPUTS

Q
Infinitesimal generator matrix. Q is a N \times N square matrix where Q(i,j) is the transition rate from state i to state j, for 1 ≤ i \neq j ≤ N. Transition rates must be nonnegative, and \sum_j=1^N Q_i j = 0
t
Time at which to compute the transient probability
p0
p0(i) is the probability that the system is in state i at time 0 .

OUTPUTS

p
If this function is invoked with a single argument, p(i) is the steady-state probability that the system is in state i, i = 1, ..., N. The vector p satisfies the equation p\bf Q = 0 and \sum_i=1^N p_i = 1. If this function is invoked with three arguments, p(i) is the probability that the system is in state i at time t, given the initial occupancy probabilities q0.

EXAMPLE

Consider a two-state CTMC such that transition rates between states are equal to 1. This can be solved as follows:

      Q = [ -1  1; \
             1 -1  ];
      q = ctmc(Q)
⇒ q = 0.50000 0.50000


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4.2.2 Birth-Death process

— Function File: p = ctmc_bd (birth, death)

Compute the steady-state solution of a birth-death process with state space (1, ..., N).

INPUTS

birth
Vector with N-1 elements, where birth(i) is the transition rate from state i to state i+1.
death
Vector with N-1 elements, where death(i) is the transition rate from state i+1 to state i.

OUTPUTS

p
p(i) is the steady-state probability that the system is in state i, i=1, ..., N.


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4.2.3 Expected Sojourn Time

Given a N state continuous-time Markov Chain with infinitesimal generator matrix \bf Q, we define the vector \bf L(t) = (L_1(t), L_2(t), \ldots L_N(t)) such that L_i(t) is the expected sojourn time in state i during the interval [0,t), assuming that the initial occupancy probability at time 0 was \bf \pi(0). Then, \bf L(t) is the solution of the following differential equation:

      dL
      --(t) = L(t) Q + pi(0),    L(0) = 0
      dt

The function ctmc_exps can be used to compute \bf L(t), by using the lsode Octave function to solve the above linear differential equation.

— Function File: L = ctmc_exps (Q, tt, p)

Compute the expected total time L(t,j) spent in state j during the time interval [0,tt(t)), assuming that at time 0 the state occupancy probability was p.

INPUTS

Q
Infinitesimal generator matrix. Q(i,j) is the transition rate from state i to state j, 1 ≤ i \neq j ≤ N. The matrix Q must also satisfy the condition sum(Q,2) == 0
tt
This parameter is a vector used for numerical integration. The first element tt(1) must be 0, and the last element tt(end) must be the upper bound of the interval [0,t) of interest (tt(end) == t).
p
p(i) is the probability that at time 0 the system was in state i, for all i = 1, ..., N

OUTPUTS

L
L(t,j) is the expected time spent in state j during the interval [0,tt(t)). 1 ≤ t ≤ length(tt)

EXAMPLE

Let us consider a pure-birth, 4-states CTMC such that the transition rate from state i to state i+1 is \lambda_i = i \lambda (i=1, 2, 3), with \lambda = 0.5. The following code computes the expected sojourn time in state i, given the initial occupancy probability p_0=(1,0,0,0).

      lambda = 0.5;
      N = 4;
      birth = lambda*linspace(1,N-1,N-1);
      death = zeros(1,N-1);
      Q = diag(birth,1)+diag(death,-1);
      Q -= diag(sum(Q,2));
      tt = linspace(0,10,100);
      p0 = zeros(1,N); p0(1)=1;
      L = ctmc_exps(Q,tt,p0);
      plot( tt, L(:,1), ";State 1;", "linewidth", 2, \
            tt, L(:,2), ";State 2;", "linewidth", 2, \
            tt, L(:,3), ";State 3;", "linewidth", 2, \
            tt, L(:,4), ";State 4 (absorbing);", "linewidth", 2);
      legend("location","northwest");
      xlabel("Time");
      ylabel("Expected sojourn time");


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4.2.4 Time-Averaged Expected Sojourn Time

— Function File: M = ctmc_taexps (Q, tt, p)

Compute the time-averaged sojourn time M(t,j), defined as the fraction of the time interval [0,tt(t)) spent in state j, assuming that at time 0 the state occupancy probability was p.

INPUTS

Q
Infinitesimal generator matrix. Q(i,j) is the transition rate from state i to state j, 1 ≤ i \neq j ≤ N. The matrix Q must also satisfy the condition sum(Q,2) == 0
tt
This parameter is a vector used for numerical integration of the sujourn time. The first element tt(1) must be slightly larger than 0, and the last element tt(end) must be the upper limit of the interval [0,t) of interest (tt(end) == t). This vector is used by the ODE solver to compute the solution M.
p
p(i) is the probability that, at time 0, the system was in state i, for all i = 1, ..., N

OUTPUTS

M
M(t,j) is the expected fraction of time spent in state j during the interval [0,tt(t)) assuming that the state occupancy probability at time zero was p. 1 ≤ t ≤ length(tt)

EXAMPLE

      lambda = 0.5;
      N = 4;
      birth = lambda*linspace(1,N-1,N-1);
      death = zeros(1,N-1);
      Q = diag(birth,1)+diag(death,-1);
      Q -= diag(sum(Q,2));
      t = linspace(1e-3,50,500);
      p = zeros(1,N); p(1)=1;
      M = ctmc_taexps(Q,t,p);
      plot(t, M(:,1), ";State 1;", "linewidth", 2, \
           t, M(:,2), ";State 2;", "linewidth", 2, \
           t, M(:,3), ";State 3;", "linewidth", 2, \
           t, M(:,4), ";State 4 (absorbing);", "linewidth", 2 );
      legend("location","east");
      xlabel("Time");
      ylabel("Time-averaged Expected sojourn time");


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4.2.5 Expected Time to Absorption

If we consider a Markov Chain with absorbing states, it is possible to define the expected time to absorption as the expected time until the system goes into an absorbing state. More specifically, let us suppose that A is the set of transient (i.e., non-absorbing) states of a CTMC with N states and infinitesimal generator matrix \bf Q. The expected time to absorption \bf L_A(\infty) is defined as the solution of the following equation:

     L_A( inf ) Q_A = -pi_A(0)

where \bf Q_A is the restriction of matrix \bf Q to only states in A, and \bf \pi_A(0) is the initial state occupancy probability at time 0, restricted to states in A.

— Function File: t = ctmc_mtta (Q, p)

Compute the Mean-Time to Absorption (MTTA) starting from initial occupancy probability p at time 0. If there are no absorbing states, this function fails with an error.

INPUTS

Q
N \times N infinitesimal generator matrix. Q(i,j) is the transition rate from state i to state j, i \neq j. The matrix Q must satisfy the condition \sum_j=1^N Q_i j = 0
p
p(i) is the probability that the system is in state i at time 0, for each i=1, ..., N

OUTPUTS

t
Mean time to absorption of the process represented by matrix Q. If there are no absorbing states, this function fails.

EXAMPLE

Let us consider a simple model of a redundant disk array. We assume that the array is made of 5 independent disks, such that the array can tolerate up to 2 disk failures without losing data. If three or more disks break, the array is dead and unrecoverable. We want to estimate the Mean-Time-To-Failure (MTTF) of the disk array.

We model this system as a 4 states Markov chain with state space \ 2, 3, 4, 5 \. State i denotes the fact that exactly i disks are active; state 2 is absorbing. Let \mu be the failure rate of a single disk. The system starts in state 5 (all disks are operational). We use a pure death process, with death rate from state i to state i-1 is \mu i, for i = 3, 4, 5).

The MTTF of the disk array is the MTTA of the Markov Chain, and can be computed with the following expression:

      mu = 0.01;
      death = [ 3 4 5 ] * mu;
      Q = diag(death,-1);
      Q -= diag(sum(Q,2));
      t = ctmc_mtta(Q,[0 0 0 1])
⇒ t = 78.333

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998.


Previous: Expected Time to Absorption, Up: Continuous-Time Markov Chains

4.2.6 First Passage Times

— Function File: M = ctmc_fpt (Q)
— Function File: m = ctmc_fpt (Q, i, j)

If called with a single argument, computes the mean first passage times M(i,j), the average times before state j is reached, starting from state i, for all 1 \leq i, j \leq N. If called with three arguments, returns the single value m = M(i,j).

INPUTS

Q
Infinitesimal generator matrix. Q is a N \times N square matrix where Q(i,j) is the transition rate from state i to state j, for 1 ≤ i \neq j ≤ N. Transition rates must be nonnegative, and \sum_j=1^N Q_i j = 0
i
Initial state.
j
Destination state. If j is a vector, returns the mean first passage time to any state in j.

OUTPUTS

M
If this function is called with a single argument, the result M(i,j) is the average time before state j is visited for the first time, starting from state i.
m
If this function is called with three arguments, the result m is the average time before state j is visited for the first time, starting from state i.


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5 Single Station Queueing Systems

Single Station Queueing Systems contain a single station, and are thus quite easy to analyze. The queueing package contains functions for handling the following types of queues:

The functions which analyze the queues above can be used as building blocks for analyzing Queueing Networks. For example, Jackson networks can be solved by computing the aggregate arrival rates to each node, and then solving each node in isolation as if it were a single station queueing system.


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5.1 The M/M/1 System

The M/M/1 system is made of a single server connected to an unlimited FCFS queue. The mean arrival rate is Poisson with arrival rate \lambda; the service time is exponentially distributed with average service rate \mu. The system is stable if \lambda < \mu.

