Mean Delay In Queue Research Articles

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.

Performance Evaluation of Vertical Hard Handovers in Cellular Mobile Systems

With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.

Performance Evaluation of Vertical Hard Handovers in Cellular Mobile Systems

With the rapid increase of new and diverse cellular mobile services, the overlapping of cells has become typical in the majority of the coverage area of the network. Vertical handovers occur between two layers of cells when a user is switched from one layer to the other. In this paper we investigate the influence of network parameters on vertical hard handover performance in a cell environment. The work considers two layers of cells: a layer of macrocells and a layer of microcells. Handover requests enter the macrocell from neighbor macrocells and from microcells that belong to a different layer. Using Markov chain analysis and simulation we calculate network performance parameters such as mean queue delay, handover dropping probability and channel utilization. We also compare the handover performance for the macrocell and macrocell traffic separately. Our results show the influence of total channels, maximum queue size and handover request arrival rate on handover performance. They also show that when the traffic from each layer is treated with equal priority in the system, the performance of each layer is comparable.

Maximum Web Object Size Satisfying M/G/1 Queueing Delay Constraint in Multiple User Access Environment

This paper introduces the maximum web object size satisfying the constraint such that queueing delay in a steady state is less than the threshold users require as Quality of Service (QoS). By using the inference that mean waiting time for web server scheduling and mean queueing delay in the system become the same in the steady state, we develop the maximum web object size model. Since the packet-to-web-server arrival process exhibits an exponential distribution and web service time can be described by Hyper-exponential or Weibull distribution, the maximum object size model is formulated by using M/H2/1and M/G(Weibll)/1. Performance evaluation shows that the maximum object size for M/H2/1 is larger than that for M/G(Weibull)/1 on the average, however, it becomes nearly the same as the number of embedded objects in home page in M/H2/1 and the shape parameter in M/G(Weibull)/1 increase. These results can be applied to the determination of web object size satisfying the end-users mean queueing delay requirement.

Estimation of backlog and delay in OFDM/TDMA systems with traffic policing using Network Calculus

Achieving quality of service (QoS) in OFDM based networks depends, among other factors, on mechanisms for traffic policing or control. Aggregate traffic in high speed networks can present multifractal characteristics such as bursts over a wide range of time scales, heavy tailed inter-arrival time densities, self-similarity and long-range dependence. These properties are followed by a degradation of quality of service for the traffic flows. In this paper, we firstly propose a policing algorithm based on multifractal traffic modeling, evaluating its performance in comparison to other algorithms. We present a more precise in general envelope modeling process in order to efficiently police the input traffic to the system. Further, we present an approach based on Network Calculus for estimating quality of service parameters, such as mean queue length (backlog) and delay. We analyze these two parameters for an OFDM/TDMA based simplified WiMAX system with and without the application of traffic modeling based policing algorithms. In other words, we propose an approach for evaluating some QoS parameters of an OFDM/TDMA system where the traffic is policed. We also evaluate the performance of the proposed algorithm in policing the traffic in an OFDM/TDMA system.

On the Effect of a Random Access Protocol on the Performance of the Session-Based Data Subsystem in GSM/GPRS

Research into mixed services in mobile cellular networks is highly topical. Session-based applications (e.g., Web browsing) form significant and increasing data traffic in cellular networks. In such mixed services, random access (RA) protocols impose significant effects in performance metrics of the new data users. To date, analytical facility has been limited by the absence of closed-form models for the RA protocols that exist in time-division multiple access (TDMA) cellular networks with mixed voice (as circuit switched) and data with finite number of sessions (as packet switched). In this novel paper, we research an RA protocol based on S-Aloha with data as a session for mobile TDMA cellular networks and present an analytical framework. This work is germane to Global System for Mobile Communications/General Packet Radio Service systems. This paper begins with a general model description of a voice and data subsystem, and an explanation of its admission schemes. As well as the model, an analytical framework based on equilibrium point analysis is derived, and an algorithm is then formulated to complement the analytical framework. Using this novel analytical framework, together with the associated algorithm, performance metrics of the system such as throughput, mean access delay, and mean delay in the queue are obtained. Stability conditions are also highlighted. Numerical results are corroborated using simulation.

Modelo de simulación de la capa MAC IEEE 802.16-2004 para modo Mesh. IEEE 802.16-2004 MAC Layer Simulation Model in the Mesh Mode

IEEE 802.16-2004 standard supports mesh networks topology and it’s important to realize the performance of such systems. Simulation of communication systems allows its optimization in performance measures. In this article we present a model developed over Network Simulator 2 to simulate data link layer operation in mesh topologies based on that standard. We present the results based on a link scheduler and the model developed, as the mean queue length, mean queue delay and the system capacity for different topologies.

Bandwidth and Buffer Tradeoffs in Optical Packet Switching

Recognizing the difficulties in buffering or slowing data in all-optical networks, optical packet switching (OPS) may be viable if buffers are small, at the cost of some inefficiency in link utilization. In this paper, the authors consider a new single-node OPS model that focuses on a set of output wavelengths in a certain link and consider the set of input wavelengths that transmits packets competing for the set of output wavelengths. Using this model, an exact solution and an accurate and scalable approximation, based on reduction of the state-space to a single direction, are provided for packet-loss probability and mean queueing delay. Tradeoffs between optical buffering capacity and link utilization for cases with and without wavelength conversion are studied and discussed

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.

