# Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers

### Mikhail Matalytski

,### Victor Naumenko

,### Dmitry Kopats

Journal of Applied Mathematics and Computational Mechanics |
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@article{Matalytski_2016, doi = {10.17512/jamcm.2016.4.14}, url = {https://doi.org/10.17512/jamcm.2016.4.14}, year = 2016, publisher = {The Publishing Office of Czestochowa University of Technology}, volume = {15}, number = {4}, pages = {125--136}, author = {Mikhail Matalytski and Victor Naumenko and Dmitry Kopats}, title = {Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers}, journal = {Journal of Applied Mathematics and Computational Mechanics} }

TY - JOUR DO - 10.17512/jamcm.2016.4.14 UR - https://doi.org/10.17512/jamcm.2016.4.14 TI - Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers T2 - Journal of Applied Mathematics and Computational Mechanics JA - J Appl Math Comput Mech AU - Matalytski, Mikhail AU - Naumenko, Victor AU - Kopats, Dmitry PY - 2016 PB - The Publishing Office of Czestochowa University of Technology SP - 125 EP - 136 IS - 4 VL - 15 SN - 2299-9965 SN - 2353-0588 ER -

Matalytski, M., Naumenko, V., & Kopats, D. (2016). Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers. Journal of Applied Mathematics and Computational Mechanics, 15(4), 125-136. doi:10.17512/jamcm.2016.4.14

Matalytski, M., Naumenko, V. & Kopats, D., 2016. Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers. Journal of Applied Mathematics and Computational Mechanics, 15(4), pp.125-136. Available at: https://doi.org/10.17512/jamcm.2016.4.14

[1]M. Matalytski, V. Naumenko and D. Kopats, "Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers," Journal of Applied Mathematics and Computational Mechanics, vol. 15, no. 4, pp. 125-136, 2016.

Matalytski, Mikhail, Victor Naumenko, and Dmitry Kopats. "Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers." Journal of Applied Mathematics and Computational Mechanics 15.4 (2016): 125-136. CrossRef. Web.

1. Matalytski M, Naumenko V, Kopats D. Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers. Journal of Applied Mathematics and Computational Mechanics. The Publishing Office of Czestochowa University of Technology; 2016;15(4):125-136. Available from: https://doi.org/10.17512/jamcm.2016.4.14

Matalytski, Mikhail, Victor Naumenko, and Dmitry Kopats. "Finding the probabilistically-temporal characteristics of Markov G-network with batch removal of positive customers." Journal of Applied Mathematics and Computational Mechanics 15, no. 4 (2016): 125-136. doi:10.17512/jamcm.2016.4.14

FINDING THE PROBABILISTICALLY-TEMPORAL CHARACTERISTICS OF MARKOV G-NETWORK WITH BATCH REMOVAL OF POSITIVE CUSTOMERS

Mikhail Matalytski^{ 1}, Victor Naumenko^{ 2}, Dmitry Kopats^{ 2}

^{1} Institute of Mathematics, Czestochowa
University of Technology

Częstochowa, Poland

^{2} Faculty of Mathematics and Computer Science, Grodno State University

Grodno, Belarus

m.matalytski@gmail.com, victornn86@gmail.com

Received: 04 May
2016; accepted: 11 July 2016

**Abstract.** The investigation of a Markov queueing
network with positive and negative
customers and positive customers batch removal
has been carried out in the article.
The purpose of the research is analysis of such a network at the
non-stationary regime,
finding the time-dependent state probabilities and mean number of customers. In the first part of this article, a description of the G-network
operation is provided with one-line queueing systems. When a negative customer
arrives to the system, the count of positive customers is reduced by a random
value, which is set by some probability distribution. Then for the non-stationary state probabilities a
Kolmogorov system was derived of difference-differential equations. A technique for finding the state probabilities and the
mean number of customers of the investigated network, based on the use of an
apparatus of multi-
dimensional generating functions has been proposed. The theorem about the
expression
for the generating function has been given. A model example has been
calculated.

