Properties and performance modelling of finite buffer M/G/1/K networks

Finite buffer, single-server queueing systems and networks are difficult to analyze since the length of time a customer spends in the system does not follow the Markovian property. A two-moment approximation schema is developed for the probability distribution of M/G/1/K systems and extended to the analysis of M/G/1/K   queueing networks. The general purpose of this paper is to develop a flexible and practical transform-free approach for computing the probability distribution and performance measures of the system as well as identify the underlying properties of these systems. It is shown that for most performance measures, a sigmoid or S-shaped curve with an inflection point at ρ=1$\rho =1$ appears as K→∞$K\to \infty$. This has direct implications for the analysis and optimization of such systems. The performance modelling of the M/G/1/K queueing networks of general topologies along with extensive numerical results accompany the paper along with the linear concave performance measures for these systems.     

Approximate analysis of M/G/c/c state-dependent queueing networks

Congestion is ever present in most practical situations. We describe a methodology for approximate analysis of open state-dependent M/G/c/c queueing networks in which the service rate is subject to congestion, that is, it is a function of the number of customers in the system. Important performance measurements are easily computed with high accuracy, such as the blocking probability, throughput, expected number of customers in the system (known also as work-in-process), and expected waiting time. The methodology forms a basic building block useful in many practical applications and contexts. Computational results demonstrate that the methodology provides accurate results in many topological configurations as well as in the analysis of network evacuation problems in high-rise buildings.     

Approximating queue lengths inM(t)/G/1 queue using the maximum entropy principle

Using the discrete time approach a model is developed for obtaining the expected queue length of theM(t)/G/1 queue. This type of queue occurs in different forms in transportation and traffic systems and in communications and manufacturing systems. In order to cut down the very high computational efforts required to evaluate the performance measures in such queues by exact methods, the Maximum Entropy Principle is used to approximate the expected queue length which is one of the most commonly used performance measures. A procedure is then developed for reducing the error encountered when this approximation is adopted. The results from this paper will encourage the practitioners to use the appropriate time-varying queueing models when the need arises instead of resorting to very poor approximations.     

An M/G/C/C state-dependent network simulation model

A discrete-event digital simulation model is developed to study traffic flows in M/G/C/C state-dependent queueing networks. Several performance measures are evaluated, namely (i) the blocking probability, (ii) throughput, (iii) the expected number of the customers in the system, and (iv) expected travel (service) time. Series, merge, and split topologies are examined with special application to pedestrian planning evacuation problems in buildings. Extensive computational experiments are presented showing that the simulation model is an effective and insightful tool to validate analytical expressions and also to analyze general accessibility in network evacuation problems especially in high-rise buildings.     

A diffusion approximate solution to the g/g/k queueing system

In the opening sections of this paper we present a diffusion approximate solution to a G/G/k queueing station under steady state condtions. The approximation technique is computationally simple and practical in that it requires knowledge of the means and variances of the interarrival and service time distributions, but not their distributional forms. Several numeric examples attesting to the good quality of the approximation are also included. We then discuss extending the basic approximation technique to account first for a feed back option and then to networks of multiserver queueing stations.     

A maximum entropy priority approximation for a stableG/G/1 queue

Summary The principle of maximum entropy is used under two different sets of mean value constraints to analyse a stableG/G/1 queue withR priority classes under preemptive-resume (PR) and non-preemptive head-of-line (HOL) scheduling disciplines. New one-step recursions for the maximum entropy state probabilities are established and closed form approximations for the marginal queue length distribution per priority class are derived. To expedite the utility of the maximum entropy solutions exact analysis, based on the generalised exponential (GE) distribution, is used to approximate the marginal mean queue length and idle state probability class constraints for both the PR and HOLG/G/1 priority queues. Moreover, these results are used as building blocks in order to provide new approximate formulae for the mean and coefficient of variation of the effective priority service-time and suggest a maximum entropy algorithm for general open queueing networks with priorities in the context of the reduced occupancy approximation (ROA) method. Numerical examples illustrate the accuracy of the proposed maximum entropy approximations in relation to simulations involving different interarrival-time and service-time distributions per class. Comments on the extension of the work to more complex types of queueing systems are included.This work is sponsored in part by the Science and Engineering Research Council (SERC), UK, under grant GR/D/12422 and in part by the Ministry of Higher Education of the Algerian Government     

Approximate analysis of finite fork/join queueing networks

We perform an approximate analysis on the finite-buffered acyclic fork/join queueing networks under the “blocking before service” mechanism. This study, besides being able to handle a network with complex topology and with finite buffers, is more general than the existing ones of its kind in that two performance measures, the system throughput and the average number of customers in each buffer, are taken into account. For a simple two-sibling network, we propose in detail a decomposition algorithm in which each decomposed subsystem carries over the local fork/join/tandem structure. The extension of this algorithm to a more general system is also discussed. Experimental results are provided showing that the proposed algorithm yields accurate results on the two performance measures.     