— Function File: [U, R, Q, X, p0] = qnmm1 (lambda, mu)

Compute utilization, response time, average number of requests and throughput for a M/M/1 queue.

INPUTS

lambda
Arrival rate (lambda > 0).
mu
Service rate (mu > lambda).

OUTPUTS

U
Server utilization
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput. If the system is ergodic, we will always have X = lambda
p0
Steady-state probability that there are no requests in the system.

lambda and mu can be vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmmm, qnmminf, qnmmmk.

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 6.3.


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5.2 The M/M/m System

The M/M/m system is similar to the M/M/1 system, except that there are m \geq 1 identical servers connected to a single queue. Thus, at most m requests can be served at the same time. The M/M/m system can be seen as a single server with load-dependent service rate \mu(n), which is a function of the number n of nodes in the center:

     mu(n) = min(m,n)*mu

— Function File: [U, R, Q, X, p0, pm] = qnmmm (lambda, mu)
— Function File: [U, R, Q, X, p0, pm] = qnmmm (lambda, mu, m)

Compute utilization, response time, average number of requests in service and throughput for a M/M/m queue, a queueing system with m identical service centers connected to a single queue.

INPUTS

lambda
Arrival rate (lambda>0).
mu
Service rate (mu>lambda).
m
Number of servers (m ≥ 1). If omitted, it is assumed m=1.

OUTPUTS

U
Service center utilization, U = \lambda / (m \mu).
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput. If the system is ergodic, we will always have X = lambda
p0
Steady-state probability that there are 0 requests in the system
pm
Steady-state probability that an arriving request has to wait in the queue

lambda, mu and m can be vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmm1,qnmminf,qnmmmk.

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 6.5.


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5.3 The M/M/inf System

The M/M/\infty system is similar to the M/M/m system, except that there are infinitely many identical servers (that is, m = \infty). Each new request is assigned to a new server, so that queueing never occurs. The M/M/\infty system is always stable.

— Function File: [U, R, Q, X, p0] = qnmminf (lambda, mu)

Compute utilization, response time, average number of requests and throughput for a M/M/\infty queue. This is a system with an infinite number of identical servers. Note that a M/M/\infty system is always stable, regardless the values of the arrival and service rates.

INPUTS

lambda
Arrival rate (lambda>0).
mu
Service rate (mu>0).

OUTPUTS

U
Traffic intensity (defined as \lambda/\mu). Note that this is different from the utilization, which in the case of M/M/\infty centers is always zero.


R
Service center response time.
Q
Average number of requests in the system (which is equal to the traffic intensity \lambda/\mu).
X
Throughput (which is always equal to X = lambda).
p0
Steady-state probability that there are no requests in the system

lambda and mu can be vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmm1,qnmmm,qnmmmk.

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 6.4.


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5.4 The M/M/1/K System

In a M/M/1/K finite capacity system there can be at most k jobs at any time. If a new request tries to join the system when there are already K other requests, the arriving request is lost. The queue has K-1 slots. The M/M/1/K system is always stable, regardless of the arrival and service rates \lambda and \mu.

— Function File: [U, R, Q, X, p0, pK] = qnmm1k (lambda, mu, K)

Compute utilization, response time, average number of requests and throughput for a M/M/1/K finite capacity system. In a M/M/1/K queue there is a single server; the maximum number of requests in the system is K, and the maximum queue length is K-1.

INPUTS

lambda
Arrival rate (lambda>0).
mu
Service rate (mu>0).
K
Maximum number of requests allowed in the system (K ≥ 1).

OUTPUTS

U
Service center utilization, which is defined as U = 1-p0
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput
p0
Steady-state probability that there are no requests in the system
pK
Steady-state probability that there are K requests in the system (i.e., that the system is full)

lambda, mu and K can be vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmm1,qnmminf,qnmmm.


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5.5 The M/M/m/K System

The M/M/m/K finite capacity system is similar to the M/M/1/k system except that the number of servers is m, where 1 \leq m \leq K. The queue is made of K-m slots. The M/M/m/K system is always stable.

— Function File: [U, R, Q, X, p0, pK] = qnmmmk (lambda, mu, m, K)

Compute utilization, response time, average number of requests and throughput for a M/M/m/K finite capacity system. In a M/M/m/K system there are m \geq 1 identical service centers sharing a fixed-capacity queue. At any time, at most K ≥ m requests can be in the system. The maximum queue length is K-m. This function generates and solves the underlying CTMC.

INPUTS

lambda
Arrival rate (lambda>0).
mu
Service rate (mu>0).
m
Number of servers (m ≥ 1).
K
Maximum number of requests allowed in the system, including those inside the service centers (Km).

OUTPUTS

U
Service center utilization
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput
p0
Steady-state probability that there are no requests in the system.
pK
Steady-state probability that there are K requests in the system (i.e., probability that the system is full).

lambda, mu, m and K can be either scalars, or vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmm1,qnmminf,qnmmm.

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 6.6.


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5.6 The Asymmetric M/M/m System

The Asymmetric M/M/m system contains m servers connected to a single queue. Differently from the M/M/m system, in the asymmetric M/M/m each server may have a different service time.

— Function File: [U, R, Q, X] = qnammm (lambda, mu)

Compute approximate utilization, response time, average number of requests in service and throughput for an asymmetric M/M/m queue. In this system there are m different service centers connected to a single queue. Each server has its own (possibly different) service rate. If there is more than one server available, requests are routed to a randomly-chosen one.

INPUTS

lambda
Arrival rate (lambda>0).
mu
mu(i) is the service rate of server i, 1 ≤ i ≤ m. The system must be ergodic (lambda < sum(mu)).

OUTPUTS

U
Approximate service center utilization, U = \lambda / ( \sum_i \mu_i ).
R
Approximate service center response time
Q
Approximate number of requests in the system
X
Approximate service center throughput. If the system is ergodic, we will always have X = lambda
     
     
See also: qnmmm.

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998


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5.7 The M/G/1 System

— Function File: [U, R, Q, X, p0] = qnmg1 (lambda, xavg, x2nd)

Compute utilization, response time, average number of requests and throughput for a M/G/1 system. The service time distribution is described by its mean xavg, and by its second moment x2nd. The computations are based on results from L. Kleinrock, Queuing Systems, Wiley, Vol 2, and Pollaczek-Khinchine formula.

INPUTS

lambda
Arrival rate.
xavg
Average service time
x2nd
Second moment of service time distribution

OUTPUTS

U
Service center utilization
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput
p0
probability that there is not any request at system

lambda, xavg, t2nd can be vectors of the same size. In this case, the results will be vectors as well.

     
     
See also: qnmh1.


Previous: The M/G/1 System, Up: Single Station Queueing Systems

5.8 The M/H_m/1 System

— Function File: [U, R, Q, X, p0] = qnmh1 (lambda, mu, alpha)

Compute utilization, response time, average number of requests and throughput for a M/H_m/1 system. In this system, the customer service times have hyper-exponential distribution:

                 ___ m
                 \
          B(x) =  >  alpha(j) * (1-exp(-mu(j)*x))   x>0
                 /__
                     j=1

where \alpha_j is the probability that the request is served at phase j, in which case the average service rate is \mu_j. After completing service at phase j, for some j, the request exits the system.

INPUTS

lambda
Arrival rate.
mu
mu(j) is the phase j service rate. The total number of phases m is length(mu).
alpha
alpha(j) is the probability that a request is served at phase j. alpha must have the same size as mu.

OUTPUTS

U
Service center utilization
R
Service center response time
Q
Average number of requests in the system
X
Service center throughput


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6 Queueing Networks


Next: , Up: Queueing Networks

6.1 Introduction to QNs

Queueing Networks (QN) are a very simple yet powerful modeling tool which is used to analyze many kind of systems. In its simplest form, a QN is made of K service centers. Each service center i has a queue, which is connected to m_i (generally identical) servers. Customers (or requests) arrive at the service center, and join the queue if there is a slot available. Then, requests are served according to a (de)queueing policy. After service completes, the requests leave the service center.

The service centers for which m_i = \infty are called delay centers or infinite servers. If a service center has infinite servers, of course each new request will find one server available, so there will never be queueing.

Requests join the queue according to a queueing policy, such as:

FCFS
First-Come-First-Served
LCFS-PR
Last-Come-First-Served, Preemptive Resume
PS
Processor Sharing
IS
Infinite Server, there is an infinite number of identical servers so that each request always finds a server available, and there is no queueing

A population of requests or customers arrives to the system system, requesting service to the service centers. The request population may be open or closed. In open systems there is an infinite population of requests. New customers arrive from outside the system, and eventually leave the system. In closed systems there is a fixed population of request which continuously interacts with the system.

There might be a single class of requests, meaning that all requests behave in the same way (e.g., they spend the same average time on each particular server), or there might be multiple classes of requests.

6.1.1 Single class models

In single class models, all requests are indistinguishable and belong to the same class. This means that every request has the same average service time, and all requests move through the system with the same routing probabilities.

Model Inputs

\lambda_i
External arrival rate to service center i.
\lambda
Overall external arrival rate to the whole system: \lambda = \sum_i \lambda_i.
S_i
Average service time. S_i is the average service time on service center i. In other words, S_i is the average time from the instant in which a request is extracted from the queue and starts being service, and the instant at which service finishes and the request moves to another queue (or exits the system).
P_ij
Routing probability matrix. \bf P = P_ij is a K \times K matrix such that P_ij is the probability that a request completing service at server i will move directly to server j, The probability that a request leaves the system after service at service center i is 1-\sum_j=1^K P_ij.
V_i
Average number of visits. V_i is the average number of visits to the service center i. This quantity will be described shortly.

