Stationary Queue Length Distribution Research Articles

Product Form Models for Queueing Networks with an Inventory

We investigate a new class of stochastic networks that exhibit a product form steady state distribution. The stochastic networks developed here are integrated models for networks of service stations and inventories. We integrate a server with attached inventory under (r, Q)- or (r, S)-policy into Jackson or Gordon-Newell networks. Replenishment lead times are non-zero and random and depend on the load of the system. While the inventory is depleted the server with attached inventory does not accept new customers (lost sales regime), but we assume that the lost sales are not lost to the system. We pursue three different approaches to handle routing with respect to this node during the time the inventory is empty. We derive stationary distributions of joint queue lengths and inventory processes in explicit product form. The stationary distributions are then used to calculate performance measures of the respective systems. We discuss the advantages and disadvantages of product form modeling in the context of service-inventory systems. Finally, we sketch two network models where several nodes may have an attached inventory.

Multi-server retrial queue with negative customers and disasters

We consider a multi-server retrial queue with waiting places in service area and four types of arrivals, positive customers, disasters and two types of negative customers, one for deleting customers in orbit and the other for deleting customers in service area. The four types of arrivals occur according to a Markovian arrival process with marked transitions (MMAP) which may induce the dependence among the arrival processes of the four types. We derive a necessary and sufficient condition for the system to be positive recurrent by comparing sample paths of auxiliary systems whose stability conditions can be obtained. We use a generalized truncated system that is obtained by modifying the retrial rates for an approximation of stationary queue length distribution and show the convergence of approximation to the original model. An algorithmic solution for the stationary queue length distribution and some numerical results are presented.

Stationary distribution of queue length in G / M / 1 queue with two-stage service policy

We consider a G / M / 1 queue with two-stage service policy. The server starts to serve with rate of μ1 customers per unit time until the number of customers in the system reaches λ. At this moment, the service rate is changed to that of μ2 customers per unit time and this rate continues until the system is empty. We obtain the stationary distribution of the number of customers in the system.

M/M/1 Queueing systems with inventory

We derive stationary distributions of joint queue length and inventory processes in explicit product form for various M/M/1-systems with inventory under continuous review and different inventory management policies, and with lost sales. Demand is Poisson, service times and lead times are exponentially distributed. These distributions are used to calculate performance measures of the respective systems. In case of infinite waiting room the key result is that the limiting distributions of the queue length processes are the same as in the classical M/M/1/?-system.

Analysis of BMAP/G/1 Queue with Reservation of Service

In this article, we study BMAP/G/1 queue with service time distribution depending on number of processed items. This type of queue models the systems with the possibility of preliminary service. For the considered system, an efficient algorithm for calculating the stationary queue length distribution is proposed, and Laplace–Stieltjes transform of the sojourn time is derived. Little's law is proved. An optimization problem is considered.

Equivalences of Batch-Service Queues and Multi-Server Queues and Their Complete Simple Solutions in Terms of Roots

In this article, we first present a unified discussion of several equivalence relationships among (as well as between) batch-service queues and multi-server queues, in terms of the stationary queue-length and waiting-time distributions. Then, we present a complete and simple solution for the queue-length and waiting-time distributions of the discrete-time multi-server deterministic-service Geo/D/b queue, in terms of roots of the so-called characteristic equation. This solution also represents the solutions for the other equivalent queues, as a result of the equivalence relationships. To aid in the applications of these results, sample numerical results are presented at the end.

Monotonicity properties in various retrial queues and their applications

We consider several multi-server retrial queueing models with exponential retrial times that arise in the literature of retrial queues. The effect of retrial rates on the behavior of the queue length process is investigated via sample path approach. We show that the number of customers in orbit and in the system as a whole are monotonically changed if the retrial rates in one system are bounded by the rates in second one. The monotonicity results are applied to show the convergence of generalized truncated systems that have been widely used for approximating the stationary queue length distribution in retrial queues.

AN MMAP[3]/PH/1 QUEUE WITH NEGATIVE CUSTOMERS AND DISASTERS

We consider a single-server queue with service time distribution of phase type where positive customers, negative customers and disasters arrive according to a Markovian arrival process with marked transitions (MMAP). We derive simple formulae for the stationary queue length distributions. The Laplace-Stieltjes transforms (LST's) of the sojourn time distributions under the combinations of removal policies and service disciplines are also obtained by using the absorption time distribution of a Markov chain.