Higher-order distributional properties in closed queueing networks

In many real-life computer and networking applications, the distributions of service times, or times between arrivals of requests, or both, can deviate significantly from the memoryless negative exponential distribution that underpins the product-form solution for queueing networks. Frequently, the coefficient of variation of the distributions encountered is well in excess of one, which would be its value for the exponential. For closed queueing networks with non-exponential servers there is no known general exact solution, and most, if not all, approximation methods attempt to account for the general service time distributions through their first two moments.We consider two simple closed queueing networks which we solve exactly using semi-numerical methods. These networks depart from the structure leading to a product-form solution only to the extent that the service time at a single node is non-exponential. We show that not only the coefficients of variation but also higher-order distributional properties can have an important effect on such customary steady-state performance measures as the mean number of customers at a resource or the resource utilization level in a closed network.Additionally, we examine the state that a request finds upon its arrival at a server, which is directly tied to the resulting quality of service. Although the well-known Arrival Theorem holds exactly only for product-form networks of queues, some approximation methods assume that it can be applied to a reasonable degree also in other closed queueing networks. We investigate the validity of this assumption in the two closed queueing models considered. Our results show that, even in the case when there is a single non-exponential server in the network, the state found upon arrival may be highly sensitive to higher-order properties of the service time distribution, beyond its mean and coefficient of variation.This dependence of mean numbers of customers at a server on higher-order distributional properties is in stark contrast with the situation in the familiar open M/G/1 queue. Thus, our results put into question virtually all traditional approximate solutions, which concentrate on the first two moments of service time distributions.     

On the number of customers served in the M/G/1 retrial queue: first moments and maximum entropy approach

In this paper we present general results on the number of customers, I, served during the busy period in an M/G/1 retrial system. Its analysis in terms of Laplace transforms has been previously discussed in the literature. However, this solution presents important limitations in practice; in particular, the moments of I cannot be obtained by direct differentiation. We propose a direct method of computation for the second moment of I and also for the probability of k,k⩽4, customers being served in a busy period. Then, the maximum entropy principle approach is used to estimate the true distribution of I according to the available information.Scope and purposeWe consider an M/G/1 queue with retrials. Retrial queueing systems are characterized by the fact that, an arriving customer who finds the server busy is obliged to leave the service area and return later to repeat his request after some random time. We deal with I, the number of customers served during the busy period of a retrial queue, and obtain closed expressions for its main characteristics, which will be employed in order to estimate the true distribution of this random variable.     

An improved truncation technique to analyze a Geo/PH/1 retrial queue with impatient customers

This paper considers a discrete-time retrial queue with impatient customers. We establish the global balance equations of the Markov chain describing the system evolution and prove that this queueing system is stable as long as the customers are strict impatient and the mean retrial time is finite. Direct truncation with matrix decomposition is used to approximate the steady-state distribution of the system state and hence derive a set of performance measures. The proposed matrix decomposition scheme is presented in a general form which is applicable to any finite Markov chain of the GI/M/1-type. It represents a generalization of the Gaver–Jacobs–Latouche's algorithm that deals with QBD process. Different sets of numerical results are presented to test the efficiency of this technique compared to the generalized truncation one. Moreover, an emphasis is put on the effect of impatience on the main performance measures.     

Service and capacity allocation in M/G/c/c state-dependent queueing networks

The problem of service and capacity allocation in state-dependent M/G/c/c queueing networks is analyzed and algorithms are developed to compute the optimal allocation c. The model is applied to the modeling of pedestrian circulation systems and basic series, merge, and split topologies are examined. Also of interest are applications to problems of evacuation planning in buildings. Computational experiments assert the algorithm's speed, robustness, and effectiveness. The results obtained indicate that the pattern of the optimal capacity surprisingly repeats over different topologies and it is also heavily dependent upon the arrival rate. Additional computational simulation results are provided to show the accuracy of the approach in all configurations tested.     

Maximum entropy analysis of the M[x]/M/1 queueing system with multiple vacations and server breakdowns

We consider a single unreliable sever in an M^[x]/M/1 queueing system with multiple vacations. As soon as the system becomes empty, the server leaves the system for a vacation of exponential length. When he returns from the vacation, if there are customers waiting in the queue, he begins to serve the customers; otherwise, another vacation is taken. Breakdown times and repair times of the server are assumed to obey a negative exponential distribution. Arrival rate varies according to the server’s status: vacation, busy, or breakdown. Using the maximum entropy principle, we develop the approximate formulae for the probability distributions of the number of customers in the system which is used to obtain various system performance measures. We perform a comparative analysis between the exact results and the maximum entropy results. We demonstrate, through the maximum entropy results, that the maximum entropy principle approach is accurate enough for practical purposes.     

