Product form stationary distributions for queueing networks with blocking and rerouting

In this paper we study Markovian queueing networks in which the service and the routing characteristics have a particular form which leads to a product form stationary distribution for the number of customers in the various queues of the network. We show that if certain transitions are prohibited due to blocking conditions, then the form of the stationary distribution is preserved under a certain rerouting protocol. Several examples are presented which illustrate the wide applicability of the model. This revised version was published online in June 2006 with corrections to the Cover Date.     

A General Decomposition Algorithm for Parallel Queues with Correlated Arrivals

Queueing with correlated arrivals occurs when customers arrive at a set of queues simultaneously. The difficulty in analyzing systems with correlated arrivals is due to the fact that the individual queueing systems are stochastically dependent. Exact methods for analyzing these systems are computationally intensive and are limited to only a few special cases. In this paper, we consider a system of parallel queues with bulk service and correlated arrivals. We show how the matrix-geometric approach can be used to obtain the performance measures of the system. We also develop an algorithm for large systems that efficiently approximates the performance measures by decomposing it into individual queueing systems. Finally, we describe how the principles of our decomposition algorithm can be extended to analyze a variety of different parallel queueing systems with correlated arrivals. We then evaluate the accuracy of our algorithm through a numerical study.     

Estimating characteristics of queueing networks using transactional data

We are motivated by queueing networks in which queues are difficult to observe but services are easy to record. Our goal is to estimate the queues from service data. More specifically, we consider an open queueing network with Poisson external arrivals, multi‐server stations, general service times and Markovian switches of customers between stations. Customers' transitions between stations may be either immediate or of exponentially distributed durations. Each customer is supplied with an Identification Number (ID) upon entering the network. Operational data is collected which includes transaction times (starts and terminations of services) and ID's of served customers. Our objective is to estimate the evolution of the queues in the network, given the collected data. We cover estimation at both end of busy periods and in real time. The applicability of the theory is demonstrated by analyzing a service operation. This revised version was published online in June 2006 with corrections to the Cover Date.     

Moderate deviations for queues in critical loading

We establish logarithmic asymptotics of moderate deviations for queue-length and waiting-time processes in single server queues and open queueing networks in critical loading. Our results complement earlier diffusion approximation results. This revised version was published online in June 2006 with corrections to the Cover Date.     

Higher order approximations for tandem queueing networks

In this paper a higher order approximation for single server queues and tandem queueing networks is proposed and studied. Different from the most popular two-moment based approximations in the literature, the higher order approximation uses the higher moments of the interarrival and service distributions in evaluating the performance measures for queueing networks. It is built upon the MacLaurin series analysis, a method that is recently developed to analyze single-node queues, along with the idea of decomposition using higher orders of the moments matched to a distribution. The approximation is computationally flexible in that it can use as many moments of the interarrival and service distributions as desired and produce the corresponding moments for the waiting and interdeparture times. Therefore it can also be used to study several interesting issues that arise in the study of queueing network approximations, such as the effects of higher moments and correlations. Numerical results for single server queues and tandem queueing networks show that this approximation is better than the two-moment based approximations in most cases.     

A survey on retrial queues

Queueing systems in which arriving customers who find all servers and waiting positions (if any) occupied may retry for service after a period of time are called retrial queues or queues with repeated orders. Retrial queues have been widely used to model many problems in telephone switching systems, telecommunication networks, computer networks and computer systems. In this paper, we discuss some important retrial queueing models and present their major analytic results and the techniques used. Our concentration is mainly on single-server queueing models. Multi-server queueing models are briefly discussed, and interested readers are referred to the original papers for details. We also discuss the stochastic decomposition property which commonly holds in retrial queues and the relationship between the retrial queue and the queue with server vacations.     

A Markovian single server with upstream job and downstream demand arrival stream

In this paper we consider a Markovian single server system which processes items arriving from an upstream region (as usual in queueing systems) and is controlled by a demand arrival stream for finished items from a downstream area. A finite storage is available at the server to store finished items not immediately needed in the downstream area. The system considered corresponds to an assembly-like queue with two input streams. The system is stable in a strict sense only if all queues are finite, i.e., both random processes are synchronized via blocking. This notion leads to a complementary system with a very similar state space which is a pair of Markovian single servers with synchronous arrivals. In the mathematical analysis the main focus is on the state probabilities and expectation of minimum and maximum of the two input queues. This revised version was published online in June 2006 with corrections to the Cover Date.     

Deadlock free buffer allocation in closed queueing networks

Blocking queueing networks are of much interest in performance analysis due to their realistic modeling capability. One important feature of such networks is that they may have deadlocks which can occur if the node capacities are not sufficiently large. A necessary and sufficient condition for the node capacities is presented such that the network is deadlock free. An algorithm is given for buffer allocation in blocking queueing networks such that no deadlocks will occur assuming that the network has the special structure called cacti-graph. Additional algorithm which takes linear time in the number of nodes, is presented to find cycles in cacti networks.Akyildiz's work was supported in part by School of Information and Computer Science, ICS, of Georgia Tech and by the Air Force Office of the Scientific Research (AFOSR) under Grant AFOSR-88-0028.     