Modeling and Performance Analysis of a Finite-Buffer Queue with Batch Arrivals, Batch Services, and Setup Times: The MX/GY/1/K + B Queue with Setup Times

In this paper, we consider a finite-buffer MX/GY/1/K + B queue with setup times, which has a wide range of applications. The primary purpose of this paper is to discuss both exact analytic and computational aspects of this system. For this purpose, we present a modern applied approach to queueing theory. First, we present a set of linear equations for the stationary queue-length distribution at a departure epoch based on the embedded Markov-chain technique. Next, using two simple approaches that are based on the conditioning of the system states and discrete renewal theory, we establish two numerically stable relationships for the stationary queue-length distributions at three different epochs: departure, random, and arrival. Finally, based on these relationships, we present useful performance measures of interest such as the moments of the stationary queue lengths at three different epochs, the blocking probability, the mean delay in queue, and the probability that the server is busy, with computational experience.

Analysis and Computational Algorithm for Queues with State-Dependent Vacations I: G/M(n)/1/K

In this paper we study a queueing system with state-dependent services and state-dependent vacations, or simply G/M(n)/1/K. Since the service rate is state-dependent, this system includes G/M/c and G/M/c/K queues with various types of station vacations as special cases. We provide a recursive algorithm using the supplementary variable technique to numerically compute the stationary queue length distribution of the system. The only input requirement is the Laplace-Stieltjes transform of the interarrival distribution as well as the state-dependent service rate and state-dependent vacation rate. In a subsequent companion paper, we study its dual system M(n)/G/1/K queue with state-dependent vacations.

Sample-path analysis of the proportional relation and its constant for discrete-time single-server queues

In the previous work, the authors have considered a discrete-time queueing system and they have established that, under some assumptions, the stationary queue length distribution for the system with capacity K1 is completely expressed in terms of the stationary distribution for the system with capacity K0 (>K1). In this paper, we study a sample-path version of this problem in more general setting, where neither stationarity nor ergodicity is assumed. We establish that, under some assumptions, the empirical queue length distribution (along through a sample path) for the system with capacity K1 is completely expressed only in terms of the quantities concerning the corresponding system with capacity K0 (>K1). Further, we consider a probabilistic setting where the assumptions are satisfied with probability one, and under the probabilistic setting, we obtain a stochastic version of our main result. The stochastic version is considered as a generalization of the author's previous result, because the probabilistic assumptions are less restrictive.

A two threshold vacation policy in multiserver queueing systems

We consider a queueing system with c servers and a threshold type vacation policy. In this system, when a certain number d < c of servers become idle at a service completion instant, these d servers will take a synchronous vacation of random length together. After each vacation, the number of customers in the system is checked. If that number is N or more, these d servers will resume serving the queue; otherwise, they will take another vacation together. Using the matrix analytical method, we obtain the stationary distribution of queue length and prove the conditional stochastic decomposition properties. Through numerical examples, we discuss the performance evaluation and optimization issues in such a vacation system with this ( d, N) threshold policy.

Factorization and Stochastic Decomposition Properties in Bulk Queues with Generalized Vacations

This paper considers a class of stationary batch-arrival, bulk-service queues with generalized vacations. The system consists of a single server and a waiting room of infinite capacity. Arrivals of customers follow a batch Markovian arrival process. The server is unavailable for occasional intervals of time called vacations, and when it is available, customers are served in groups of fixed size B. For this class of queues, we show that the vector probability generating function of the stationary queue length distribution is factored into two terms, one of which is the vector probability generating function of the conditional queue length distribution given that the server is on vacation. The special case of batch Poisson arrivals is carefully examined, and a new stochastic decomposition formula is derived for the stationary queue length distribution.

Analytic Computation Schemes for the Discrete-Time Bulk Service Queue

In commonly used root-finding approaches for the discrete-time bulk service queue, the stationary queue length distribution follows from the roots inside or outside the unit circle of a characteristic equation. We present analytic representations of these roots in the form of sample values of periodic functions with analytically given Fourier series coefficients, making these approaches more transparent and explicit. The resulting computational scheme is easy to implement and numerically stable. We also discuss a method to determine the roots by applying successive substitutions to a fixed point equation. We outline under which conditions this method works, and compare these conditions with those needed for the Fourier series representation. Finally, we present a solution for the stationary queue length distribution that does not depend on roots. This solution is explicit and well-suited for determining tail probabilities up to a high accuracy, as demonstrated by some numerical examples.

Applications of maximum queue lengths to call center management

This paper deals with the distribution of the maximum queue length in two-dimensional Markov models. In this framework, two typical assumptions are: (1) the stationary regime, and (2) the system homogeneity (i.e., homogeneity of the underlying infinitesimal generator). In the absence of these assumptions, the computation of the stationary queue length distribution becomes extremely intricate or, even, intractable. The use of maximum queue lengths provides an alternative queueing measure overcoming these problems. We apply our results to some problems arising from call center management.