Model Outputs

U_i
Service center utilization. U_i is the utilization of service center i. The utilization is defined as the fraction of time in which the resource is busy (i.e., the server is processing requests).
R_i
Average response time. R_i is the average response time of service center i. The average response time is defined as the average time between the arrival of a customer in the queue, and the completion of service.
Q_i
Average number of customers. Q_i is the average number of requests in service center i. This includes both the requests in the queue, and the request being served.
X_i
Throughput. X_i is the throughput of service center i. The throughput is defined as the ratio of job completions (i.e., average number of jobs completed over a fixed interval of time).

Given these output parameters, additional performance measures can be computed as follows:

X
System throughput, X = X_1 / V_1
R
System response time, R = \sum_k=1^K R_k V_k
Q
Average number of requests in the system, Q = N-XZ

For open, single-class models, the scalar \lambda denotes the external arrival rate of requests to the system. The average number of visits satisfy the following equation:

     V == P0 + V*P;

where P_0 j is the probability that an external arrival goes to service center j. If \lambda_j is the external arrival rate to service center j, and \lambda = \sum_j \lambda_j is the overall external arrival rate, then P_0 j = \lambda_j / \lambda.

For closed models, the visit ratios satisfy the following equation:

     V(1) == 1 && V == V*P;

6.1.2 Multiple class models

In multiple class QN models, we assume that there exist C different classes of requests. Each request from class c spends on average time S_ck in service at service center k. For open models, we denote with \bf \lambda = \lambda_ck the arrival rates, where \lambda_ck is the external arrival rate of class c customers at service center k. For closed models, we denote with \bf N = (N_1, N_2, \ldots N_C) the population vector, where N_c is the number of class c requests in the system.

The transition probability matrix for these kind of networks will be a C \times K \times C \times K matrix \bf P = P_risj such that P_risj is the probability that a class r request which completes service at center i will join server j as a class s request.

Model input and outputs can be adjusted by adding additional indexes for the customer classes.

Model Inputs

\lambda_ci
External arrival rate of class-c requests to service center i
\lambda
Overall external arrival rate to the whole system: \lambda = \sum_c \sum_i \lambda_ci
S_ci
Average service time. S_ci is the average service time on service center i for class c requests.
P_risj
Routing probability matrix. \bf P = P_risj is a C \times K \times C \times K matrix such that P_risj is the probability that a class r request which completes service at server i will move to server j as a class s request.
V_ci
Average number of visits. V_ci is the average number of visits of class c requests to the service center i.

Model Outputs

U_ci
Utilization of service center i by class c requests. The utilization is defined as the fraction of time in which the resource is busy (i.e., the server is processing requests).
R_ci
Average response time experienced by class c requests on service center i. The average response time is defined as the average time between the arrival of a customer in the queue, and the completion of service.
Q_ci
Average number of class c requests on service center i. This includes both the requests in the queue, and the request being served.
X_ci
Throughput of service center i for class c requests. The throughput is defined as the rate of completion of class c requests.

It is possible to define aggregate performance measures as follows:

U_i
Utilization of service center i: Ui = sum(U,1);
R_c
System response time for class c requests: Rc = sum( V.*R, 1 );
Q_c
Average number of class c requests in the system: Qc = sum( Q, 2 );
X_c
Class c throughput: Xc = X(:,1) ./ V(:,1);

We can define the visit ratios V_sj for class s customers at service center j as follows:

V_sj = sum_r sum_i V_ri P_risj, for all s,j

while for open networks:

V_sj = P_0sj + sum_r sum_i V_ri P_risj, for all s,j

where P_0sj is the probability that an external arrival goes to service center j as a class-s request. If \lambda_sj is the external arrival rate of class s requests to service center j, and \lambda = \sum_s \sum_j \lambda_sj is the overall external arrival rate to the whole system, then P_0sj = \lambda_sj / \lambda.


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6.2 Generic Algorithms

The queueing package provides a couple of high-level functions for defining and solving QN models. These functions can be used to define a open or closed QN model (with single or multiple job classes), with arbitrary configuration and queueing disciplines. At the moment only product-form networks can be solved, See Algorithms for Product-Form QNs.

The network is defined by two parameters. The first one is the list of nodes, encoded as an Octave cell array. The second parameter is the visit ration V, which can be either a vector (for single-class models) or a two-dimensional matrix (for multiple-class models).

Individual nodes in the network are structures build using the qnmknode function.

— Function File: Q = qnmknode ("m/m/m-fcfs", S)
— Function File: Q = qnmknode ("m/m/m-fcfs", S, m)
— Function File: Q = qnmknode ("m/m/1-lcfs-pr", S)
— Function File: Q = qnmknode ("-/g/1-ps", S)
— Function File: Q = qnmknode ("-/g/1-ps", S, s2)
— Function File: Q = qnmknode ("-/g/inf", S)
— Function File: Q = qnmknode ("-/g/inf", S, s2)

Creates a node; this function can be used together with qnsolve. It is possible to create either single-class nodes (where there is only one customer class), or multiple-class nodes (where the service time is given per-class). Furthermore, it is possible to specify load-dependent service times.

INPUTS

S
Average service time. S can be either a scalar, a row vector, a column vector or a two-dimensional matrix.
  • If S is a scalar, it is assumed to be a load-independent, class-independent service time.
  • If S is a column vector, then S(c) is assumed to the the load-independent service time for class c customers.
  • If S is a row vector, then S(n) is assumed to be the class-independent service time at the node, when there are n requests.
  • Finally, if S is a two-dimensional matrix, then S(c,n) is assumed to be the class c service time when there are n requests at the node.

m
Number of identical servers at the node. Default is m=1.
s2
Squared coefficient of variation for the service time. Default is 1.0.

The returned struct Q should be considered opaque to the client.

     
     
See also: qnsolve.

After the network has been defined, it is possible to solve it using the qnsolve function. Note that this function is somewhat less efficient than those described in later sections, but generally easier to use.

— Function File: [U, R, Q, X] = qnsolve ("closed", N, QQ, V)
— Function File: [U, R, Q, X] = qnsolve ("closed", N, QQ, V, Z)
— Function File: [U, R, Q, X] = qnsolve ("open", lambda, QQ, V)
— Function File: [U, R, Q, X] = qnsolve ("mixed", lambda, N, QQ, V)

General evaluator of QN models. Networks can be open, closed or mixed; single as well as multiclass networks are supported.

INPUTS

N
Number of requests in the system for closed networks. For single-class networks, N must be a scalar. For multiclass networks, N(c) is the population size of closed class c.
lambda
External arrival rate (scalar) for open networks. For single-class networks, lambda must be a scalar. For multiclass networks, lambda(c) is the class c overall arrival rate.
QQ
List of queues in the network. This must be a cell array with N elements, such that QQ{i} is a struct produced by the qnmknode function.
Z
External delay ("think time") for closed networks. Default 0.

OUTPUTS

U
If i is a FCFS node, then U(i) is the utilization of service center i. If i is an IS node, then U(i) is the traffic intensity defined as X(i)*S(i).
R
R(i) is the average response time of service center i.
Q
Q(i) is the average number of customers in service center i.
X
X(i) is the throughput of service center i.

Note that for multiclass networks, the computed results are per-class utilization, response time, number of customers and throughput: U(c,k), R(c,k), Q(c,k), X(c,k),

EXAMPLE

Let us consider a closed, multiclass network with C=2 classes and K=3 service center. Let the population be M=(2, 1) (class 1 has 2 requests, and class 2 has 1 request). The nodes are as follows:

After defining the per-class visit count V such that V(c,k) is the visit count of class c requests to service center k. We can define and solve the model as follows:

      QQ = { qnmknode( "m/m/m-fcfs", [0.2 0.1 0.1; 0.2 0.1 0.1] ), \
             qnmknode( "-/g/1-ps", [0.4; 0.6] ), \
             qnmknode( "-/g/inf", [1; 2] ) };
      V = [ 1 0.6 0.4; \
            1 0.3 0.7 ];
      N = [ 2 1 ];
      [U R Q X] = qnsolve( "closed", N, QQ, V );


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6.3 Algorithms for Product-Form QNs

Product-form queueing networks fulfill the following assumptions:

6.3.1 Jackson Networks

Jackson networks satisfy the following conditions:

We define the joint probability vector \pi(k_1, k_2, \ldots k_N) as the steady-state probability that there are k_i requests at service center i, for all i=1,2, \ldots N. Jackson networks have the property that the joint probability is the product of the marginal probabilities \pi_i:

     joint_prob = prod( pi )

where \pi_i(k_i) is the steady-state probability that there are k_i requests at service center i.

— Function File: [U, R, Q, X] = qnjackson (lambda, S, P )
— Function File: [U, R, Q, X] = qnjackson (lambda, S, P, m )
— Function File: pr = qnjackson (lambda, S, P, m, k)

With three or four input parameters, this function computes the steady-state occupancy probabilities for a Jackson network. With five input parameters, this function computes the steady-state probability pi(j) that there are k(j) requests at service center j.