On a class of approximations for closed queueing networks

This paper presents some analytical results concerning an approximation procedure for closed queueing networks. The procedure is well-known and has been found useful for product-form networks where large numbers of queues, jobs or job classes prohibit an exact analysis, as well as for networks which do not possess product-form. The procedure represents the mean sojourn time at a queue as a function of the throughput of the queue, and derives a set of fixed point equations for the throughputs of the various job classes. We begin by showing that under a mild regularity condition the fixed point equations have a unique solution. Then we show that derivatives of performance measures can be readily calculated, and that their simple form provides an interesting insight into capacity allocation in closed queueing networks.This work was supported in part by the Nuffield Foundation     

Queueing Networks of Random Link Topology: Stationary Dynamics of Maximal Throughput Schedules

In this paper, we study the stationary dynamics of a processing system comprised of several parallel queues and a single server of constant rate. The connectivity of the server to each queue is randomly modulated, taking values 1 (connected) or 0 (severed). At any given time, only the currently connected queues may receive service. A key issue is how to schedule the server on the connected queues in order to maximize the system throughput. We investigate two dynamic schedules, which are shown to stabilize the system under the highest possible traffic load, by scheduling the server on the connected queue of maximum backlog (workload or job number). They are analyzed under stationary ergodic traffic flows and connectivity modulation. The results also extend to the more general case of random server rate.We then investigate the dynamics of acyclic (feed-forward) queueing networks with nodes of the previous type. Their links (connectivities) are stochastically modulated, inducing fluctuating network topologies. We focus on the issue of network throughput and show that it is maximized by simple node server schedules. Rate ergodicity of the traffic flows traversing the network is established, allowing the computation of the maximal throughput.Queueing networks of random topology model several practical systems with unreliable service, including wireless communication networks with extraneous interference, flexible manufacturing systems with failing components, production management under random availability of resources etc.Research supported in part by the National Science Foundation.This revised version was published online in June 2005 with corrected coverdate     

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The discriminatory processor sharing queues with multiple classes of customers (abbreviated as DPS queues) are an important but difficult research direction in queueing theory, and it has many important practical applications in the fields of, such as, computer networks, manufacturing systems, transportation networks, and so forth. Recently, researchers have carried out some key work for the DPS queues. They gave the generating function of the steady-state joint queue lengths, which leads to the first two moments of the steady-state joint queue lengths. However, using the generating function to provide explicit expressions for the steady-state joint queue lengths has been a difficult and challenging problem for many years. Based on this, this paper applies the maximum entropy principle in the information theory to providing an approximate expression with high precision, and this approximate expression can have the same first three moments as those of its exact expression. On the other hand, this paper gives efficiently numerical computation by means of this approximate expression, and analyzes how the key variables of this approximate expression depend on the original parameters of this queueing system in terms of some numerical experiments. Therefore, this approximate expression has important theoretical significance to promote practical applications of the DPS queues. At the same time, not only do the methodology and results given in this paper provide a new line in the study of DPS queues, but they also provide the theoretical basis and technical support for how to apply the information theory to the study of queueing systems, queueing networks and more generally, stochastic models.     

Deadlock properties of queueing networks with finite capacities and multiple routing chains

Blocking in queueing network models with finite capacities can lead to deadlock situations. In this paper, deadlock properties are investigated in queueing networks with multiple routing chains. The necessary and sufficient conditions for deadlockfree queueing networks with blocking are provided. An optimization algorithm is presented for finding deadlock-free capacity assignments with the least total capacity. The optimization algorithm maps the queueing network into a directed graph and obtains the deadlock freedom conditions from a specified subset of cycles in the directed graph. In certain network topologies, the number of deadlock freedom conditions can be large, thus, making any optimization computationally expensive. For a special class of topologies, so-calledtandem networks, it is shown that a minimal capacity assignment can be directly obtained without running an optimization algorithm. Here, the solution to the minimal capacity assignment takes advantage of the regular topology of tandem networks.This work was supported by the National Science Foundation under Grant No. CCR-90-11981.     

Generalized M/G/C/C state dependent queueing models and pedestrian traffic flows

The generality and usefulness ofM/G/C/C state dependent queueing models for modelling pedestrian traffic flows is explored in this paper. We demonstrate that the departure process and the reversed process of these generalizedM/G/C/C queues is a Poisson process and that the limiting distribution of the number of customers in the queue depends onG only through its mean. Consequently, the models developed in this paper are useful not only for the analysis of pedestrian traffic flows, but also for the design of the physical systems accommodating these flows. We demonstrate how theM/G/C/C state dependent model is incorporated into the modelling of large scale facilities where the blocking probabilities in the links of the network can be controlled. Finally, extensions of this work to queueing network applications where blocking cannot be controlled are also presented, and we examine an approximation technique based on the expansion method for incorporating theseM/G/C/C queues in series, merge, and splitting topologies of these networks.     

