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2014, Lecture Notes in Computer Science
We consider a P H/P H/1 queue in which a threshold policy determines the stage of the system. The arrival and service processes follow a Phase-Type (P H) distribution depending on the stage of the system. Each stage has both a lower and an upper threshold at which the stage of the system changes, and a new stage is chosen according to a prescribed distribution. This P H/P H/1 multi-threshold queue is modelled as a Level Dependent Quasi-Birth-and-Death process. An efficient algorithm is presented to obtain the stationary queue length vectors using Matrix Analytic methods.
Computers & Industrial Engineering, 1998
An M/G/1 queue with general server setup time under a control policy is studied. We consider the case when the arrival rate varies according to the server's status: idle, setup and busy states. For this model, the optimal N-value from which the server starts his setup is found by minimizing the total operation cost of the system.
Proceedings of the 2009 Winter Simulation Conference (WSC), 2009
In this paper, we consider a preemptive (multiple) priority queueing model in which arrivals occur according to a Markovian arrival process (MAP). An arriving customer belongs to priority type i, 1 ≤ i ≤ m + 1, with probability p i. The highest priority, labeled as 0, is generated by other priority customers while waiting in the system and not otherwise. Also, a customer of priority i can turn into a priority j, j = i, 1 ≤ i, j ≤ m + 1, customer, after a random amount of time that is assumed to be exponentially distributed with parameter depending on the priority type. The waiting spaces for all but priority type m + 1 are assumed to be finite. The (m + 1) − st priority customers have unlimited waiting space. At any given time, the system can have at most one highest priority customer. Thus, all priority customers except the (m + 1) − st are subject to loss. Customers are served on a first-come-first-served basis within their priority by a single server and the service times are assumed to follow a phase type distribution that may depend on the customer priority type. This queueing model, which is a level-dependent quasi-birth-and-death process, is amenable for investigation algorithmically through the well-known matrix-analytic methodology. However, here we propose to study through simulation using ARENA, a powerful simulation software as some key measures such as the waiting time distributions are highly complex to characterize analytically. The simulated results for a few scenarios are presented.
Queueing Systems - Theory and Applications, 2010
We consider decay properties including the decay parameter, invariant measures and quasi-stationary distributions for a Markovian bulk-arrival and bulk-service queue which stops when the waiting line is empty. Investigating such a model is crucial for understanding the busy period and other related properties of the Markovian bulk-arrival and bulk-service queuing processes. The exact value of the decay parameter λ C
OPSEARCH, 2019
This paper investigates an optimal K-policy for a two-server Markovian queueing system M∕(M 1 , M 2)∕2∕(B 1 , B 2), with one fast server S 1 and one slow server S 2 , using the matrix analytic method. Two buffers B 1 and B 2 are organized to form waiting lines of customers in which, buffer B 1 is of finite size K(< ∞) and buffer B 2 is of infinite capacity. Buffer B 1 stalls customers who arrive when the system size (queue + service) is less than (K + 1) and dispatches a customer to the fast server S 1 only after S 1 completes its previous service. This K-policy is of threshold type which deals with controlling of informed customers and hence the customers have better choice of choosing the fast server routing through the buffer B 1. The (K + 2)-nd customer who arrives when the number of customers present in the system is exactly (K + 1) has the Hobson's choice of getting service from the slow server S 2. Buffer B 2 accommodates other customers who arrive when the number of customers present in the system is (K + 2) or more and feeds them one after another to either buffer B 1 or the sever S 2 whichever event can first accept the customer at the head-of-the-line in B 2. Queue length processes of interest are (1) q 1 = lim t→∞ X 1 (t) and (2) q 2 = lim t→∞ X 2 (t) , where X 1 (t) represents the number of customers who are in the buffers B 1 and B 2 and also in the service with server S 1 at time 't' and X 2 (t) represents the number of customers available with server S 2 only. The bi-variate random sequence (t) = (X 1 (t), X 2 (t)) of the system size (queue + service) forms a quasi-birth and death process (QBD). Steady state characteristics, and some of the performance measures such as the expected queue length, the probability that each server is busy etc are obtained. Numerical illustrations are provided based on the average cost function to explore the methodology of finding the best K-policy which minimizes the mean sojourn time of customers. Keywords QBD processes and M∕(M 1 ,M 2)∕2∕(B 1 ,B 2) • Fast server • Slow server • Matrix analytic method • Stationary distribution
1996
In this chapter, we develop computational techniques for the time-dependent solution of the queue length distribution for a class of non-Markovian queueing systems. The class of systems we consider are those for which the queue length process is Markov regenerative. We consider standard single server nite capacity queues such as the M/G/1/K and the GI/M/1/K queues. We also show how these algorithms can be extended to more general arrival processes such as the Batch Markovian Arrival Process (BMAP) and to multiclass queueing systems.