This function solves a subset of Jackson networks, with the following constraints:

INPUTS

lambda
lambda(i) is the external arrival rate to service center i. lambda must be a vector of length N, lambda(i) ≥ 0.
S
S(i) is the average service time on service center i S must be a vector of length N, S(i)>0.
P
P(i,j) is the probability that a job which completes service at service center i proceeds to service center j. P must be a matrix of size N \times N.
m
m(i) is the number of servers at service center i. If m(i) < 1, service center i is an infinite-server node. Otherwise, it is a regular FCFS queueing center with m(i) servers. If this parameter is omitted, default is m(i) = 1 for all i. If this parameter is a scalar, it will be promoted to a vector with the same size as lambda. Otherwise, m must be a vector of length N.
k
Compute the steady-state probability that there are k(i) requests at service center i. k must have the same length as lambda, with k(i) ≥ 0.

OUTPUT

U
If i is a FCFS node, then U(i) is the utilization of service center i. If i is an IS node, then U(i) is the traffic intensity defined as X(i)*S(i).
R
R(i) is the average response time of service center i.
Q
Q(i) is the average number of customers in service center i.
X
X(i) is the throughput of service center i.
pr
pr(i) is the steady state probability that there are k(i) requests at service center i.
     
     
See also: qnopen.

REFERENCES

This implementation is based on G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, pp. 284–287.

6.3.2 The Convolution Algorithm

According to the BCMP theorem, the state probability of a closed single class queueing network with K nodes and N requests can be expressed as:

     k = [k1, k2, ... kn]; population vector
     p = 1/G(N+1) \prod F(i,k);

Here \pi(k_1, k_2, \ldots k_K) is the joint probability of having k_i requests at node i, for all i=1,2, \ldots K.

The convolution algorithms computes the normalization constants G = (G(0), G(1), \ldots G(N)) for single-class, closed networks with N requests. The normalization constants are returned as vector G=[G(1), G(2), ... G(N+1)] where G(i+1) is the value of G(i) (remember that Octave uses 1-base vectors). The normalization constant can be used to compute all performance measures of interest (utilization, average response time and so on).

queueing implements the convolution algorithm, in the function qnconvolution and qnconvolutionld. The first one supports single-station nodes, multiple-station nodes and IS nodes. The second one supports networks with general load-dependent service centers.

— Function File: [U, R, Q, X, G] = qnconvolution (N, S, V)
— Function File: [U, R, Q, X, G] = qnconvolution (N, S, V, m)

This function implements the convolution algorithm for computing steady-state performance measures of product-form, single-class closed queueing networks. Load-independent service centers, multiple servers (M/M/m queues) and IS nodes are supported. For general load-dependent service centers, use the qnconvolutionld function instead.

INPUTS

N
Number of requests in the system (N>0).
S
S(k) is the average service time on center k (S(k) ≥ 0).
V
V(k) is the visit count of service center k (V(k) ≥ 0).
m
m(k) is the number of servers at center k. If m(k) < 1, center k is a delay center (IS); if m(k) ≥ 1, center k it is a regular M/M/m queueing center with m(k) identical servers. Default is m(k) = 1 for all k.

OUTPUT

U
U(k) is the utilization of center k. For IS nodes, U(k) is the traffic intensity.
R
R(k) is the average response time of center k.
Q
Q(k) is the average number of customers at center k.
X
X(k) is the throughput of center k.
G
Vector of normalization constants. G(n+1) contains the value of the normalization constant with n requests G(n), n=0, ..., N.
     
     
See also: qnconvolutionld.

EXAMPLE

The normalization constant G can be used to compute the steady-state probabilities for a closed single class product-form Queueing Network with K nodes. Let k=[k_1, k_2, ... k_K] be a valid population vector. Then, the steady-state probability p(i) to have k(i) requests at service center i can be computed as:

      k = [1 2 0];
      K = sum(k); # Total population size
      S = [ 1/0.8 1/0.6 1/0.4 ];
      m = [ 2 3 1 ];
      V = [ 1 .667 .2 ];
      [U R Q X G] = qnconvolution( K, S, V, m );
      p = [0 0 0]; # initialize p
      # Compute the probability to have k(i) jobs at service center i
      for i=1:3
        p(i) = (V(i)*S(i))^k(i) / G(K+1) * \
               (G(K-k(i)+1) - V(i)*S(i)*G(K-k(i)) );
        printf("k(%d)=%d prob=%f\n", i, k(i), p(i) );
      endfor
-| k(1)=1 prob=0.17975 -| k(2)=2 prob=0.48404 -| k(3)=0 prob=0.52779

NOTE

For a network with K service centers and N requests, this implementation of the convolution algorithm has time and space complexity O(NK).

REFERENCES

Jeffrey P. Buzen, Computational Algorithms for Closed Queueing Networks with Exponential Servers, Communications of the ACM, volume 16, number 9, september 1973, pp. 527–531. http://doi.acm.org/10.1145/362342.362345

This implementation is based on G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, pp. 313–317.

— Function File: [U, R, Q, X, G] = qnconvolutionld (N, S, V)

This function implements the convolution algorithm for product-form, single-class closed queueing networks with general load-dependent service centers.

This function computes steady-state performance measures for single-class, closed networks with load-dependent service centers using the convolution algorithm; the normalization constants are also computed. The normalization constants are returned as vector G=[G(1), ..., G(N+1)] where G(i+1) is the value of G(i).

INPUTS

N
Number of requests in the system (N>0).
S
S(k,n) is the mean service time at center k where there are n requests, 1 ≤ n ≤ N. S(k,n) = 1 / \mu_k,n, where \mu_k,n is the service rate of center k when there are n requests.
V
V(k) is the visit count of service center k (V(k) ≥ 0). The length of V is the number of servers K in the network.

OUTPUT

U
U(k) is the utilization of center k.
R
R(k) is the average response time at center k.
Q
Q(k) is the average number of customers in center k.
X
X(k) is the throughput of center k.
G
Normalization constants (vector). G(n+1) corresponds to G(n), as array indexes in Octave start from 1.
     
     
See also: qnconvolution.

REFERENCES

Herb Schwetman, Some Computational Aspects of Queueing Network Models, Technical Report CSD-TR-354, Department of Computer Sciences, Purdue University, feb, 1981 (revised). http://www.cs.purdue.edu/research/technical_reports/1980/TR%2080-354.pdf

M. Reiser, H. Kobayashi, On The Convolution Algorithm for Separable Queueing Networks, In Proceedings of the 1976 ACM SIGMETRICS Conference on Computer Performance Modeling Measurement and Evaluation (Cambridge, Massachusetts, United States, March 29–31, 1976). SIGMETRICS '76. ACM, New York, NY, pp. 109–117. http://doi.acm.org/10.1145/800200.806187

This implementation is based on G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, pp. 313–317. Function qnconvolutionld is slightly different from the version described in Bolch et al. because it supports general load-dependent centers (while the version in the book does not). The modification is in the definition of function F() in qnconvolutionld which has been made similar to function f_i defined in Schwetman, Some Computational Aspects of Queueing Network Models.

6.3.3 Open networks

— Function File: [U, R, Q, X] = qnopensingle (lambda, S, V)
— Function File: [U, R, Q, X] = qnopensingle (lambda, S, V, m)

Analyze open, single class BCMP queueing networks.

This function works for a subset of BCMP single-class open networks satisfying the following properties:

INPUTS

lambda
Overall external arrival rate (lambda>0).
S
S(k) is the average service time at center i (S(k)>0).
V
V(k) is the average number of visits to center k (V(k) ≥ 0).
m
m(k) is the number of servers at center i. If m(k) < 1, then service center k is a delay center (IS); otherwise it is a regular queueing center with m(k) servers. Default is m(k) = 1 for each k.

OUTPUTS

U
If k is a queueing center, U(k) is the utilization of center k. If k is an IS node, then U(k) is the traffic intensity defined as X(k)*S(k).
R
R(k) is the average response time of center k.
Q
Q(k) is the average number of requests at center k.
X
X(k) is the throughput of center k.
     
     
See also: qnopen,qnclosed,qnvisits.

From the results computed by this function, it is possible to derive other quantities of interest as follows:

EXAMPLE

      lambda = 3;
      V = [16 7 8];
      S = [0.01 0.02 0.03];
      [U R Q X] = qnopensingle( lambda, S, V );
      R_s = dot(R,V) # System response time
      N = sum(Q) # Average number in system
-| R_s = 1.4062 -| N = 4.2186

REFERENCES

G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998.

— Function File: [U, R, Q, X] = qnopenmulti (lambda, S, V)
— Function File: [U, R, Q, X] = qnopenmulti (lambda, S, V, m)

Exact analysis of open, multiple-class BCMP networks. The network can be made of single-server queueing centers (FCFS, LCFS-PR or PS) or delay centers (IS). This function assumes a network with K service centers and C customer classes.

INPUTS

lambda
lambda(c) is the external arrival rate of class c customers (lambda(c)>0).
S
S(c,k) is the mean service time of class c customers on the service center k (S(c,k)>0). For FCFS nodes, average service times must be class-independent.
V
V(c,k) is the average number of visits of class c customers to service center k (V(c,k) ≥ 0 ).
m
m(k) is the number of servers at service center k. Valid values are m(k) < 1 to denote a delay center (-/G/\infty), and m(k)==1 to denote a single server queueing center (M/M/1–FCFS, -/G/1–LCFS-PR or -/G/1–PS).