Nonstationary analysis of the loss queue and of queueing networks of loss queues

We present an iterative scheme based on the fixed-point approximation method, for the numerical calculation of the time-dependent mean number of customers and blocking probability functions in a nonstationary queueing network with multi-rate loss queues. We first show how the proposed method can be used to analyze a single-class, multi-class, and multi-rate nonstationary loss queue. Subsequently, the proposed method is extended to the analysis of a nonstationary queueing network of multi-rate loss queues. Comparisons with exact and simulation results showed that the results are consistently close to the exact results and they are always within simulation confidence intervals.     

An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Analytic queueing network models often assume infinite capacity queues due to the difficulty of grasping the between-queue correlation. This correlation can help to explain the propagation of congestion. We present an analytic queueing network model which preserves the finite capacity of the queues and uses structural parameters to grasp the between-queue correlation. Unlike pre-existing models it maintains the network topology and the queue capacities exogenous. Additionally, congestion is directly modeled via a novel formulation of the state space of the queues which explicitly captures the blocking phase. The model can therefore describe the sources and effects of congestion.     

Blocking Probabilities in Mobile Communications Networks with Time-Varying Rates and Redialing Subscribers

Call-blocking probabilities are among the key performance measures in mobile communications networks. For their analysis, mobile networks can be modelled as networks of Erlang loss queues with common capacity restrictions dictated by the allocation of frequencies to the cells of the network. However, due to the time-varying load offered to the cells of such networks, blocking probabilities usually cannot be obtained in closed form. The relation between networks of Erlang loss queues and networks of infinite server queues, for which the time-dependent occupancy distribution is multidimensional Poisson, suggests to use that distribution as approximate distribution for the network of Erlang loss queues. This paper extends this so-called Modified Offered Load (MOL) approximation to networks of Erlang loss queues, and also allows subscribers that find their call blocked to redial to continue their call. For GSM networks operating under Fixed Channel Allocation, it is shown that blocking probabilities are increasing in the redial rates so that the MOL approximation that is most accurate for maximal redial rates turns out to be fairly accurate for the resulting upper bound for blocking probabilities. The accuracy is explicitly evaluated in an application of the results towards blocking probabilities in a hot spot travelling along a road through a GSM network.     

A note on stable flow-equivalent aggregation in closed networks

We introduce the Conditional Mean Value Analysis (CMVA) algorithm, an exact solution method for product-form load-dependent closed queueing networks that provides a numerically stable solution of models where the load-dependent Mean Value Analysis (MVA) is numerically unstable. Similarly to the MVA algorithm for constant-rate queues, CMVA performs operations in terms of mean quantities only, i.e., queue-lengths, throughput, response times. Numerical stability derives from a new version of the MVA arrival theorem for load-dependent models which is expressed in terms of mean queue-lengths instead of marginal probabilities. The formula is obtained by the analysis of the conditional state spaces which describe network equilibrium as seen by jobs during their residence times at queues. We also provide a generalization of CMVA to multiclass models that preserves the numerical stability property.     

Near optimal control of queueing networks over a finite time horizon

We propose a method for the control of multi-class queueing networks over a finite time horizon. We approximate the multi-class queueing network by a fluid network and formulate a fluid optimization problem which we solve as a separated continuous linear program. The optimal fluid solution partitions the time horizon to intervals in which constant fluid flow rates are maintained. We then use a policy by which the queueing network tracks the fluid solution. To that end we model the deviations between the queuing and the fluid network in each of the intervals by a multi-class queueing network with some infinite virtual queues. We then keep these deviations stable by an adaptation of a maximum pressure policy. We show that this method is asymptotically optimal when the number of items that is processed and the processing speed increase. We illustrate these results through a simple example of a three stage re-entrant line. Research supported in part by Israel Science Foundation Grant 249/02 and 454/05 and by European Network of Excellence Euro-NGI.     

Exact asymptotic formulae of the stationary distribution of a discrete-time two-dimensional QBD process

We propose an analytically tractable approach for studying the transient behavior of multi-server queueing systems and feed-forward networks. We model the queueing primitives via polyhedral uncertainty sets inspired by the limit laws of probability. These uncertainty sets are characterized by variability parameters that control the degree of conservatism of the model. Assuming the inter-arrival and service times belong to such uncertainty sets, we obtain closed-form expressions for the worst case transient system time in multi-server queues and feed-forward networks with deterministic routing. These analytic formulas offer rich qualitative insights on the dependence of the system times as a function of the variability parameters and the fundamental quantities in the queueing system. To approximate the average behavior, we treat the variability parameters as random variables and infer their density by using ideas from queues in heavy traffic under reflected Brownian motion. We then average the worst case values obtained with respect to the variability parameters. Our averaging approach yields approximations that match the diffusion approximations for a single queue with light-tailed primitives and allows us to extend the framework to heavy-tailed feed-forward networks. Our methodology achieves significant computational tractability and provides accurate approximations for the expected system time relative to simulated values.