Probability in the Engineering and Informational Sciences, 2011
We consider M/G/1-type Markov chains where a transition that decreases the value of the level triggers the phase to a small subset of the phase space. We show how this structure—referred to as restricted downward transitions—can be exploited to speed up the computation of the stationary probability vector of the chain. To this end we define a new M/G/1-type Markov chain with a smaller block size, the G matrix of which is used to find the original chain's G matrix. This approach is then used to analyze the BMAP/PH/1 queue and the BMAP[2]/PH[2]/1 preemptive priority queue, yielding significant reductions in computation time.
Queueing Systems, 2004
We consider two types of queues with workload-dependent arrival rate and service speed. Our study is motivated by queueing scenarios where the arrival rate and/or speed of the server depends on the amount of work present, like production systems and the Internet.
2014
In this paper we present probability density function of vacation period of M/G/1 queueing process that operates under (0,k) vacation policy, wherein the server goes on the vacation when the system becomes empty and reopens for service immediately at the arrival of the k th customer. The number of lattice paths when last arrival is an arrival has also been derived. The transient analysis is based on approximating the general service time distribution by Coxian two-phase distribution and representing the corresponding queueing process as a lattice path. Finally the lattice path combinatorics is used to present the number of lattice paths.
Mathematical and Computer Modelling, 2011
We consider an infinite-buffer single-server queue with renewal input. The service to the queueing system is provided in batches of random size, according to a batch Markovian service process (BMSP). The queue length distribution of the number of customers in the system at pre-arrival and arbitrary epochs has been obtained along with some important performance measures, such as the mean number of customers in the system and the mean system sojourn time of a customer. Secondly, we study a similar queueing system with queue-length-dependent inter-arrival times and obtain the abovementioned state probabilities and performance measures. These queueing models have potential applications in the areas of computer networks, telecommunication systems, manufacturing systems, etc.
Communications in Computer and Information Science, 2016
We consider two server queueing system with an infinite buffer. Customers arrive to the system according to the Markovian Arrival Process. Service time of a customer has a phase-type distribution. The servers use the same equipment (phases of P H) for customers processing. So, if service of a customer transits to the phase, at which another server is currently providing the service, the service of the customer is suspended until the phase will become available. Behavior of the system is described by the multi-dimensional Markov chain. The generator of this Markov chain is derived. Expressions for computation of the main performance measures are derived.
2013
In this paper, we discuss a finite capacity queue with integrated traffic and batch services under N-policy. There are two types of customer arrive according to poisson distribution. As soon as the queue size reaches the threshold level N, the all server C are turned on and serves both types of traffic one by one upto threshold level d( >C) of the customers in the system. After the threshold level d, the server provides the service of the type 1 customers in a batch whereas type 2 customers are lost. The queue size distribution is derived with the help of recursive method. Various performance measures, i.e. expected number of customers in the queue and in the system, probability of the server being turn off, under setup, busy and the expected idle/busy period etc. are determined.
Operations Research Letters, 2004
We study a GI=M=1 queue with an N threshold policy. In this system, the server stops attending the queue when the system becomes empty and resumes serving the queue when the number of customers reaches a threshold value N. Using the embeded Markov chain method, we obtain the stationary distributions of queue length and waiting time and prove the stochastic decomposition properties.
2013
This paper presents a discrete-time single-server finite buffer N threshold policy queue with renewal input and discrete Markovian service process. The server terminates service whenever the system becomes empty, and recommences service as soon as the number of waiting customers in the queue is N . We obtain the system-length distributions at prearrival and arbitrary epochs using the supplementary variable and the imbedded Markov chain techniques. Various performance measures such as the loss probability, mean queue length and mean waiting time in the queue along with some numerical results have been presented. The proposed model has potential applications in the areas of computer and telecommunication systems.