OUTPUTS

U
If k is a queueing center, then U(c,k) is the class c utilization of center k. If k is an IS node, then U(c,k) is the class c traffic intensity defined as X(c,k)*S(c,k).
R
R(c,k) is the class c response time at center k. The system response time for class c requests can be computed as dot(R, V, 2).
Q
Q(c,k) is the average number of class c requests at center k. The average number of class c requests in the system Qc can be computed as Qc = sum(Q, 2)
X
X(c,k) is the class c throughput at center k.
     
     
See also: qnopen,qnopensingle,qnvisits.

REFERENCES

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 7.4.1 ("Open Model Solution Techniques").

6.3.4 Closed Networks

— Function File: [U, R, Q, X, G] = qnclosedsinglemva (N, S, V)
— Function File: [U, R, Q, X, G] = qnclosedsinglemva (N, S, V, m)
— Function File: [U, R, Q, X, G] = qnclosedsinglemva (N, S, V, m, Z)

Analyze closed, single class queueing networks using the exact Mean Value Analysis (MVA) algorithm. The following queueing disciplines are supported: FCFS, LCFS-PR, PS and IS (Infinite Server). This function supports fixed-rate service centers or multiple server nodes. For general load-dependent service centers, use the function qnclosedsinglemvald instead.

Additionally, the normalization constant G(n), n=0, ..., N is computed; G(n) can be used in conjunction with the BCMP theorem to compute steady-state probabilities.

INPUTS

N
Population size (number of requests in the system, N ≥ 0). If N == 0, this function returns U = R = Q = X = 0
S
S(k) is the mean service time on server k (S(k)>0).
V
V(k) is the average number of visits to service center k (V(k) ≥ 0).
Z
External delay for customers (Z ≥ 0). Default is 0.
m
m(k) is the number of servers at center k (if m is a scalar, all centers have that number of servers). If m(k) < 1, center k is a delay center (IS); otherwise it is a regular queueing center (FCFS, LCFS-PR or PS) with m(k) servers. Default is m(k) = 1 for all k (each service center has a single server).

OUTPUTS

U
If k is a FCFS, LCFS-PR or PS node (m(k) == 1), then U(k) is the utilization of center k. If k is an IS node (m(k) < 1), then U(k) is the traffic intensity defined as X(k)*S(k).
R
R(k) is the response time at center k. The system response time Rsys can be computed as Rsys = N/Xsys - Z
Q
Q(k) is the average number of requests at center k. The number of requests in the system can be computed either as sum(Q), or using the formula N-Xsys*Z.
X
X(k) is the throughput of center k. The system throughput Xsys can be computed as Xsys = X(1) / V(1)
G
Normalization constants. G(n+1) corresponds to the value of the normalization constant G(n), n=0, ..., N as array indexes in Octave start from 1. G(n) can be used in conjunction with the BCMP theorem to compute steady-state probabilities.
     
     
See also: qnclosedsinglemvald.

From the results provided by this function, it is possible to derive other quantities of interest as follows:

EXAMPLE

      S = [ 0.125 0.3 0.2 ];
      V = [ 16 10 5 ];
      N = 20;
      m = ones(1,3);
      Z = 4;
      [U R Q X] = qnclosedsinglemva(N,S,V,m,Z);
      X_s = X(1)/V(1); # System throughput
      R_s = dot(R,V); # System response time
      printf("\t    Util      Qlen     RespT      Tput\n");
      printf("\t--------  --------  --------  --------\n");
      for k=1:length(S)
        printf("Dev%d\t%8.4f  %8.4f  %8.4f  %8.4f\n", k, U(k), Q(k), R(k), X(k) );
      endfor
      printf("\nSystem\t          %8.4f  %8.4f  %8.4f\n\n", N-X_s*Z, R_s, X_s );

REFERENCES

M. Reiser and S. S. Lavenberg, Mean-Value Analysis of Closed Multichain Queuing Networks, Journal of the ACM, vol. 27, n. 2, April 1980, pp. 313–322. http://doi.acm.org/10.1145/322186.322195

This implementation is described in R. Jain , The Art of Computer Systems Performance Analysis, Wiley, 1991, p. 577. Multi-server nodes are treated according to G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 8.2.1, "Single Class Queueing Networks".

— Function File: [U, R, Q, X] = qnclosedsinglemvald (N, S, V)
— Function File: [U, R, Q, X] = qnclosedsinglemvald (N, S, V, Z)

Exact MVA algorithm for closed, single class queueing networks with load-dependent service centers. This function supports FCFS, LCFS-PR, PS and IS nodes. For networks with only fixed-rate service centers and multiple-server nodes, the function qnclosedsinglemva is more efficient.

INPUTS

N
Population size (number of requests in the system, N ≥ 0). If N == 0, this function returns U = R = Q = X = 0
S
S(k,n) is the mean service time at center k where there are n requests, 1 ≤ n ≤ N. S(k,n) = 1 / \mu_k,n, where \mu_k,n is the service rate of center k when there are n requests.
V
V(k) is the average number of visits to service center k (V(k) ≥ 0).
Z
external delay ("think time", Z ≥ 0); default 0.

OUTPUTS

U
U(k) is the utilization of service center k. The utilization is defined as the probability that service center k is not empty, that is, U_k = 1-\pi_k(0) where \pi_k(0) is the steady-state probability that there are 0 jobs at service center k.
R
R(k) is the response time on service center k.
Q
Q(k) is the average number of requests in service center k.
X
X(k) is the throughput of service center k.

REFERENCES

M. Reiser and S. S. Lavenberg, Mean-Value Analysis of Closed Multichain Queuing Networks, Journal of the ACM, vol. 27, n. 2, April 1980, pp. 313–322. http://doi.acm.org/10.1145/322186.322195

This implementation is described in G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998, Section 8.2.4.1, “Networks with Load-Deèpendent Service: Closed Networks”.

— Function File: [U, R, Q, X] = qncmva (N, S, Sld, V)
— Function File: [U, R, Q, X] = qncmva (N, S, Sld, V, Z)

Implementation of the Conditional MVA (CMVA) algorithm, a numerically stable variant of MVA for load-dependent servers. CMVA is described in G. Casale, A Note on Stable Flow-Equivalent Aggregation in Closed Networks. The network is made of M service centers and a delay center. Servers 1, \ldots, M-1 are load-independent; server M is load-dependent.

INPUTS

N
Population size (number of requests in the system, N ≥ 0). If N == 0, this function returns U = R = Q = X = 0
S
S(k) is the mean service time on server k = 1, ..., M-1 (S(k) > 0).
Sld
Sld(n) is the mean service time on server M when there are n requests, n=1, ..., N. Sld(n) = 1 / \mu(n), where \mu(n) is the service rate at center N when there are n requests.
V
V(k) is the average number of visits to service center k= 1, ..., M (V(k) ≥ 0).
Z
External delay for customers (Z ≥ 0). Default is 0.

OUTPUTS

U
U(k) is the utilization of center k=1, ..., M
R
R(k) is the response time at center k=1, ..., M. The system response time Rsys can be computed as Rsys = N/Xsys - Z
Q
Q(k) is the average number of requests at center k=1, ..., M.
X
X(k) is the throughput of center k=1, ..., M.

REFERENCES

G. Casale. A note on stable flow-equivalent aggregation in closed networks. Queueing Syst. Theory Appl., 60:193–202, December 2008.

— Function File: [U, R, Q, X] = qnclosedsinglemvaapprox (N, S, V)
— Function File: [U, R, Q, X] = qnclosedsinglemvaapprox (N, S, V, m)
— Function File: [U, R, Q, X] = qnclosedsinglemvaapprox (N, S, V, m, Z)
— Function File: [U, R, Q, X] = qnclosedsinglemvaapprox (N, S, V, m, Z, tol)
— Function File: [U, R, Q, X] = qnclosedsinglemvaapprox (N, S, V, m, Z, tol, iter_max)

Analyze closed, single class queueing networks using the Approximate Mean Value Analysis (MVA) algorithm. This function is based on approximating the number of customers seen at center k when a new request arrives as Q_k(N) \times (N-1)/N. This function only handles single-server and delay centers; if your network contains general load-dependent service centers, use the function qnclosedsinglemvald instead.

INPUTS

N
Population size (number of requests in the system, N > 0).
S
S(k) is the mean service time on server k (S(k)>0).
V
V(k) is the average number of visits to service center k (V(k) ≥ 0).
m
m(k) is the number of servers at center k (if m is a scalar, all centers have that number of servers). If m(k) < 1, center k is a delay center (IS); if m(k) == 1, center k is a regular queueing center (FCFS, LCFS-PR or PS) with one server (default). This function does not support multiple server nodes (m(k) > 1).
Z
External delay for customers (Z ≥ 0). Default is 0.
tol
Stopping tolerance. The algorithm stops when the maximum relative difference between the new and old value of the queue lengths Q becomes less than the tolerance. Default is 10^-5.
iter_max
Maximum number of iterations (iter_max>0. The function aborts if convergenge is not reached within the maximum number of iterations. Default is 100.

OUTPUTS

U
If k is a FCFS, LCFS-PR or PS node (m(k) == 1), then U(k) is the utilization of center k. If k is an IS node (m(k) < 1), then U(k) is the traffic intensity defined as X(k)*S(k).
R
R(k) is the response time at center k. The system response time Rsys can be computed as Rsys = N/Xsys - Z
Q
Q(k) is the average number of requests at center k. The number of requests in the system can be computed either as sum(Q), or using the formula N-Xsys*Z.
X
X(k) is the throughput of center k. The system throughput Xsys can be computed as Xsys = X(1) / V(1)
     
     
See also: qnclosedsinglemva,qnclosedsinglemvald.

