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This manual documents how to install and run the Queueing Toolbox. It corresponds to version 1.0.0 of the package.
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.
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.
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.
The queueing
package source code in the Subversion repository
contains the following subdirectories (some of which are not included
in the installation archive):
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.
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
check
clean
distclean
dist
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
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.
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:
(k)
is
the average service time at center k.
(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.
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
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.
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
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.
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
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.
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.
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 conditionsum(
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, ..., NOUTPUTS
- 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");
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 conditionsum(
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, ..., NOUTPUTS
- 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");
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.
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, ..., NOUTPUTS
- 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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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 (K
≥
m).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.
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.
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
=
lambdaSee 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
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.
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=1where \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 islength(
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
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:
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.
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
Model Outputs
Given these output parameters, additional performance measures can be computed as follows:
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;
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
Model Outputs
It is possible to define aggregate performance measures as follows:
Ui = sum(U,1);
Rc = sum( V.*R, 1 );
Qc = sum( Q, 2 );
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.
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.
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.
General evaluator of QN models. Networks can be open, closed or mixed; single as well as multiclass networks are supported.
- For closed networks, the following server types are supported: M/M/m–FCFS, -/G/\infty, -/G/1–LCFS-PR, -/G/1–PS and load-dependent variants.
- For open networks, the following server types are supported: M/M/m–FCFS, -/G/\infty and -/G/1–PS. General load-dependent nodes are not supported. Multiclass open networks do not support multiple server M/M/m nodes, but only single server M/M/1–FCFS.
- For mixed networks, the following server types are supported: M/M/1–FCFS, -/G/\infty and -/G/1–PS. General load-dependent nodes are not 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.- List of queues in the network. This must be a cell array with N elements, such that QQ
{i}
is a struct produced by theqnmknode
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:
[0.2 0.1 0.1; 0.2 0.1 0.1]
. Thus, S(1,2) =
0.2
means that service time for class 1 customers where there are 2
requests in 0.2. Note that service times are class-independent;
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 );
Product-form queueing networks fulfill the following assumptions:
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.
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:
- External arrival rates are load-independent.
- Service center i consists either of m
(i) ≥ 1
identical servers with individual average service time S(i)
, or of an Infinite Server (IS) node.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.
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.
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.
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
.
Analyze open, single class BCMP queueing networks.
This function works for a subset of BCMP single-class open networks satisfying the following properties:
- The allowed service disciplines at network nodes are: FCFS, PS, LCFS-PR, IS (infinite server);
- Service times are exponentially distributed and load-independent;
- Service center i can consist of m
(i) ≥ 1
identical servers.- Routing is load-independent
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:
R_s = dot(V,R);
Q_s = sum(Q)
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.
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 asdot(
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 asQc = 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").
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 assum(
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".
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”.
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.
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 assum(
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").
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 asdot(
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 issum(
Q(:,k))
. The total number of class c requests in the system issum(
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").
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.
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
orqnclosedmultimva
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
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
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:
- There exists only a single class of customers.
- The network has K service centers. Center i has m_i > 0 servers, and has a total (finite) capacity of C_i \geq m_i which includes both buffer space and servers. The buffer space at service center i is therefore C_i - m_i.
- The network can be open, with external arrival rate to center i equal to \lambda_i, or closed with fixed population size N. For closed networks, the population size N must be strictly less than the network capacity: N < \sum_i C_i.
- Average service times are load-independent.
- P_ij is the probability that requests completing execution at center i are transferred to center j, i \neq j. For open networks, a request may leave the system from any node i with probability 1-\sum_j P_ij.
- Blocking type is Repetitive-Service (RS). Service center j is saturated if the number of requests is equal to its capacity
C_j
. Under the RS blocking discipline, a request completing service at center i which is being transferred to a saturated server j is put back at the end of the queue of i and will receive service again. Center i then processes the next request in queue. External arrivals to a saturated servers are dropped.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.
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").
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").
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").
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.
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.
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.
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
orqnclosedmultimva
.
- If N is a scalar, the network is assumed to have a single class of requests; in this case, the exact MVA algorithm is used to analyze the network. If S is a vector, then S
(k)
is the average service time of center k, and this function callsqnclosedsinglemva
which supports load-independent service centers. If S is a matrix, S(k,i)
is the average service time at service center k when i ≥ 1 jobs are present; in this case, the network is analyzed with theqnclosedsinglemvald
function.- If N is a vector, the network is assumed to have multiple classes of requests, and is analyzed using the exact multiclass MVA algorithm as implemented in the
qnclosedmultimva
function.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");
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 usingqnopenmulti
.See also: qnopensingle, qnopenmulti.
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.
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 thatsum(
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 ismax(
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 );
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 isrows(
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
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 issum(
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
Contributions and bug reports are always welcome. If you want
to contribute to the queueing
package, here are some
guidelines:
texinfo
format, so that it will be extracted and formatted into
the printable manual. See the existing functions of the
queueing
package for the documentation style.
queueing
package are (mostly) correct.
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.
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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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.
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.
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.
ctmc
: CTMC Stationary Probabilityctmc_bd
: Birth-Death processctmc_exps
: Expected Sojourn Timectmc_fpt
: CTMC First Passage Timesctmc_mtta
: Expected Time to Absorptionctmc_taexps
: Time-Averaged Expected Sojourn Timedtmc
: DTMC Stationary Probabilitydtmc_fpt
: DTMC First Passage Timespopulation_mix
: Utility functionsqnammm
: The Asymmetric M/M/m Systemqnclosed
: Utility functionsqnclosedab
: Bounds on performanceqnclosedbsb
: Bounds on performanceqnclosedgb
: Bounds on performanceqnclosedmultimva
: Algorithms for Product-Form QNsqnclosedmultimvaapprox
: Algorithms for Product-Form QNsqnclosedpb
: Bounds on performanceqnclosedsinglemva
: Algorithms for Product-Form QNsqnclosedsinglemvaapprox
: Algorithms for Product-Form QNsqnclosedsinglemvald
: Algorithms for Product-Form QNsqncmva
: Algorithms for Product-Form QNsqnconvolution
: Algorithms for Product-Form QNsqnconvolutionld
: Algorithms for Product-Form QNsqnjackson
: Algorithms for Product-Form QNsqnmarkov
: Algorithms for non Product-form QNsqnmg1
: The M/G/1 Systemqnmh1
: The M/Hm/1 Systemqnmix
: Algorithms for Product-Form QNsqnmknode
: Generic Algorithmsqnmm1
: The M/M/1 Systemqnmm1k
: The M/M/1/K Systemqnmminf
: The M/M/inf Systemqnmmm
: The M/M/m Systemqnmmmk
: The M/M/m/K Systemqnmvablo
: Algorithms for non Product-form QNsqnmvapop
: Utility functionsqnopen
: Utility functionsqnopenab
: Bounds on performanceqnopenbsb
: Bounds on performanceqnopenmulti
: Algorithms for Product-Form QNsqnopensingle
: Algorithms for Product-Form QNsqnsolve
: Generic Algorithmsqnvisits
: Utility functions