Analysis of a semi-open queueing network with Markovian arrival process

2018 ◽  
Vol 120 ◽  
pp. 1-19 ◽  
Author(s):  
Jiseung Kim ◽  
Alexander Dudin ◽  
Sergey Dudin ◽  
Chesoong Kim
2007 ◽  
Vol 44 (02) ◽  
pp. 306-320
Author(s):  
Marc Lelarge

A network belongs to the monotone separable class if its state variables are homogeneous and monotone functions of the epochs of the arrival process. This framework contains several classical queueing network models, including generalized Jackson networks, max-plus networks, polling systems, multiserver queues, and various classes of stochastic Petri nets. We use comparison relationships between networks of this class with independent and identically distributed driving sequences and the GI/GI/1/1 queue to obtain the tail asymptotics of the stationary maximal dater under light-tailed assumptions for service times. The exponential rate of decay is given as a function of a logarithmic moment generating function. We exemplify an explicit computation of this rate for the case of queues in tandem under various stochastic assumptions.


2009 ◽  
Vol 50 (5-6) ◽  
pp. 879-884 ◽  
Author(s):  
Rafael Pérez-Ocón ◽  
Maria del Carmen Segovia

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 825
Author(s):  
Chesoong Kim ◽  
Sergei Dudin ◽  
Olga Dudina

We consider a queueing network with a finite number of nodes and servers moving between the nodes as a model of car sharing. The arrival process of customers to various nodes is defined by a marked Markovian arrival process. The customer that arrives at a certain node when there is no idle server (car) is lost. Otherwise, he/she is able to start the service. With known probability, which depends on the node and the number of available cars, this customer can balk the service and leave the system. The service time of a customer has an exponential distribution. Location of the server in the network after service completion is random with the known probability distribution. The behaviour of the network is described by a multi-dimensional continuous-time Markov chain. The generator of this chain is derived which allows us to compute the stationary distribution of the network states. The formulas for computing the key performance indicators of the system are given. Numerical results are presented. They characterize the dependence of some performance measures of the network and the nodes on the total number of cars (fleet size of the car sharing system) and correlation in the arrival process.


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