REFERENCES

This implementation is based on Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 6.4.2.2 ("Approximate Solution Techniques").

— Function File: [U, R, Q, X] = qnclosedmultimva (N, S )
— Function File: [U, R, Q, X] = qnclosedmultimva (N, S, V)
— Function File: [U, R, Q, X] = qnclosedmultimva (N, S, V, m)
— Function File: [U, R, Q, X] = qnclosedmultimva (N, S, V, m, Z)
— Function File: [U, R, Q, X] = qnclosedmultimva (N, S, P)
— Function File: [U, R, Q, X] = qnclosedmultimva (N, S, P, m)

Analyze closed, multiclass queueing networks with K service centers and C independent customer classes (chains) using the Mean Value Analysys (MVA) algorithm.

Queueing policies at service centers can be any of the following:

FCFS
(First-Come-First-Served) customers are served in order of arrival; multiple servers are allowed. For this kind of queueing discipline, average service times must be class-independent.
PS
(Processor Sharing) customers are served in parallel by a single server, each customer receiving an equal share of the service rate.
LCFS-PR
(Last-Come-First-Served, Preemptive Resume) customers are served in reverse order of arrival by a single server and the last arrival preempts the customer in service who will later resume service at the point of interruption.
IS
(Infinite Server) customers are delayed independently of other customers at the service center (there is effectively an infinite number of servers).
Note: If this function is called specifying the visit ratios V, class switching is not allowed.

If this function is called specifying the routing probability matrix P, then class switching is allowed; however, in this case all nodes are restricted to be fixed rate service centers or delay centers: multiple-server and general load-dependent centers are not supported.

INPUTS

N
N(c) is the number of class c requests in the system; N(c) ≥ 0. If class c has no requests (N(c) = 0), then U(c,k) = R(c,k) = Q(c,k) = X(c,k) = 0 for all k.
S
S(c,k) is the mean service time for class c customers at center k (S(c,k) ≥ 0). If service time at center k is class-dependent, then center #mathk is assumed to be of type -/G/1–PS (Processor Sharing). If center k is a FCFS node (m(k)>1), then the service times must be class-independent.
V
V(c,k) is the average number of visits of class c customers to service center k; V(c,k) ≥ 0, default is 1. If you pass this parameter, no class switching is not allowed
P
P(r,i,s,j) is the probability that a class r job completing service at center i is routed to center j as a class s job. If you pass this parameter, class switching is allowed.
m
If m(k)<1, then center k is assumed to be a delay center (IS node -/G/\infty). If m(k)==1, then service center k is a regular queueing center (M/M/1–FCFS, -/G/1–LCFS-PR or -/G/1–PS). Finally, if m(k)>1, center k is a M/M/m–FCFS center with m(k) identical servers. Default is m(k)=1 for each k.
Z
Z(c) is the class c external delay (think time); Z(c) ≥ 0. Default is 0.

OUTPUTS

U
If k is a FCFS, LCFS-PR or PS node, then U(c,k) is the class c utilization at center k. If k is an IS node, then U(c,k) is the class c traffic intensity at center k, defined as U(c,k) = X(c,k)*S(c,k).
R
R(c,k) is the class c response time at center k. The total class c system response time can be computed as dot(R, V, 2).
Q
Q(c,k) is the average number of class c requests at center k. The total number of requests at center k is sum(Q(:,k)). The total number of class c requests in the system is sum(Q(c,:)).
X
X(c,k) is the class c throughput at center k. The class c system throughput can be computed as X(c,1) / V(c,1).
     
     
See also: qnclosed, qnclosedmultimvaapprox.

NOTE

Given a network with K service centers, C job classes and population vector \bf N=(N_1, N_2, \ldots N_C), the MVA algorithm requires space O(C \prod_i (N_i + 1)). The time complexity is O(CK\prod_i (N_i + 1)). This implementation is slightly more space-efficient (see details in the code). While the space requirement can be mitigated by using some optimizations, the time complexity can not. If you need to analyze large closed networks you should consider the qnclosedmultimvaapprox function, which implements the approximate MVA algorithm. Note however that qnclosedmultimvaapprox will only provide approximate results.

REFERENCES

M. Reiser and S. S. Lavenberg, Mean-Value Analysis of Closed Multichain Queuing Networks, Journal of the ACM, vol. 27, n. 2, April 1980, pp. 313–322. http://doi.acm.org/10.1145/322186.322195

This implementation is based on G. Bolch, S. Greiner, H. de Meer and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998 and Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 7.4.2.1 ("Exact Solution Techniques").

— Function File: [U, R, Q, X] = qnclosedmultimvaapprox (N, S, V)
— Function File: [U, R, Q, X] = qnclosedmultimvaapprox (N, S, V, m)
— Function File: [U, R, Q, X] = qnclosedmultimvaapprox (N, S, V, m, Z)
— Function File: [U, R, Q, X] = qnclosedmultimvaapprox (N, S, V, m, Z, tol)
— Function File: [U, R, Q, X] = qnclosedmultimvaapprox (N, S, V, m, Z, tol, iter_max)

Analyze closed, multiclass queueing networks with K service centers and C customer classes using the approximate Mean Value Analysys (MVA) algorithm.

This implementation uses Bard and Schweitzer approximation. It is based on the assumption that the queue length at service center k with population set \bf N-\bf 1_c is approximately equal to the queue length with population set \bf N, times (n-1)/n:

          Q_i(N-1c) ~ (n-1)/n Q_i(N)

where \bf N is a valid population mix, \bf N-\bf 1_c is the population mix \bf N with one class c customer removed, and n = \sum_c N_c is the total number of requests.

This implementation works for networks made of infinite server (IS) nodes and single-server nodes only.

INPUTS

N
N(c) is the number of class c requests in the system (N(c)>0).
S
S(c,k) is the mean service time for class c customers at center k (S(c,k) ≥ 0).
V
V(c,k) is the average number of visits of class c requests to center k (V(c,k) ≥ 0).
m
m(k) is the number of servers at service center k. If m(k) < 1, then the service center k is assumed to be a delay center (IS). If m(k) == 1, service center k is a regular queueing center (FCFS, LCFS-PR or PS) with a single server node. If omitted, each service center has a single server. Note that multiple server nodes are not supported.
Z
Z(c) is the class c external delay. Default is 0.
tol
Stopping tolerance (tol>0). The algorithm stops if the queue length computed on two subsequent iterations are less than tol. Default is 10^-5.
iter_max
Maximum number of iterations (iter_max>0. The function aborts if convergenge is not reached within the maximum number of iterations. Default is 100.

OUTPUTS

U
If k is a FCFS, LCFS-PR or PS node, then U(c,k) is the utilization of class c requests on service center k. If k is an IS node, then U(c,k) is the class c traffic intensity at device k, defined as U(c,k) = X(c)*S(c,k)
R
R(c,k) is the response time of class c requests at service center k.
Q
Q(c,k) is the average number of class c requests at service center k.
X
X(c,k) is the class c throughput at service center k.
     
     
See also: qnclosed.

REFERENCES

Y. Bard, Some Extensions to Multiclass Queueing Network Analysis, proc. 4th Int. Symp. on Modelling and Performance Evaluation of Computer Systems, feb. 1979, pp. 51–62.

P. Schweitzer, Approximate Analysis of Multiclass Closed Networks of Queues, Proc. Int. Conf. on Stochastic Control and Optimization, jun 1979, pp. 25–29.

This implementation is based on Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 7.4.2.2 ("Approximate Solution Techniques"). This implementation is slightly different from the one described above, as it computes the average response times R instead of the residence times.

6.3.5 Mixed Networks

— Function File: [U, R, Q, X] = qnmix (lambda, N, S, V, m)

Solution of mixed queueing networks through MVA. The network consists of K service centers (single-server or delay centers) and C independent customer chains. Both open and closed chains are possible. lambda is the vector of per-chain arrival rates (open classes); N is the vector of populations for closed chains.

Note: In this implementation class switching is not allowed. Each customer class must correspond to an independent chain.

If the network is made of open or closed classes only, then this function calls qnopenmulti or qnclosedmultimva respectively, and prints a warning message.

INPUTS

lambda
N
For each customer chain c:
  • if c is a closed chain, then N(c)>0 is the number of class c requests and lambda(c) must be zero;
  • If c is an open chain, lambda(c)>0 is the arrival rate of class c requests and N(c) must be zero;

For each c, the following must hold:

               (lambda(c)>0 && N(c)==0) || (lambda(c)==0 && N(c)>0)

which means that either lambda(c) is nonzero and N(n) is zero, or the other way around. If for some c, lambda(c) \neq 0 and N(c) \neq 0, an error is reported and this function aborts.

S
S(c,k) is the mean service time for class c customers on service center k, S(c,k) ≥ 0. For FCFS nodes, service times must be class-independent.
V
V(c,k) is the average number of visits of class c customers to service center k (V(c,k) ≥ 0).
m
m(k) is the number of servers at service center k. Only single-server (m(k)==1) or IS (Infinite Server) nodes (m(k)<1) are supported. If omitted, each service center is assumed to have a single server. Queueing discipline for single-server nodes can be FCFS, PS or LCFS-PR.

OUTPUTS

U
U(c,k) is the utilization of class c requests on service center k.
R
R(c,k) is the response time of class c requests on service center k.
Q
Q(c,k) is the average number of class c requests on service center k.
X
X(c,k) is the class c throughput on service center k.
     