Single server stochastic recirculation systems

A recursive methodology is suggested for approximating the asymptotic performance of a single server recirculation system. A single server recirculation system is modeled by a G/G/1/0 queueing loss system with generally distributed interarrivai times, generally distributed service times, a single server, no waiting room, first come first served queueing discipline, and retrials. In this queueing system, the units which initially are not processed by the server are not lost. These units retry to receive service by retrying. Furthermore, numerical results are provided and the approximation outcomes are compared against those from a simulation study.     

A new approach to the analysis of discrete event dynamic systems

We present a new, time domain approach to the study of discrete event dynamical systems (DEDS), typified by queueing networks and production systems. A general state-space representation is developed and perturbation analysis is carried out. Observation of a single sample realization of such a system can be used to predict behavior over other sample realizations, when some parameter is perturbed, without having to make additional observations. Conditions under which this is always possible are investigated and explicit results for some special cases are included.     

Non-asymptotic end-to-end performance bounds for networks with long range dependent fBm cross traffic

Fractional Brownian motion (fBm) emerged as a useful model for self-similar and long-range dependent aggregate Internet traffic. Asymptotic, respectively, approximate performance measures are known for single queueing systems with fBm through traffic. In this paper end-to-end performance bounds for a through flow in a network of tandem queues under open-loop fBm cross traffic are derived. To this end, a rigorous sample path envelope for fBm is proven that complements previous approximate results. The sample path envelope and the concept of leftover service curves are employed to model the remaining service after scheduling fBm cross traffic at a queuing system. Using composition results for tandem systems from the stochastic network calculus end-to-end statistical performance bounds for individual flows in networks under fBm cross traffic are derived. The discovery is that these bounds grow in O(n(logn)^1/(2-2H)) for n systems in series where H is the Hurst parameter of the cross traffic. Explicit results on the impact of the variability and the burstiness of through and cross traffic on network performance are shown. Our analysis has direct implications on fundamental questions in network planning and service management.     

A two-stage approach in solving the state probabilities of the multi-queue M/G/1 model

The M/G/1 model is the fundamental basis of the queueing system in many network systems. Usually, the study of the M/G/1 is limited by the assumption of single queue and infinite capacity. In practice, however, these postulations may not be valid, particularly when dealing with many real-world problems. In this paper, a two-stage state-space approach is devoted to solving the state probabilities for the multi-queue finite-capacity M/G/1 model, i.e. q-M/G/1/K_i with K_i buffers in the ith queue. The state probabilities at departure instants are determined by solving a set of state transition equations. Afterward, an embedded Markov chain analysis is applied to derive the state probabilities with another set of state balance equations at arbitrary time instants. The closed forms of the state probabilities are also presented with theorems for reference. Applications of Little's theorem further present the corresponding results for queue lengths and average waiting times. Simulation experiments have demonstrated the correctness of the proposed approaches.     

Maximum entropy and the G/G/1/N queue

Summary A new hybrid analytic framework, based on the principle of maximum entropy, is used to derive a closed form expression for the queue length distribution of a G/G/1 finite capacity queue. It is shown that Birth-Death homogeneous recursions for a single resource queue are special cases of maximum entropy one-step transitions which can be applied either in an operational or stochastic context. Furthermore, an equivalence relationship is used to analyse two-stage cyclic queueing networks with general service times, and favourable comparisons are made with global balance and approximate results. Numerical examples provide useful information on how critically system behaviour is affected by the distributional form of interarrival and service patterns. Comments on the implication of the work to the performance analysis and aggregation of computer systems are included.Some of the material included in this paper has been presented to the Performance '86 and ACM Sigmetrics 1986 Joint Conference on Computer Modelling, Measurement and Evaluation, May 28–30, 1986, University of North Carolina, USA     

Queueing-model based analysis of assembly lines with finite buffers and general service times

In this paper, we study the production process on multi-stage assembly lines. These production systems comprise simple processing as well as assembly stations. At the latter, workpieces from two or more input stations have to be merged to form a new one for further processing. As the flow of material is asynchronous with stochastic processing times at each station, queueing effects arise as long as buffers provide waiting room. We consider finite buffer capacities and generally distributed processing times. Processing is a service operation to customer items in the sense of a queueing system. The arrival stream of customer items is generated by processing parts at a predecessor station. This paper describes an approximation procedure for determining the throughput of such an assembly line. Exact solutions are not available in this case. For performance evaluation, a decomposition approach is used. The two-station subsystems are analyzed by G/G/1/N$G/G/1/N$ stopped-arrival queueing models. In this heuristic approach, the virtual arrival and service rates, and the squared coefficients of variation of these subsystems are determined. A system of decomposition equations which are solved iteratively is presented. Any solution to this system of equations indicates estimated values for the subsystems’ unknown parameters. The quality of the presented approximation procedure is tested against the results of various simulation experiments.