*Keywords**: **G-network, positive and negative customers, customers batch removal,
non-stationary regime, generation function, state
probabilities*

1. Introduction

G-networks or queueing networks with positive and negative customers was introduced by Gelenbe [1]. Such networks with batch removal of positive customers are used in the development of computer systems and networks models, in which defective problems (negative customers) destroy tasks or user jobs (positive customers). Task handlers are the processes running on servers; customers - user jobs for processing [2].

Let’s consider an open G-queueing network
with single-line queueing
systems (QS). To the system external environment (QS ) a simple flow
of customers arrives with the rate and
a simple flow of negative customers with the rate , . All customer flows that arrive to the network are independent. The
service durations of positive customers in *i*-th QS distributed
exponentially with the rate , .

In the articles [3-5], a G-network at a transient (non-stationary) regime has been investigated. At that negative customer, arrivining to the system, where there were positive customers, immediately destroy one of them, and then immediately left the network, not having received service in the QS. In the article [6] was conducted analysis at non-stationary regime of G-network with signals, and in the article [7] - G-network with signals with random delay.

In this paper, we describe in more detail the
other behaviors of negative
customers, which arrive to the
network systems. Let at time *t* in the system there are positive customers, where - integer random value. Negative customer arriving to the some system of the network instantly destroys
(removes from the network) destroyed
immediately of positive customers. If , the system completely is
devastated (all positive customers, which are in this QS at a given time are immediately destroyed). Thus,
random value , which effectively deter-
mines the maximum size of the destruction batch of customers in QS , is subject to an arbitrary discrete distribution law:

, . | (1) |

Gelenbe studied such behavior of negative customers considering signals in the paper [8], but only at stationary regime.

As previously explained, in each network system only positive customers are serviced. A positive customer serviced in the QS , with probability sent to the QS as a positive customer, with a probability - as a negative customer, and with probability leaving from the network to the external environment (QS ), .

The state of the network at time meaning the vector

, | (2) |

which forms a Markov random process with a countable number of states, where state means that at time in QS there are of positive customers, .

2. The system of the difference-differential Kolmogorov equations for the network state probabilities

We introduce some notations. Let’s - probability of the network state at time ; - Heaviside function, ; - a vector of dimension , consisting of zeros, except for the component with number of , which is equal to 1, . Let’s allows

, , , , , , . | (3) |

There are following transitions of a random process to the state during time :

1) from the state , in this case, during time a positive customer arriving to the QS with probability ;

2) from the state , wherein the positive customer leaves the network or in an external environment passes into QS as a negative customer, if it had no customers; the probability of such an event equals ;

3) from the states , in this case a negative customer arrives to the QS and destroys a random batch of positive customers; the probability of such event is equal to ;

4) from the state , in this case, after the servicing of a positive customer in the QS it goes to the QS ; the probability of such an event is equal to ;

5) from the states in this case, after the servicing of a positive customer in the QS it goes to the QS as a negative customer, which destroys in it random batch of positive customers; the probability of such an event is equal to ;

6) from the state , while in each QS , , there are no positive customers nor any negative customers arriving, and which during was not to serviced by any customer; the probability of such event is equal to , ;

7) of the remaining states with a probability .

Then, using the formula of total probability, we shall have

. | (4) |

Dividing both sides of this relation by and passing to the limit we obtain that the non-stationary states probabilities of the considered network satisfy the following system of difference-differential equations

(5) . |

Suppose that all systems of the network operating in a heavy traffic regime, i.e. , , . Taking this assumption into account, the system (5) takes the form

. | (6) |

3. Finding network state probabilities using the generating function

Denote by
, where , , *n*-dimensional generating function:

(7) |

Multiplied (6) on and adding together all possible values from 0 to , , obtain:

. | (8) |

Consider some sums, contained on the right side of (8). Let’s allows

, |

then we can show that

. |

For the sum of we have:

. |

For the sum of we will have:

. |

For the sum of we obtain:

. |

And finally, for the last sum

we shall have:

. |

Therefore, the generating function is a fairly ordinary inhomogeneous linear ordinary differential equation

. | (9) |

Since all of the whole QS network operate under high load conditions, the last two expressions in the form of the sums in equation (9) will be zero, and it becomes homogeneous:

. |

Its general solution has the form

. | (10) |

We assume that at the initial moment of time, the network is in state , , , , , . Then the initial condition for the last equation (10) will be . Using it, we obtain . Thus, the expression for the generating function has the form

, | (11) |

where

. |

To find the probability of the network states, we transform (11) to a convenient form by expanding its member exhibitors in a Maclaurin series. We can show that the statement is true.

