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Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
by
Kopats, Dmitry Y.
, Dudina, Olga S.
, Dudin, Sergei A.
, Imomov, Azam A.
in
Admission control
/ Analysis
/ asymptotically quasi-Toeplitz Markov chain
/ batch Markov arrival process
/ Customer services
/ Customers
/ File servers
/ impatience
/ Markov chains
/ Markov processes
/ phase-type service time distribution
/ Quality of service
/ Queuing theory
/ Random variables
/ retrial admission control
2025
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Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
by
Kopats, Dmitry Y.
, Dudina, Olga S.
, Dudin, Sergei A.
, Imomov, Azam A.
in
Admission control
/ Analysis
/ asymptotically quasi-Toeplitz Markov chain
/ batch Markov arrival process
/ Customer services
/ Customers
/ File servers
/ impatience
/ Markov chains
/ Markov processes
/ phase-type service time distribution
/ Quality of service
/ Queuing theory
/ Random variables
/ retrial admission control
2025
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Do you wish to request the book?
Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
by
Kopats, Dmitry Y.
, Dudina, Olga S.
, Dudin, Sergei A.
, Imomov, Azam A.
in
Admission control
/ Analysis
/ asymptotically quasi-Toeplitz Markov chain
/ batch Markov arrival process
/ Customer services
/ Customers
/ File servers
/ impatience
/ Markov chains
/ Markov processes
/ phase-type service time distribution
/ Quality of service
/ Queuing theory
/ Random variables
/ retrial admission control
2025
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Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
Journal Article
Analysis of BMAP/PH/N-Type Queueing System with Flexible Retrials Admission Control
2025
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Overview
This research examines a multi-server retrial queueing system with a batch Markov arrival process and a phase-type service time distribution. The system’s distinguishing feature is its ability to control the admission of retrial customers. An attempt by a customer to retry is successful only if the number of busy servers does not exceed certain threshold values, which may depend on the state of the fundamental process of the primary customer’s arrival. Impatient retrying customers may abandon the system without obtaining service. A group of primary customers that arrives while the number of available servers is fewer than the group size is either entirely rejected or occupies all available servers, while the remainder of the group transitions to the orbit. The system’s behavior, under a defined set of thresholds, is characterized by a multidimensional Markov chain classified as asymptotically quasi-Toeplitz. This enables the acquisition of the ergodicity condition and the computation of the steady-state distribution of the Markov chain and the system’s performance measures. The presented numerical examples demonstrate the impact of threshold value variation. An example of solving an optimization problem is presented. The importance of the account of the batch arrivals is shown.
Publisher
MDPI AG
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