Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime

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@article{Naumenko_2016,
   doi = {10.17512/jamcm.2016.3.11},
   url = {https://doi.org/10.17512/jamcm.2016.3.11},
   year = 2016,
   publisher = {The Publishing Office of Czestochowa University of Technology},
   volume = {15},
   number = {3},
   pages = {111--122},
   author = {Victor Naumenko and  Mikhail Matalytski and  Dmitry Kopats},
   title = {Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime},
   journal = {Journal of Applied Mathematics and Computational Mechanics}
}

TY  - JOUR
DO  - 10.17512/jamcm.2016.3.11
UR  - https://doi.org/10.17512/jamcm.2016.3.11
TI  - Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime
T2  - Journal of Applied Mathematics and Computational Mechanics
JA  - J Appl Math Comput Mech
AU  - Naumenko, Victor
AU  - Matalytski, Mikhail
AU  - Kopats, Dmitry
PY  - 2016
PB  - The Publishing Office of Czestochowa University of Technology
SP  - 111
EP  - 122
IS  - 3
VL  - 15
SN  - 2299-9965
SN  - 2353-0588
ER  - 

Naumenko, V., Matalytski, M., & Kopats, D. (2016). Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime. Journal of Applied Mathematics and Computational Mechanics, 15(3), 111-122. doi:10.17512/jamcm.2016.3.11

Naumenko, V., Matalytski, M. & Kopats, D., 2016. Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime. Journal of Applied Mathematics and Computational Mechanics, 15(3), pp.111-122. Available at: https://doi.org/10.17512/jamcm.2016.3.11

[1]V. Naumenko, M. Matalytski and D. Kopats, "Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime," Journal of Applied Mathematics and Computational Mechanics, vol. 15, no. 3, pp. 111-122, 2016.

Naumenko, Victor, Mikhail Matalytski, and Dmitry Kopats. "Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime." Journal of Applied Mathematics and Computational Mechanics 15.3 (2016): 111-122. CrossRef. Web.

1. Naumenko V, Matalytski M, Kopats D. Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime. Journal of Applied Mathematics and Computational Mechanics. The Publishing Office of Czestochowa University of Technology; 2016;15(3):111-122. Available from: https://doi.org/10.17512/jamcm.2016.3.11

Naumenko, Victor, Mikhail Matalytski, and Dmitry Kopats. "Analysis of the queueing network with a random waiting time of negative customers at a non-stationary regime." Journal of Applied Mathematics and Computational Mechanics 15, no. 3 (2016): 111-122. doi:10.17512/jamcm.2016.3.11

ANALYSIS OF THE QUEUEING NETWORK WITH A RANDOM WAITING TIME OF NEGATIVE CUSTOMERS AT A NON-STATIONARY REGIME

Victor Naumenko ¹, Mikhail Matalytski ², Dmitry Kopats ¹

¹ Faculty of Mathematics and Computer Science, Grodno State University
Grodno, Belarus
² Institute of Mathematics, Czestochowa University of Technology
Częstochowa, Poland
victornn86@gmail.com, m.matalytski@gmail.com

Abstract. In the article a queueing network (QN) with positive customers and a random waiting time of negative customers has been investigated. Negative customers destroy positive customers on the expiration of a random time. Queueing systems (QS) operate under a heavy-traffic regime. The system of difference-differential equations (DDE) for state probabilities of such a network was obtained. The technique of solving this system and finding mean characteristics of the network, which is based on the use of multivariate generating functions was proposed.

Keywords: G-network, positive customers, negative customers, random waiting time, heavy-traffic regime, state probabilities, mean characteristics, non-stationary regime

1. Network description

Consider an open G-network [1] with single-queues QS. An independent Poisson flow of positive customers with rate and a Poisson flow of negative customers with rate arrive to QS from outside (system ), . All arriving to QS customer flows are assumed to be independent. The probability that the positive customer serviced in during time , if at the current moment in the system there are customers, are equal to . The positive customer gets serviced in with probability move to QS as a positive customer and with probability - as a negative customer and with probability come out of the network to the external environ- ment, .

A negative customer is arriving to QS increases the length of the queue of nega- tive customers for one, and requires no service. Each negative customer, located in i-th QS, stays in the queue for a random time according to a Poisson process of rate , . By the end this time, the negative customer destroys one positive customer in the QS and leaves the network. If after this random time in the system there are no positive customers, then a given negative customer leaves the network, without exerting any influence on the operation of the network as a whole. Wherein the probability that in QS , negative customer leaves the queue during , on the condition that, in this QS at time there are negative customers, equals .

The network state at time described by the vector , which forms a homogeneous Markov process with a countable number of states, where the state means that at time in QS , there are positive customers and negative customers, . We introduce the vectors and , - vector, which is -th component equal to 1, all the others are 0, .

Negative customers may describe the behavior of computer viruses, whose impact on the information (positive customers) occurs through a random time.

It should be noted that analisys at a stationary regime of QN with positive and negative customers excluding random queueing time, and also with signals has been carried out in [2, 3] and at non-stationary regime in [4, 5].