**Theorem****.** *The expression for the generating function can be represented
in the form*

, | (12) |

where , .

The probability can be found as the coefficient of in the expansion of function in multiple series (12), and the mean number of customers in the system in the form .

4. Model example

In this example the count of QS lets
arriving probabilities of customers to *i*-th QS be
respectively equal , , , , and , .

Consider the time interval . As a time unit we take 1 hour, then h. Let the arriving rates of positive and negative customers be respectively equal and . Let’s arriving rates of positive and negative customers to each QS and , taking into account that , , , respectively will be equal: , , , , , , , , , . Suppose, that service rates of customers in QS are equal: , , , ,.

Let us also transition probabilities of positive and negative customers between QS be equal , , , , , , ; , , , , , , , , , .

Calculations have shown that .

Let’s a
random value has a Poisson distribution
with parameter** ** and let , then (1) takes form , , . Let also .

We need to find, for example, state probability . It is the coefficient of in the expansion of in multiple series (12), so that the degree of must satisfy the relation , , where , , , , . Hence, it follows that

, , , , , |

, , |

Then from (12) we find that

. |

Figure 1 shows a chart of the state
probability for different on the
condition that at the initial time, the network is in a state of , *.*

Fig. 1. The chart of the state probability

Calculating the partial derivative of the
generating function (12) by variable *z _{i}* in dot

*z*= (1,1,…,1), and carrying out a series of mathematical transformations, we find that the mean number of customers in the queue system

*S*is calculated by the formula

_{i}. |

Figure 2 shows a plot of changes of the mean number of customers in QS .

Fig. 2. The chat for changes of the mean number
of positive customers *N*_{2}(*t*)
in QS *S*_{2}

5. Conclusions

In the article an investigation of Markov G-network was conducted in the case when a negative customer can destroy a random batch of positive customers. For such a network operating under a heavy traffic regime, finding a technique for non-stationary state probabilities and a mean number of customers in network systems was proposed using an apparatus based on the use of multivariate generating functions.

References

[1] Gelenbe E., Product form queueing networks with negative and positive customers, Journal of Applied Probability 1991, 28, 656-663.

[2] Naumenko V., Pankov A., Kopats D., Investigation of Markov G-network with positive customers batch removal in transient regime. [In Russian: Issledovaniye markovskoy G-seti s gruppovym udaleniyem polozhitel'nykh zayavok v perekhodnom rezhime]. Vestnik of GrSU. Ser 2, 2016, 3.

[3] Matalytski M., Naumenko V., Stochastic networks with non-standard customers movement. [In Russian: Stokhasticheskiye seti s nestandartnymi peremeshcheniyami zayavok], Monograph, GrSU, Grodno 2016.

[4] Naumenko V., Matalytski M., Network analysis with positive and negative requests in transitional mode. [In Russian: Analiz seti s polozhitel'nymi i otritsatel'nymi zaiavkami v perekhodnom rezhime], Vestnik of GrSU, Ser 2. 2016, 3(159), 135-142.

[5] Matalytski M., Naumenko V., Non-stationary analysis of queueing network with positive and negative messages, Journal of Applied Mathematics and Computational Mechanics 2013, 12(2), 61-71.

[6] Matalytski M., Naumenko V., Investigation of G-network with signals at transient behavior, Journal of Applied Mathematics and Computational Mechanics 2014, 13(1), 75-86.

[7] Matalytski M., Naumenko V., Investigation of G-network with random delay of signals at non-stationary behavior, Journal of Applied Mathematics and Computational Mechanics 2014, 13(3), 155-166.

[8] Gelenbe E., G-networks with signals and batch removal, Probability in the Engineering and Informational Sciences 1993, 7, 335-342.