Retrial Queueing Systems

Artalejo, Jesús R.; Gómez‐Corral, Antonio

doi:10.1007/978-3-540-78725-9

Cited by 509 publications

(187 citation statements)

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“…The retrial queue is a special type of queueing system that takes into account the retrial phenomenon whereby arriving requests that find all the channels in a cell occupied or busy tend to retry after a random interval of time, following a random order or the first-in-first-out (FIFO) order [6,10,20]. The retrial queue model is found in various practical situations such as in retail shopping where a customer who finds a long waiting line may wish to do something else to return at a later time with the hope that the queue dissolves [16].…”

Section: The Retrial Queuementioning

confidence: 99%

“…As a matter of fact, the assumption that customers who leave the system as a result of inadequate channel resource are lost is just a first order approximation to a real situation. Usually such a customer returns to the system after a random time interval and tries to get service again [6,16]. Retrial queues have been shown to improve network quality and customer satisfaction [7,9,10,12].…”

Section: The Retrial Queuementioning

confidence: 99%

“…Most authors believe that handover requests are the best determinants for the level of the network's QoS parameters. However, standard queueing models do not take the retrial phenomenon into account and therefore cannot be applied in solving a number of practically important problems such as fairness between receptions of new and handover requests [6,11]. Various research methods combine the retrial queue model and maybe an additional prioritization technique for the handover calls such as an additional queue for the handover calls.…”

Section: Related Work 21 Standard Retrial Queue Modelsmentioning

confidence: 99%

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An integrated approach to solving retrial queue complexity

2018

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Retrial queues have been proposed in literature as a means of managing lost traffic in wireless cellular networks. However, the retrial queue system has led to a more complex Markovic process in network analysis. This complexity is further worsened by the addition of handover prioritization. In this paper, a solution that can minimize this complexity has been studied. The solution involves an integration of computational and analytical modeling techniques. The work was simulated and tested in Matlab/Simulink with guard channels as a handover prioritization scheme. Further evaluations were carried out on the effect of blocking probability (PB) as a quality of service (QoS) parameter. Numerical results obtained from the integrated approach show that the retrial queue reduces the PB for all requests (new and handover). The result obtained from this paper simplifies the solution to the complexity found in retrial queue systems. Furthermore, it was also deduced that reduced retrial rate with corresponding increase in the retrial queue size improves the network quality without an increase in system complexity.

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Section: The Retrial Queuementioning

confidence: 99%

Section: The Retrial Queuementioning

confidence: 99%

Section: Related Work 21 Standard Retrial Queue Modelsmentioning

confidence: 99%

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An integrated approach to solving retrial queue complexity

2018

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“…Queueing systems with repeated attempts are found suitable for modelling the processes in telephone switching systems, digital cellular mobile networks, packet switching networks, local area networks, stock and flow etc. Review of retrial queueing literature can be found in the bibliographies of Artalejo (1999aArtalejo ( , 1999b and the books by Falin and Templeton (1997) and Artalejo and Gomez Corral (2008). The applications of retrial queues in science and engineering are given in Kulkarni and Liang (1997).…”

Section: Introductionmentioning

confidence: 99%

An M[X]/G/1 Retrial G-Queue with Server Breakdown

A.Divya¹

2015

IJIRSET

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Batch arrival retrial queue with positive and negative customers is considered. If the server is idle upon the arrival of a batch, one of the customers in the batch receives service immediately and others join the orbit. If the server is busy, all the customers join the orbit. The arrival of negative customer brings the server down and removes the customer in service from the system. The server is subject to random breakdown while it is working. Using supplementary variable technique, expected number of customers in the orbit and expected number of customers in the system are derived. Stochastic decomposition property is established. Some special cases are discussed and numerical results are presented.

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“…These systems have received considerable attention in the literature, see e.g. the book by Falin and Templeton [7], and the more recent book by Artalejo and Gomez-Corral [3].…”

Section: Introductionmentioning

confidence: 99%

Vacation and Polling Models with Retrials

Boxma

Resing

2014

Computer Performance Engineering

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Abstract. We study a vacation-type queueing model, and a singleserver multi-queue polling model, with the special feature of retrials. Just before the server arrives at a station there is some deterministic glue period. Customers (both new arrivals and retrials) arriving at the station during this glue period will be served during the visit of the server. Customers arriving in any other period leave immediately and will retry after an exponentially distributed time. Our main focus is on queue length analysis, both at embedded time points (beginnings of glue periods, visit periods and switch-or vacation periods) and at arbitrary time points.

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Retrial Queueing Systems

Cited by 509 publications

References 0 publications

An integrated approach to solving retrial queue complexity

An integrated approach to solving retrial queue complexity

An M[X]/G/1 Retrial G-Queue with Server Breakdown

Vacation and Polling Models with Retrials

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