     
See also: qnclosedmultimva, qnopenmulti.

REFERENCES

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 7.4.3 ("Mixed Model Solution Techniques"). Note that in this function we compute the mean response time R instead of the mean residence time as in the reference.

Herb Schwetman, Implementing the Mean Value Algorithm for the Solution of Queueing Network Models, Technical Report CSD-TR-355, Department of Computer Sciences, Purdue University, feb 15, 1982, available at http://www.cs.purdue.edu/research/technical_reports/1980/TR%2080-355.pdf


Next: , Previous: Algorithms for Product-Form QNs, Up: Queueing Networks

6.4 Algorithms for non Product-Form QNs

— Function File: [U, R, Q, X] = qnmvablo (N, S, M, P)

MVA algorithm for closed queueing networks with blocking. qnmvablo computes approximate utilization, response time and mean queue length for closed, single class queueing networks with blocking.

INPUTS

N
population size, i.e., number of requests in the system. N must be strictly greater than zero, and less than the overall network capacity: 0 < N < sum(M).
S
Average service time. S(i) is the average service time requested on server i (S(i) > 0).
M
Server capacity. M(i) is the capacity of service center i. The capacity is the maximum number of requests in a service center, including the request currently in service (M(i) ≥ 1).
P
P(i,j) is the probability that a request which completes service at server i will be transferred to server j.

OUTPUTS

U
U(i) is the utilization of service center i.
R
R(i) is the average response time of service center i.
Q
Q(i) is the average number of requests in service center i (including the request in service).
X
X(i) is the throughput of service center i.
     
     
See also: qnopen, qnclosed.

REFERENCES

Ian F. Akyildiz, Mean Value Analysis for Blocking Queueing Networks, IEEE Transactions on Software Engineering, vol. 14, n. 2, april 1988, pp. 418–428. http://dx.doi.org/10.1109/32.4663

— Function File: [U, R, Q, X] = qnmarkov (lambda, S, C, P)
— Function File: [U, R, Q, X] = qnmarkov (lambda, S, C, P, m)
— Function File: [U, R, Q, X] = qnmarkov (N, S, C, P)
— Function File: [U, R, Q, X] = qnmarkov (N, S, C, P, m)

Compute utilization, response time, average queue length and throughput for open or closed queueing networks with finite capacity. Blocking type is Repetitive-Service (RS). This function explicitly generates and solve the underlying Markov chain, and thus might require a large amount of memory.

More specifically, networks which can me analyzed by this function have the following properties:

INPUTS

lambda
N
If the first argument is a vector lambda, it is considered to be the external arrival rate lambda(i) ≥ 0 to service center i of an open network. If the first argument is a scalar, it is considered as the population size N of a closed network; in this case N must be strictly less than the network capacity: N < sum(C).
S
S(i) is the average service time at service center i
C
C(i) is the Capacity of service center i. The capacity includes both the buffer and server space m(i). Thus the buffer space is C(i)-m(i).
P
P(i,j) is the transition probability from service center i to service center j.
m
m(i) is the number of servers at service center i. Note that m(i) ≥ C(i) for each i. If m is omitted, all service centers are assumed to have a single server (m(i) = 1 for all i).

OUTPUTS

U
U(i) is the utilization of service center i.
R
R(i) is the response time on service center i.
Q
Q(i) is the average number of customers in the service center i, including the request in service.
X
X(i) is the throughput of service center i.
Note: The space complexity of this implementation is O( \prod_i=1^K (C_i + 1)^2). The time complexity is dominated by the time needed to solve a linear system with \prod_i=1^K (C_i + 1) unknowns.


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6.5 Bounds on performance

— Function File: [Xu, Rl] = qnopenab (lambda, D)

Compute Asymptotic Bounds for single-class, open Queueing Networks with K service centers.

INPUTS

lambda
overall arrival rate to the system (scalar). Abort if lambda ≤ 0
D
D(k) is the service demand at center k. The service demand vector D must be nonempty, and all demands must be nonnegative (D(k) ≥ 0 for all k).

OUTPUTS

Xu
Upper bound on the system throughput.
Rl
Lower bound on the system response time.
     
     
See also: qnopenbsb.

REFERENCES

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 5.2 ("Asymptotic Bounds").

— Function File: [Xl, Xu, Rl, Ru] = qnclosedab (N, D)
— Function File: [Xl, Xu, Rl, Ru] = qnclosedab (N, D, Z)

Compute Asymptotic Bounds for single-class, closed Queueing Networks with K service centers.

INPUTS

N
number of requests in the system (scalar, N>0).
D
D(k) is the service demand of service center k, D(k) ≥ 0.
Z
external delay (think time, scalar, Z ≥ 0). If omitted, it is assumed to be zero.

OUTPUTS

Xl
Xu
Lower and upper bound on the system throughput.
Rl
Ru
Lower and upper bound on the system response time.
     
     
See also: qnclosedbsb, qnclosedgb, qnclosedpb.

REFERENCES

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 5.2 ("Asymptotic Bounds").

— Function File: [Xu, Rl, Ru] = qnopenbsb (lambda, D)

Compute Balanced System Bounds for single-class, open Queueing Networks with K service centers.

INPUTS

lambda
overall arrival rate to the system (scalar). Abort if lambda < 0
D
D(k) is the service demand at center k. The service demand vector D must be nonempty, and all demands must be nonnegative (D(k) ≥ 0 for all k).

OUTPUTS

Xl
Lower bound on the system throughput.
Rl
Ru
Lower and upper bound on the system response time.
     
     
See also: qnopenab.

REFERENCES

Edward D. Lazowska, John Zahorjan, G. Scott Graham, and Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models, Prentice Hall, 1984. http://www.cs.washington.edu/homes/lazowska/qsp/. In particular, see section 5.4 ("Balanced Systems Bounds").

— Function File: [Xl, Xu, Rl, Ru] = qnclosedbsb (N, D)
— Function File: [Xl, Xu, Rl, Ru] = qnclosedbsb (N, D, Z)

Compute Balanced System Bounds for single-class, closed Queueing Networks with K service centers.

INPUTS

N
number of requests in the system (scalar).
D
D(k) is the service demand at center k; K(k) ≥ 0.
Z
external delay (think time, scalar, Z ≥ 0). If omitted, it is assumed to be zero.

OUTPUTS

Xl
Xu
Lower and upper bound on the system throughput.
Rl
Ru
Lower and upper bound on the system response time.
     
     
See also: qnclosedab, qnclosedgb, qnclosedpb.

— Function File: [Xl, Xu] = qnclosedpb (N, D )

Compute PB Bounds (C. H. Hsieh and S. Lam, 1987) for single-class, closed Queueing Networks with K service centers.

INPUTS

N
number of requests in the system (scalar). Must be N > 0.
D
D(k) is the service demand of service center k. Must be D(k) ≥ 0 for all k.
Z
external delay (think time, scalar). If omitted, it is assumed to be zero. Must be Z ≥ 0.

OUTPUTS

Xl
Xu
Lower and upper bounds on the system throughput.
     
     
See also: qnclosedab, qbclosedbsb, qnclosedgb.

REFERENCES

The original paper describing PB Bounds is C. H. Hsieh and S. Lam, Two classes of performance bounds for closed queueing networks, PEVA, vol. 7, n. 1, pp. 3–30, 1987

This function implements the non-iterative variant described in G. Casale, R. R. Muntz, G. Serazzi, Geometric Bounds: a Non-Iterative Analysis Technique for Closed Queueing Networks, IEEE Transactions on Computers, 57(6):780-794, June 2008.

— Function File: [Xl, Xu, Ql, Qu] = qnclosedgb (N, D, Z)

Compute Geometric Bounds (GB) for single-class, closed Queueing Networks.

INPUTS

N
number of requests in the system (scalar, N > 0).
D
D(k) is the service demand of service center k (D(k) ≥ 0).
Z
external delay (think time, scalar). If omitted, it is assumed to be zero.

OUTPUTS

Xl
Xu
Lower and upper bound on the system throughput. If Z>0, these bounds are computed using Geometric Square-root Bounds (GSB). If Z==0, these bounds are computed using Geometric Bounds (GB)
Ql
Qu
Ql(i) and Qu(i) are the lower and upper bounds respectively of the queue length for service center i.
     
     
See also: qnclosedab.

REFERENCES

G. Casale, R. R. Muntz, G. Serazzi, Geometric Bounds: a Non-Iterative Analysis Technique for Closed Queueing Networks, IEEE Transactions on Computers, 57(6):780-794, June 2008. http://doi.ieeecomputersociety.org/10.1109/TC.2008.37

In this implementation we set X^+ and X^- as the upper and lower Asymptotic Bounds as computed by the qnclosedab function, respectively.


Previous: Bounds on performance, Up: Queueing Networks

6.6 Utility functions

6.6.1 Open or closed networks

— Function File: [U, R, Q, X] = qnclosed (N, S, V, ...)

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 qnclosedsinglemva, qnclosedsinglemvald or qnclosedmultimva.

     
     
See also: qnclosedsinglemva, qnclosedsinglemvald, qnclosedmultimva.