A queueing control model for retail services having back room operations and cross-trained workers

Many retail service facilities have both front room and back room operations. The front room deals with serving customers, perhaps from a queue. The back room focuses typically on restocking of shelves and sorting and/or processing of paperwork. We model such a facility having workers who are cross-trained to do both jobs. We assume that back room work is interruptible. A manager can bring a worker from the back room to the front room when the customer checkout queue becomes “too long”. A reverse assignment occurs when the number of customers is sufficiently small. We assume that the retail facility contains two customer-counting technologies. The first counts the number of shoppers in the store who are not already in checkout queues and the second counts the number of customers at checkout. The goal is to find the minimum number of workers to staff the facility subject to two performance constraints. The mean queue delay in the front room must be less than a pre-specified value, and the time-average number of workers in the back room must be greater than a pre-specified value. Once the minimum complement of workers is found, a secondary goal is to minimize mean queueing delay subject to retaining back room feasibility. The system is a continuous time Markov process having a three-dimensional state space. The (heuristic) optimization process includes state-dependent rules for switching workers between rooms. Scope and purpose Many customer service facilities have both front room and back room operations for workers. In the front room, workers serve customers who may be delayed in a queue. In the back room workers perform required but time-insensitive work such as restocking of shelves, sorting of materials and/or processing of paperwork. Allowing workers to be cross-trained to perform both functions creates a potentially high productivity system with worker utilization rates near 100%. We model such a facility having workers who are cross-trained using techniques of queueing theory and heuristic optimization. The assignment of workers over time to each of the two tasks is dependent on the number of customers in the front room. When the customer queue becomes “too long”, the manager can bring a worker from the back room to front room and vice versa when there are few customers in the front room. The goal is to find the minimum number of workers to staff the facility subject to performance constraints in both rooms: a mean queue delay constraint in the front room and an average number of workers constraint in the back room. Of particular interest is determining the rules for bringing in and sending back workers.

Approximating by enhanced interpolation in queueing analyses

It is often difficult to obtain waiting-time measures for non-Markovian, general multi-server queues. Thus there has long been interest in the development of effective approximations for assessing waiting-time behavior in complex waiting-line environments, especially those which can be formulated as variations of the classical queueing network paradigm. As a result, we have developed and extensively tested a procedure for mean delay-time approximation in general multi-server queues by combining the ideas of classical numerical interpolation with major concepts from the classical queueing literature. Queueing theorists have long made productive use of major concepts from applied mathematics. However, the familiar numerical technique of interpolation, with its great variety of possible interpolation functions, has seen incomplete use in the modeling of delay phenomena. In this work, we show that interpolation has very broad application in the process of deriving approximate results. We study the properties of interpolation approximations for the G/G/ c queue and develop a desired analytic framework for such approximations. This structure is then used to derive a simple approximation for the mean queue delay for G/G/ c. The approximation contains three key elements: a light-traffic parameter, a heavy-traffic parameter, and a drift parameter that specifically allows the introduction of external data into the approximation.

A sample path analysis of the delay in the M/G/C system

Using sample path analysis we show that under the same load the mean delay in queue in the M/G/2 system is smaller than that in the corresponding M/G/1 system, when the service time has either the DMRL or NBU property and the service discipline is FCFS. The proof technique uses a new device that equalizes the work in a two server system with that in a single sterver system. Other interesting quantities such as the average difference in work between the two servers in the GI/G/2 system and an exact alternate derivation of the mean delay in the M/M/2 system from sample path analysis are presented. For the same load, we also show that the mean delay in the M/G/C system with general service time distribution is smaller than that in the M/G/1 system when the traffic intensity is less than 1/c.

A sample path analysis of the delay in the M/G/C system

Using sample path analysis we show that under the same load the mean delay in queue in the M/G/2 system is smaller than that in the corresponding M/G/1 system, when the service time has either the DMRL or NBU property and the service discipline is FCFS. The proof technique uses a new device that equalizes the work in a two server system with that in a single sterver system. Other interesting quantities such as the average difference in work between the two servers in the GI/G/2 system and an exact alternate derivation of the mean delay in the M/M/2 system from sample path analysis are presented. For the same load, we also show that the mean delay in the M/G/C system with general service time distribution is smaller than that in the M/G/1 system when the traffic intensity is less than 1/c.

An Asymptotic Analysis of a Queueing System with Markov-Modulated Arrivals

We study the mean delay and the mean number in queue in a single-server system whose arrivals are given by a nonhomogeneous Poisson process with rate equal to a function of the state of an independent Markov process. Models of this sort arise naturally in the study of packet arrivals to a local switch. There are, in general, no closed form expressions for these quantities. We consider the normalized delay and the normalized number in queue with respect to the equivalent quantity for an M/M/1 queue with the same arrival and service rates. We show that in light and heavy traffic these quotients converge to finite, nonzero quantities that can be calculated in terms of the original model parameters. When the arrival stream is the superposition of arrival processes, the overall limit is related to the individual limits for each of the separate arrival processes. These results lead to approximations for the mean delay and number in queue for intermediate traffic. We present several examples showing the accuracy of this approximation.

Conservation Equations and Variance Reduction in Queueing Simulations

We consider the efficient estimation of mean delay in queue, d, mean wait in system, w, time average number in queue, Q, time average number in system, L, and time average amount of work in system, V, for simulated queueing systems. We prove for the regenerative GI/G/s queue that it is more efficient to estimate w, Q, L, and V from an estimate of d than it is to estimate them directly. This generalizes previous results for the M/G/1 queue and also confirms empirical studies on other GI/G/s queues.

Efficient Estimators for Simulated Queueing Systems

In this paper we consider efficient estimators for simulated queueing systems. It is shown to be more efficient to estimate all of the usual first moment measures of performance from an estimate of mean delay in queue than to estimate them directly. For single-server queues an estimator is introduced which is more efficient than the normal estimator of mean delay in queue.