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In this paper, we investigate the flow of customers through queuing systems with randomly varying intensities. The analysis of the Kolmogorov-Chapman system of stationary equations for this model showed that it is not possible to construct a convenient symbolic solution. In this paper an attempt is made to circumvent this requirement by referring to the ergodicity theorems, which give s the conditions for the existence of the limit distribution in the service processes, but do not require knowledge of them.

In this paper, we investigate the flow of customers through queuing systems. This work was initiated by the task, which was set in 2000 to the author of the article by the late professor of the Australian National University (Canberra, Australia) Joe Gani. He proposed to calculate a model of a single-server queuing system with a Poisson input flow with a randomly changing intensity and a randomly changing service intensity. The analysis of the Kolmogorov-Chapman system of stationary equations for this model showed that it is not possible to construct a convenient symbolic solution and it is necessary to use numerical methods and to solve approximately a linear system of algebraic equations.

Progress in solving this problem could be obtained only recently, thanks to an appeal to Burke’s theorem on the coincidence of Poisson input and output flow distributions in the M | M | n | ∞ system. However, the proof of this theorem [

However, this alternative proof required knowledge of limit distributions in queuing systems, which is not always possible to obtain in a symbolic form. Therefore, in this paper an attempt is made to circumvent this requirement by referring to the ergodicity theorems, which gives the conditions for the existence of the limit distribution in the service processes, but do not require knowledge of them.

Consider queuing system A with Poisson input flow of intensity λ . Let x ( t ) be the number of customers of the input flow in the system A on a half-interval ( 0 , t ] , x ( 0 ) = 0 . Then the following relation is true.

lemma 1. The following almost sure convergence is true:

x ( t ) t → λ , t → ∞ . (1)

Proof. Let [ t ] be an integer part of t > 1 . Then almost surely the following relations are executed:

x ( [ t ] ) [ t ] ⋅ [ t ] [ t ] + 1 ≤ x ( t ) t ≤ x ( [ t ] + 1 ) [ t ] + 1 ⋅ [ t ] + 1 [ t ] , (2)

with

x ( [ t ] ) = ∑ i = 0 [ t ] − 1 ( x ( i + 1 ) − x ( i ) ) , (3)

where all terms in equality (3) are independent and have Poisson distribution with the parameter λ . Then from the relations (2), (3) and the strengthened law of large numbers (see, for example, ( [

Corollary 1. From Lemma 1 we have the convergence by probability in the relation (1).

Denote y ( t ) the number of customers that came out of the queuing system A on the half-interval ( 0 , t ] and put z ( t ) the number of customers in the system at time t.

Theorem 1. If the random process z ( t ) is ergodic and stationary, then the convergence by probability is true

y ( t ) t → λ , t → ∞ . (4)

Proof. With probability one, the following equality is performed

x ( t ) = y ( t ) + z ( t ) , t ≥ 0. (5)

In turn, from the ergodicity of the stationary random process z ( t ) , z ( 0 ) = 0 it follows (see, for example, ( [

z ( t ) t → 0, t → ∞ . (6)

Fix ε > 0 , then the following equality is true:

P ( z ( t ) t > ε ) = 1 − F ( ε t ) → 0 , t → ∞ ,

and so the convergence by probability in the relation (6) is valid. From the convergence by probability in the relation (6), equality (5) and Corollary 1 we obtain the statement of the theorem 1.

Suppose that the ergodic stationary process z ( t ) is Markov. Since this process characterizes the number of customers in the queuing system A, then it takes the values 0,1, ⋯ Denote f k = P ( z ( t ) = k ) , k = 0 , 1 , ⋯ and suppose that the intensities of μ k customers leaving the system A, in the state z ( t ) = k .

Theorem 2. The output flow in queuing system A is Poisson with the intensity λ .

Proof. A random sequence of points on the real axis is called a Poisson flow with the intensity λ if the following conditions ( [

We use the construction of [

Remark 1. In a case the queuing system A is n-server type M | M | n | ∞ , Theorem 2 is a generalization of the famous theorem of Burke [

Consider the M | M | 1 | 0 queuing system, in which the customers coming on a busy server, is refused. Input flow to this system is a Poisson with the intensity λ , service times have exponential distribution with the parameter μ . This system is described by a number z ( t ) of the customers in it at the moment t [

α ( 0 , 1 ) = λ , α ( 1 , 0 ) = μ , α ( 1 , 1 ) = λ .

Here the transition 0 → 1 corresponds to the arrival of the customer into the empty system. In turn, the transitions between the process states z ( t ) , leading to the withdrawal of customers from the system have the form 1 → 0, 1 → 1 (see

The transition 1 → 0 corresponds to the withdrawal of the severed customer from the system, and the 1 → 1 corresponds to the withdrawal of the refused customer from the system.

Markov process z ( t ) is ergodic for any parameter values λ , μ > 0 , and its limit distribution has the form

b 0 = 1 1 + ρ , b 1 = ρ 1 + ρ .

Theorem 2 implies the following statement.

Theorem 3. Stationary flows of the served customers and customers that have been rejected, are Poisson and have intensities a 1 = b 1 μ , a 2 = b 1 λ , respectively. The stationary output flow in the system M | M | 1 | 0 is Poisson and its intensity a = λ = a 1 + a 2 .

Remark 2. The statement of Theorem 3 extends to queuing systems M | M | n | N with a limited queue, a finite number of servers, as well as a system with a finite number of flows and a fairly general discipline of their service. In the latter case it is necessary to fulfil the ergodicity condition, which requires that the graph whose nodes are the states of the system, whose edges are transitions between states, satisfies the reachable condition of any node from any other node.

1) Poisson input flow with randomly varying intensity. Let the time axis t ≥ 0 be split into half-intervals

[ T 0 = 0 , T 1 = T 0 + ξ 1 ) , [ T 1 , T 2 = T 1 + ξ 2 ) , ⋯ ,

where

with parameter

states

Consider the Markov random process

Points of jumps (up) of the process

This circumstance allows us to study the output flows in queuing systems and networks with Poisson input flow having a randomly changing intensity, assuming that the random service intensities in the nodes of the queuing system or network and the random input flow are independent.

2) Queing system

flow with the intensity

Partially supported by Russian Fund of Basic Researches, project 17-07-00177.

Tsitsiashvili, G.Sh. (2018) Ergodicity and Invariance of Flows in Queuing Systems. Journal of Applied Mathematics and Physics, 6, 1454-1459. https://doi.org/10.4236/jamp.2018.67122