EXAMPLE

      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(1,3); # All centers are single-server
      Z = 2; # External delay
      N = 15; # Maximum population to consider
     
      V = qnvisits(P); # Compute number of visits from P
      D = V .* S; # Compute service demand from S and V
      X_bsb_lower = X_bsb_upper = zeros(1,N);
      X_ab_lower = X_ab_upper = zeros(1,N);
      X_mva = zeros(1,N);
      for n=1:N
        [X_bsb_lower(n) X_bsb_upper(n)] = qnclosedbsb(n, D, Z);
        [X_ab_lower(n) X_ab_upper(n)] = qnclosedab(n, D, 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]);
      xlabel("Number of Requests n");
      ylabel("System Throughput X(n)");
      legend("location","southeast");

— Function File: [U, R, Q, X] = qnopen (lambda, S, V, ...)

Compute utilization, response time, average number of requests in the system, and throughput for open queueing networks. If lambda is a scalar, the network is considered a single-class QN and is solved using qnopensingle. If lambda is a vector, the network is considered as a multiclass QN and solved using qnopenmulti.

     
     
See also: qnopensingle, qnopenmulti.

6.6.2 Computation of the visit counts

For single-class networks the average number of visits satisfy the following equation:

     V == P0 + V*P;

where P_0 j is the probability that an external arrival goes to service center j. If \lambda_j is the external arrival rate to service center j, and \lambda = \sum_j \lambda_j is the overall external arrival rate, then P_0 j = \lambda_j / \lambda.

For closed networks, the visit ratios satisfy the following equation:

     V(1) == 1 && V == V*P;

The definitions above can be extended to multiple class networks as follows. We define the visit ratios V_sj for class s customers at service center j as follows:

V_sj = sum_r sum_i V_ri P_risj, for all s,j V_s1 = 1, for all s

while for open networks:

V_sj = P_0sj + sum_r sum_i V_ri P_risj, for all s,j

where P_0sj is the probability that an external arrival goes to service center j as a class-s request. If \lambda_sj is the external arrival rate of class s requests to service center j, and \lambda = \sum_s \sum_j \lambda_sj is the overall external arrival rate to the whole system, then P_0sj = \lambda_sj / \lambda.

— Function File: [V ch] = qnvisits (P)
— Function File: V = qnvisits (P, lambda)

Compute the average number of visits to the service centers of a single class, open or closed Queueing Network with N service centers.

INPUTS

P
Routing probability matrix. For single class networks, P(i,j) is the probability that a request which completed service at center i is routed to center j. For closed networks it must hold that sum(P,2)==1. The routing graph myst be strongly connected, meaning that it must be possible to eventually reach each node starting from each node. For multiple class networks, P(r,i,s,j) is the probability that a class r request which completed service at center i is routed to center j as a class s request. Class switching is supported.
lambda
(open networks only) vector of external arrivals. For single class networks, lambda(i) is the external arrival rate to center i. For multiple class networks, lambda(r,i) is the arrival rate of class r requests to center i. If this parameter is omitted, the network is assumed to be closed.

OUTPUTS

V
For single class networks, V(i) is the average number of visits to server i. For multiple class networks, V(r,i) is the class r visit ratio at center i.
ch
(For closed networks only). ch(c,k) is the number of the chain that class c at center k belongs to. The total number of chains is max(ch).

EXAMPLE

      P = [ 0 0.4 0.6 0; \
            0.2 0 0.2 0.6; \
            0 0 0 1; \
            0 0 0 0 ];
      lambda = [0.1 0 0 0.3];
      V = qnvisits(P,lambda);
      S = [2 1 2 1.8];
      m = [3 1 1 2];
      [U R Q X] = qnopensingle( sum(lambda), S, V, m );

6.6.3 Other utility functions

— Function File: pop_mix = population_mix (k, N)

Return the set of valid population mixes with exactly k customers, for a closed multiclass Queueing Network with population vector N. More specifically, given a multiclass Queueing Network with C customer classes, such that there are N(i) requests of class i, a k-mix mix is a C-dimensional vector with the following properties:

          all( mix >= 0 );
          all( mix <= N );
          sum( mix ) == k;

This function enumerates all valid k-mixes, such that pop_mix(i) is a C dimensional row vector representing a valid population mix, for all i.

INPUTS

k
Total population size of the requested mix. k must be a nonnegative integer
N
N(i) is the number of class i requests. The condition k ≤ sum(N) must hold.

OUTPUTS

pop_mix
pop_mix(i,j) is the number of class j requests in the i-th population mix. The number of population mixes is rows( pop_mix ) .

Note that if you are interested in the number of k-mixes and you don't care to enumerate them, you can use the funcion qnmvapop.

     
     
See also: qnmvapop.

REFERENCES

Herb Schwetman, Implementing the Mean Value Algorithm for the Solution of Queueing Network Models, Technical Report CSD-TR-355, Department of Computer Sciences, Purdue University, feb 15, 1982, available at http://www.cs.purdue.edu/research/technical_reports/1980/TR 80-355.pdf

Note that the slightly different problem of generating all tuples k_1, k_2, \ldots k_N such that \sum_i k_i = k and k_i are nonnegative integers, for some fixed integer k ≥ 0 has been described in S. Santini, Computing the Indices for a Complex Summation, unpublished report, available at http://arantxa.ii.uam.es/~ssantini/writing/notes/s668_summation.pdf

— Function File: H = qnmvapop (N)

Given a network with C customer classes, this function computes the number of valid population mixes H(r,n) that can be constructed by the multiclass MVA algorithm by allocating n customers to the first r classes.

INPUTS

N
Population vector. N(c) is the number of class-c requests in the system. The total number of requests in the network is sum(N).

OUTPUTS

H
H(r,n) is the number of valid populations that can be constructed allocating n customers to the first r classes.
     
     
See also: qnclosedmultimva,population_mix.

REFERENCES

Zahorjan, J. and Wong, E. The solution of separable queueing network models using mean value analysis. SIGMETRICS Perform. Eval. Rev. 10, 3 (Sep. 1981), 80-85. DOI http://doi.acm.org/10.1145/1010629.805477


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Appendix A Contributing Guidelines

Contributions and bug reports are always welcome. If you want to contribute to the queueing package, here are some guidelines:

Send your contribution to Moreno Marzolla (marzolla@cs.unibo.it). Even if you are just a user of queueing, and find this package useful, let me know by dropping me a line. Thanks.


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Appendix B Acknowledgements

The following people (listed in alphabetical order) contributed to the queueing package, either by providing feedback, reporting bugs or contributing code: Philip Carinhas, Phil Colbourn, Yves Durand, Marco Guazzone, Dmitry Kolesnikov.


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Appendix C GNU GENERAL PUBLIC LICENSE

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     Copyright © 2007 Free Software Foundation, Inc. http://fsf.org/
     
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When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.

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    When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work's users, your or third parties' legal rights to forbid circumvention of technological measures.

  5. Conveying Verbatim Copies.

    You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program.

    You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee.

  6. Conveying Modified Source Versions.

    You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions:

    1. The work must carry prominent notices stating that you modified it, and giving a relevant date.
    2. The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to “keep intact all notices”.
    3. You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it.
    4. If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so.

    A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an “aggregate” if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate.

  7. Conveying Non-Source Forms.

    You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways:

    1. Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange.
    2. Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge.
    3. Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b.
    4. Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements.
    5. Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d.

    A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work.

    A “User Product” is either (1) a “consumer product”, which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, “normally used” refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product.

    “Installation Information” for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made.

    If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM).

    The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network.

    Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying.

  8. Additional Terms.

    “Additional permissions” are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions.

    When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission.

    Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms:

    1. Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or
    2. Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or
    3. Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or
    4. Limiting the use for publicity purposes of names of licensors or authors of the material; or
    5. Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or
    6. Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors.

    All other non-permissive additional terms are considered “further restrictions” within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying.

    If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms.

    Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way.

  9. Termination.

    You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11).

    However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation.

    Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice.

    Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10.

  10. Acceptance Not Required for Having Copies.

    You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so.

  11. Automatic Licensing of Downstream Recipients.

    Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License.

    An “entity transaction” is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts.

    You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it.

  12. Patents.

    A “contributor” is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's “contributor version”.

    A contributor's “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License.

    Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version.

    In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party.

    If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid.

    If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it.

    A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.

    Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.

  13. No Surrender of Others' Freedom.

    If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.

  14. Use with the GNU Affero General Public License.

    Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such.

  15. Revised Versions of this License.

    The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.

    Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation.

    If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.

    Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.

  16. Disclaimer of Warranty.

    THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.

  17. Limitation of Liability.

    IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

  18. Interpretation of Sections 15 and 16.

    If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.

END OF TERMS AND CONDITIONS

How to Apply These Terms to Your New Programs

If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.

To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.

     one line to give the program's name and a brief idea of what it does.
     Copyright (C) year name of author
     
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
     the Free Software Foundation, either version 3 of the License, or (at
     your option) any later version.
     
     This program is distributed in the hope that it will be useful, but
     WITHOUT ANY WARRANTY; without even the implied warranty of
     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
     General Public License for more details.
     
     You should have received a copy of the GNU General Public License
     along with this program.  If not, see http://www.gnu.org/licenses/.

Also add information on how to contact you by electronic and paper mail.

If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:

     program Copyright (C) year name of author
     This program comes with ABSOLUTELY NO WARRANTY; for details type ‘show w’.
     This is free software, and you are welcome to redistribute it
     under certain conditions; type ‘show c’ for details.

The hypothetical commands ‘show w’ and ‘show c’ should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an “about box”.

You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see http://www.gnu.org/licenses/.

The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read http://www.gnu.org/philosophy/why-not-lgpl.html.


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