scholarly journals Asymptotic Analysis of the MMРР|M|1 Retrial Queue with Negative Calls under the Heavy Load Condition

2020 ◽  
Vol 20 (4) ◽  
pp. 534-547
Author(s):  
Ekaterina A. Fedorova ◽  
◽  
Anatoly A. Nazarov ◽  
Mais P. Farkhadov ◽  
◽  
...  
Keyword(s):  
Asymptotic Analysis ◽  
Retrial Queue ◽  
Load Condition ◽  
System Capacity ◽  
Stationary Mode ◽  
Single Server ◽  
Numerical Comparison ◽  
Heavy Load ◽  
Retrial Queueing System ◽  
Asymptotic Characteristic

In the paper, a single-server retrial queueing system with MMPP arrivals and an exponential law of the service time is studied. Unserviced calls go to an orbit and stay there during random time distributed exponentially, they access to the server according to a random multiple access protocol. In the system, a Poisson process of negative calls arrives, which delete servicing positive calls. The method of the asymptotic analysis under the heavy load condition for the system studying is proposed. It is proved that the asymptotic characteristic function of a number of calls on the orbit has the gamma distribution with the obtained parameters. The value of the system capacity is obtained, so, the condition of the system stationary mode is found. The results of a numerical comparison of the asymptotic distribution and the distribution obtained by simulation are presented. Conclusions about the method applicability area are made.

scholarly journals Equilibrium Customer Strategies in the Single-Server Constant Retrial Queue with Breakdowns and Repairs

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhengwu Zhang ◽  
Jinting Wang ◽  
Feng Zhang
Keyword(s):  
Retrial Queue ◽  
Profit Maximization ◽  
Optimal Strategies ◽  
The State ◽  
Equilibrium Analysis ◽  
Arrival Process ◽  
Single Server ◽  
Retrial Queueing System ◽  
Exact Number ◽  
Number Of Customers

We consider a single-server constant retrial queueing system with a Poisson arrival process and exponential service and retrial times, in which the server may break down when it is working. The lifetime of the server is assumed to be exponentially distributed and once the server breaks down, it will be sent for repair immediately and the repair time is also exponentially distributed. There is no waiting space in front of the server and arriving customers decide whether to enter the retrial orbit or to balk depending on the available information they get upon arrival. In the paper, Nash equilibrium analysis for customers’ joining strategies as well as the related social and profit maximization problems is investigated. We consider separately the partially observable case where an arriving customer knows the state of the server but does not observe the exact number of customers waiting for service and the fully observable case where customer gets informed not only about the state of the server but also about the exact number of customers in the orbit. Some numerical examples are presented to illustrate the effect of the information levels and several parameters on the customers’ equilibrium and optimal strategies.

scholarly journals Asymptotic Analysis of Retrial Queueing System M/M/1 with Impatient Customers, Collisions and Unreliable Server

2020 ◽  
pp. 218-230 ◽  
Author(s):  
Elena Yu. Danilyuk ◽  
Svetlana P. Moiseeva ◽  
Janos Sztrik
Keyword(s):  
Asymptotic Analysis ◽  
Queueing System ◽  
Analysis Method ◽  
Impatient Customers ◽  
Heavy Load ◽  
Gaussian Form ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
Long Time ◽  
Number Of Customers

The retrial queueing system of M=M=1 type with Poisson flow of arrivals, impatient cus- tomers, collisions and unreliable service device is considered in the paper. The novelty of our contribution is the inclusion of breakdowns and repairs of the service into our previous study to make the problem more realistic and hence more complicated. Retrial time of customers in the orbit, service time, impa- tience time of customers in the orbit, server lifetime (depending on whether it is idle or busy) and server recovery time are supposed to be exponentially distributed. An asymptotic analysis method is used to find the stationary distribution of the number of customers in the orbit. The heavy load of the system and long time patience of customers in the orbit are proposed as asymptotic conditions. Theorem about the Gaussian form of the asymptotic probability distribution of the number of customers in the orbit is formulated and proved. Numerical examples are given to show the accuracy and the area of feasibility of the proposed method

scholarly journals Heavy outgoing call asymptotics for MMPP|M|1 retrial queue with two way communication and multiple types of outgoing calls

2021 ◽  
Vol 21 (1) ◽  
pp. 111-124
Author(s):  
Anatoly A. Nazarov ◽  
◽  
Svetlana V. Paul ◽  
Olga D. Lizyura ◽  
◽  
...  
Keyword(s):  
Characteristic Function ◽  
Operating Time ◽  
Retrial Queue ◽  
Random Variable ◽  
High Rate ◽  
Single Server ◽  
Term Operation ◽  
Repeated Calls ◽  
Asymptotic Characteristic ◽  
Limiting Condition

In this paper, we consider a single server retrial queue MMPP|M|1 with two way communication and multiple types of outgoing calls. Calls received by the system occupy the device for operating, if it is free, or are sent to orbit, where they make a random delay before the next attempt to occupy the device. The duration of the delay has an exponential distribution. The main issue of this model is an existence of various types of outgoing calls in the system. The intensity of outgoing calls is different for different types of outgoing calls. The operating time of the outgoing calls also differs depending on the type and is exponential random variable, the parameters of which in the general case do not coincide. The device generates calls from the outside only when it does not operate the calls received from the flow. We use asymptotic analysis methods under two limit conditions: high rate of outgoing calls and low rate of serving outgoing calls. The aim of the current research is to derive an asymptotic stationary probability distribution of the number of incoming calls in the system that arrived from the flow, without taking into account the outgoing call if it is operated on the device. In this paper, we obtain asymptotic characteristic function under aforementioned limit conditions. In the limiting condition of high intensity of outgoing calls, the asymptotic characteristic function of the number of incoming calls in a system with repeated calls and multiple types of outgoing calls is a characteristic function of a Gaussian random variable. The type of the asymptotic characteristic function of the number of incoming calls in the system under study in the limiting condition of long-term operation of the outgoing calls is uniquely determined.

The M/G/1 Retrial Queue With Retrial Rate Control Policy

1993 ◽  
Vol 7 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Bong Dae Choi ◽  
Kyung Hyune Rhee ◽  
Kwang Kyu Park
Keyword(s):  
Retrial Queue ◽  
Control Policy ◽  
Single Server ◽  
Retrial Queueing System ◽  
Retrial Queueing ◽  
Virtual Waiting Time ◽  
The Laplace Transform ◽  
The Stability ◽  
Necessary And Sufficient ◽  
Number Of Customers

We consider a single-server retrial queueing system where retrial time is inversely proportional to the number of customers in the system. A necessary and sufficient condition for the stability of the system is found. We obtain the Laplace transform of virtual waiting time and busy period. The transient distribution of the number of customers in the system is also obtained.

scholarly journals An M/G/1 Retrial Queue with Fluctuating Modes of Services

2018 ◽  
Vol 7 (4.10) ◽  
pp. 762
Author(s):  
P. Rajadurai ◽  
S. Venkatesh ◽  
K. Parameswari
Keyword(s):  
Steady State ◽  
Queueing System ◽  
Retrial Queue ◽  
Probability Generating Function ◽  
Single Server ◽  
Supplementary Variable ◽  
Working Vacation ◽  
Variable Technique ◽  
Retrial Queueing System ◽  
Retrial Queueing

In this paper, we consider a single server retrial queueing system with working vacation and two classes of customers, which are priority customers and ordinary customers. The single server provides fluctuating modes (optional phases) of services. Using the method of Probability Generating Function (PGF) approach and supplementary variable technique, the steady state results are obtained. 

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