Applied Mathematical Modelling, 1996
In this paper we study the time-dependent analysis of a limited capacity queueing model with the bulk arrival rate depending upon the nature of service available in the system. The customers arrive in the system in batches of size x, which is a random variable, and the service consists of two stages, one is essential (first stage) while the other may be inessential. The decision to offer the inessential service depends upon the size of the system. However, if this inessential service is temporarily suspended, the arrival rate of the customers decreases. Laplace transforms (in time) of the different probability generating functions describing the system size under various conditions of service and the expected system size are derived. Steady-state results consequently follow.
Performance Evaluation, 1997
A new approximate method is developed for finding the waiting and sojourn time distributions in a class of multi-queue systems served in cyclic order at discrete intervals. An immediate application for such a model is in communication networks where a number of different traffic sources compete to access a group of transmission channels operating under a time-slotted sharing policy. This system maps naturally onto a model in which the inter-visit time has a probability mass function of phase-type. We derive a set of matrix equations with easily tractable iterative procedures for their solution and controllable accuracy in their numerical evaluation. We then validate the analytical model against simulation and discuss the validity of the assumptions. This methodology can be extended to several other polling strategies. © 1997 Elsevier Science B.V. a stochastic decomposition of the system's unfinished work. E:cact expressions for weighted sums of mean waiting times were derived using this method (see [i]). Several other methods have been developed for computing the mean delay, the mean queue length for each queue, the amount of work of the server and the cycle time. A survey of these methods is given by Levy and Sidi (see [ 10]). This paper tackles the problem of finding an approximate method for evaluating the waiting time pi ,'Jability density function in multi-queue systems with discrete service time. More abstractly, it also considers the sojourn ~.ime distribution of systems with exceptional first service times. A direct application for such systems can be found in time-slotted medium access protocols. In these systems several traffic sources compete to use a group of transmission channels. These transmitters are available only at the beginning of fixed length time-slots. At each discrete interval a single server starts visiting queues in cyclic order and assigning packets to the transmitters.
Performance Evaluation, 2007
We consider a discrete time single server queue with discrete autoregressive process of order 1 (DAR(1)) input. By extracting a Markov process from the queue size process and applying the BASTA property, we derive simple recursive formulae for the stationary distributions of the queue size and the waiting time. These formulae are simple, numerically stable and transformfree. A stochastic decomposition property is given for the stationary waiting time, and relations between the distributions of the stationary queue size and the stationary waiting time are discussed. Numerical examples are given for stationary distributions of the queue size and the waiting time for various DAR(1) inputs.
Applied and Computational Mathematics, 2014
In this study we have obtained stochastic equation systems of a Coxian queueing model with two phases where arrival stream of this model is according to the exponential distribution with λ parameter. The service time of any customer at server 1,2 is exponential with parameter. In addition we have obtained state probabilities of this queueing model at any given moment.Furthermore performance measures of this queueing system are calculated. Various queueing systems are found for some values of probability and service parameters: if 1and µ µ taken then / /1/ 0 queueing model is obtained, for 1it is shown that service time of a customer is according to hypoexponential, if 0 is taken we have / /1/ 0 queueing system. Lately,an application of this queueing model is done. The optimal value of the mean customer number in the system is found. Finally, optimal ordering according to the loss probability is obtained by changing the service parameters .A numerical example is given on the subject
Journal of Applied Probability, 2003
We study the multi-server queue with Poisson arrivals and identical independent servers with exponentially distributed service times. Customers arriving to the system are admitted or rejected according to a fixed threshold policy. Moreover, the system is subject to holding, waiting, and rejection costs. We give a closed-form expression for the average costs and the value function for this multi-server queue. The result will then be used in a single step of policy iteration in the model where a controller has to route to several finite buffer queues with multiple servers. We numerically show that the improved policy has a close to optimal value.
Performance Evaluation, 2011
Most research concerning batch-service queueing systems has focussed on some specific aspect of the buffer content. Further, the customer delay has only been examined in the case of single arrivals. In this paper, we examine three facets of a threshold-based batch-service system with batch arrivals and general service times. First, we compute a fundamental formula from which an entire gamut of known as well as new results regarding the buffer content of batch-service queues can be extracted. Secondly, we produce accurate light-and heavy-traffic approximations for the buffer content. Thirdly, we calculate various quantities with regard to the customer delay. This paper thus provides a whole spectrum of tools to evaluate the performance of batch-service systems.
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