An M/M/2 queueing system with heterogeneous servers and multiple vacations

In this paper, a Markovian queue with two heterogeneous servers and multiple vacations has been studied. For this system, the stationary queue length distribution and mean system size have been obtained by using matrix geometric method. The busy period analysis of the system and mean waiting time distribution are discussed. Extensive numerical illustrations are provided.

Effective bandwidth for a single server queueing system with fractional Brownian input

The traffic patterns of today’s IP networks exhibit two important properties: self-similarity and long-range dependence. The fractional Brownian motion is widely used for representing the traffic model with the properties. We consider a single server fluid queueing system with input process of a fractional Brownian motion type. Packet-loss probability and mean delay are considered as QoS. We show that there is a scaling property among the stationary queue-length distributions of different input parameters and service rates. We also evaluate the scaling factor. From the scaling property, we drive formulas for the effective bandwidth to guarantee the QoS in a single source and multiple sources cases. The formulas indicate that it is essential to evaluate the distribution functions of a type of random variables. For multiple sources the shape of an admissible region and multiplexing gain are analyzed. Finally, numerical examples are shown to validate the proposed scheme.

An Invariance Relation and a Unified Method to Derive Stationary Queue-Length Distributions

For a broad class of discrete- and continuous-time queueing systems, we show that the stationary number of customers in system (queue plus servers) is the sum of two independent random variables, one of which is the stationary number of customers in queue and the other is the number of customers that arrive during the time a customer spends in service. We call this relation an invariance relation in the sense that it does not change for a variety of single-sever queues (with batch arrivals and batch services) and some multiserver queues (with batch arrivals and deterministic service times) that satisfy a certain set of assumptions. Making use of this relation, we also present a simple method of deriving the probability generating functions (PGFs) of the stationary numbers in queue and in system, as well as some of their properties. This is illustrated by several examples, which show that new simple derivations of old results as well as new results can be obtained in a unified manner. Furthermore, we show that the invariance relation and the method we are presenting are easily generalized to analyze queues with batch Markovian arrival process (BMAP) arrivals. Most of the results are presented under the discrete-time setting. The corresponding continuous-time results, however, are covered as well because deriving the results for continuous-time queues runs exactly parallel to that for their discrete-time counterparts.

Error Bounds for Perturbing Nonexponential Queues

A general framework is provided to derive analytic error bounds for the effect of perturbations and inaccuracies of nonexponential service or arrival distributions in single- and multiserver queues. The general framework is worked out in detail for the three types of finite or infinite buffer queues: GI/G/1/N, M/G/c/N, and GI/M/c/N. First, for the standard GI/G/1/N queue, it is illustrated how the general error bound result can lead to error bounds for different performance measures like the throughput, mean queue length, and stationary queue length distribution. Next, for the M/G/c/N queue, an error bound and monotonicity result are established for the throughput. M/G/c/N queues can so be compared even when hazard rates are not ordered. Finally, for the GI/M/c/N queue, a similar result is obtained with a perturbation of the interarrival time distribution. The error bound results are supported by asymptotic expressions for the M/M/c/N queue and numerical results for the GI/G/1/N queue.

Flow equivalence and stochastic equivalence in G-networks

G-networks are novel product form queuing networks that, in addition to ordinary customers, contain unusual entities such as “negative customers” which eliminate normal customers, and “triggers” that move other customers from some queue to another. Recently we introduced one more special type of customer, a “reset”, which may be sent out by any server at the end of a service epoch, and that will reset the queue to which it arrives into its steady state when that queue is empty. A reset which arrives to a non-empty queue has no effect at all. The sample paths of a system with resets is significantly different from that of a system without resets, because the arrival of a reset to an empty queue will provoke a finite positive jump in queue length which may be arbitrarily large, while without resets positive jumps are only of size + 1 and they occur only when a positive customer arrives to a queue. In this paper we review this novel model, and then discuss its traffic equations. We introduce the concept of “stationary equivalence” for queueing models, and of “flow equivalence” for distinct queueing models. We show that the flow equivalence of two G-networks implies that they are also stationary equivalent. We then show that the stationary probability distribution of a G-network with resets is identical to that of a G-network without resets whose transition probabilities for positive (ordinary) customers has been increased in a specific manner. Our results show that a G-network with resets has the same form of traffic equations and the same joint stationary probability distribution of queue length as that of a G-network without resets.