2. State probabilities of the network operating under a heavy-traffic regime

Lemma. Let - state probability at time . State probabilities of considered network are satisfy system of DDE:

(1)

where , .

Proof. The possible transitions of our Markov process in the state during time :

1) from the state in this case into QS for the time a positive customer will arrive with probability , ;

2) from the state , while to the QS for the time a negative custo- mer will arrive with probability , ;

3) from the state , in this case the positive customer comes out of the network to the external environment with probability , ;

4) from the state , in the given case into QS the negative customer, destroys in the QS the positive customer, leaves the network; the probability of such an event is equal to , ;

5) from the state , while in the QS , the residence time in the queue of the negative customer finished, if in time there were negative customers and there were no positive customers; the probability of such an event is equal to , ;

6) from the state , in given case after finishing the service of the positive customer in the QS it moves to the QS again as a positive customer with probability , ;

7) from the state , in this case the positive customer, which is ser- viced in QS , moves to QS as a negative customer; the probability of such an event is equal to , ;

8) from the state , while in each QS , , do not arrive any positive nor any negative customers, and in which for the time any customer didn’t service, no negative customer will come out of the queue; the probability of such event is equal to

, ;

9) from other states with probability .

Then, using the formula of total probability, we can write

Taking the limit we obtain a system of equations for state probabilities of the network. (1). The lemma is proved.

We will assume, that all queuing network systems are single-queue, and customer service duration in the QS has an exponential distribution with the rate . Consequently, in this case , .

Denote by , where , the generating function of the dimension of :

(2)

the summation is taking for each , from 0 to , .

We will assume that , , , .

Multiplying each of the equations (1) to and summing up all possible values and from 1 to , . Here the summation for all and is taken from 1 to , i.e. all summands in (2), for which in the network state there are components and , due to the assumptions put forward above. Because, for example

, .

Then we obtain

(3)

Let’s consider the sums, contained on the right side of the relation (3). Let

Then

Similarly for the sum we have:

.

For the sum we obtain:

.

The sum has the form:

For the sum we obtain:

.

The sum .

For the sum we shall obtain:

.

And, finally, for the last sum we shall have:

Using these sums, we obtain a homogeneous linear differential equation:

Its solution has the form

Let's consider, that at the initial moment of time, the network is in a state , , ,

, , , .

Then the initial condition for the last equation will be

from which we obtain .

Theorem. If at the initial moment of time the QN is in a state , , , , then the expression for the generating function , taking into account the expansions appearing in it exponent Maclaurin, has the form

(4)

where

, , .

Proof. We have:

,

where

Multiplying , , and we will obtain an expression (4), .

State probability of is the coefficient of in the expansion of in multiple series (4), with the proviso, that at the initial time the network is in a state .

3. Finding the main characteristics

With the help of the generating function a different mean network characteristics can also be found at the transient regime. The expectation of a component with the number of a multivariate random variable can be found, differentiating (4) by and suppose , . Therefore for the mean number of positive customers in the network system we will use the relation:

(5)

The change of variables will be done in the expression (5) , then and

So like all network QS operating under heavy-traffic regime, we obtain, then and, consequently, , therefore

(6)

Similarly, we can find the relation for the mean number of negative customers in the system , that are awaiting:

(7)

Example. Let the number of QS in QN be . Let external arrivals to the network of positive and negative customers respectively equal: , , , , , , and the service times of rates equal: , , . Let negative customers stay in the queue for a random time, which has an exponential distribution with parameters equal: , , . We assume that the transition probability of positive customers has the form: , , , , , ; transition probabilities of negative customers equal: , , , , , ; then the probabilities will be equal respectively: , , . In this case .

The mean number of customers in network systems (in the queue and in servicing), on the condition that , , can be found by the formula (6), and the mean number of negative customers (waiting in the queue) may be found by the formula (7).

Figure 1 shows the chart of change of the mean number of positive customers in the QS (straight line) and the chart of change of the mean number of negative customers (dash line), which are awaiting in the queue of the QS respectively.

Fig. 1. The chats of changes of the mean number of positive customers and negative customers in the QS

4. Conclusions

In the paper, the Markov network with positive customers with a random waiting time of negative customers at transient regime has been investigated. A technique of finding non-stationary state probabilities of the above network with single-queues of QS was proposed. It is based on the method of using the apparatus of multivariate generating functions. Relations for the mean characteristics depending on time of the considered G-network, on the condition that the network operates under heavy-traffic regime was obtained.

The practical significance of these results is that they can be used for modeling the functioning of various information networks and systems, a model of which is the aforementioned network taking into account the penetration of computer viruses into it.

References

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

[2] Gelenbe E., G-networks with triggered customer movement, Journal of Applied Probability 1993, 30, 742-748.

[3] Gelenbe Е., Pujolle G., Introduction to Queueing Networks, John Wiley, N.Y. 1998, 244.

[4] 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.

[5] 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.