Queuing Approach Research Articles

A GI (A, B/(A,Q)) /D/ 1/Qmax Queue Approach for the Estimation of the Proportion of a Deteriorated Length of a Road Network

A GI (A, B/(A,Q)) /D/ 1/Qmax Queue Approach for the Estimation of the Proportion of a Deteriorated Length of a Road Network

MODELLING THE PERFORMANCE OF CLASS-BASED WEIGHTED FAIR QUEUE USING OPNET

Congestion control and internet network resources management are complex and critical issues in a high-speed packet switch data network, due to the emergence growth of modern multimedia streaming services. The increasing number of computer users in various organizations and institutions of higher learning have spurred a great deal of research on network traffic control. Network administrators are facing the challenges of providing efficient services that can satisfy user requirements. This research study proposed queuing algorithm based on classbased weighted fair queue scheme to complement congestion. Network simulation environment are designed and modeled using OPNET simulation software in-order to overcome the limitation of the traditional queuing approach. Various simulations scenarios are conducted. Analysis comparison with first-in-first-out and priority queue is recorded. And also, various network traffics such as: HTTP, video conferencing and voice applications among others are considered. From the graphical results obtained clearly shows that the overall applications services performances optimize significantly. In terms of the throughput, packet loss and queuing delay, the algorithms performs excellently compared with the FIFO and priority queue. This paper examines the implication of queuing scheduling algorithms on an IP router. It also outlines the effectiveness of the proposed algorithm in managing network resources during the period of congestion.

QoS Evaluation of End-to-End Services in Virtualized Computing Environments

Quality of service (QoS) optimization for end-to-end (e2e) services always depends on performance analysis in cloud-based service delivery industry. However, performance analysis of e2e services becomes difficult as the scale and complexity of virtualized computing environments increase. In this paper, the authors present a novel hierarchical stochastic approach to evaluate the QoS of e2e virtualized cloud services using Quasi-Birth Death structures, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources, i.e., VM-configuration. To reduce the complexity of performance evaluation, the overall virtualized cloud services are partitioned into three sub-hierarchies. The authors analyze each individual sub-hierarchy using stochastic queueing approach. Thus, the key performance metrics of e2e cloud service QoS, such as acceptance probability and e2e response delay incurred on user requests, are obtained.

Impact of Queuing on Call Completion Rate in GSM Networks

Effective utilization of network resource reduces the probability that a call arriving at the base station (BS) of a network will be lost. Performance evaluation plays an important role in modelling and designing of effective schemes to utilize limited network resource. This objective is achieved by an accurate traffic characterization and a precise analysis of the performance metrics in terms of traffic intensity. This has prompted the deployment of various concepts and techniques aimed at delivering solutions on the issue of optimization of GSM networks. In this paper, we use the queuing approach to develop a model for call completion, making signal power considerations as well. A General User Interface (GUI) is designed for the developed model using MATLAB and the impact of queuing on call completion is analysed by carrying out an assessment of the performance of the model at different parametric values. http://dx.doi.org/10.4314/njt.v34i1.25

Staffing a service system with appointment-based customer arrivals

Appointment systems are widely used to facilitate customers’ access to services in many industries such as healthcare. A number of studies have taken a queueing approach to analyse service systems and facilitate managerial decisions on staffing requirements by assuming independent and stationary customer arrivals. This paper is motivated by the observation that the queueing-based method shows relatively poor performance when customers arrive according to their appointment times. Because customer arrivals are dependent on their appointment times, this study, unlike queueing-based methods, conducts a detailed analysis of appointment-based customer arrivals instead of making steady-state assumptions. We develop a new model that captures the characteristics of appointment-based customer arrivals and computes the probability of transient system states. Through the use of this model, which relaxes stationary and independent assumptions, we propose a heuristic algorithm that determines staffing requirements with aims to minimizing staff-hours while satisfying a target service level. The simulation results show that the proposed method outperforms the queueing-based method.

A queueing approach to multi-site enzyme kinetics.

Multi-site enzymes, defined as where multiple substrate molecules can bind simultaneously to the same enzyme molecule, play a key role in a number of biological networks, with the Escherichia coli protease ClpXP a well-studied example. These enzymes can form a low latency 'waiting line' of substrate to the enzyme's catalytic core, such that the enzyme molecule can continue to collect substrate even when the catalytic core is occupied. To understand multi-site enzyme kinetics, we study a discrete stochastic model that includes a single catalytic core fed by a fixed number of substrate binding sites. A natural queueing systems analogy is found to provide substantial insight into the dynamics of the model. From this, we derive exact results for the probability distribution of the enzyme configuration and for the distribution of substrate departure times in the case of identical but distinguishable classes of substrate molecules. Comments are also provided for the case when different classes of substrate molecules are not processed identically.

A queuing approach for a tri-objective manufacturing problem with defects: a tuned Pareto-based genetic algorithm

In this research, a manufacturing facility with independent workstations to remanufacture nonconforming products is investigated. Each workstation is first modeled as an M/M/m queuing system with m being a decision variable. Then, a tri-objective integer nonlinear programming models is developed to formulate the problem. The first objective tries to minimize the waiting times of products, while the second one tries to maximize the minimum reliability of machines at the workstations. Since minimization of the waiting times results in using a large number of machines with higher idle times, the third objective is considered to minimize the mean idle time of the machines. The aim is to determine optimal number of machines at each workstation. Since the problem belongs to the class of NP-hard problems, the non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to find Pareto fronts. Because there is no benchmark available in the literature to validate the results obtained, the non-dominated ranked genetic algorithm (NRGA) is used as well. In both algorithms, not only the best operators are selected but also all of their important parameters are calibrated using statistical analysis. The performances of the algorithms are statistically compared using the t test. Besides, the multiple attribute decision-making method of TOPSIS is used to determine the better algorithm. The applicability of the proposed model and the solution algorithms is demonstrated via some illustrative examples.

On Delay-optimal Issues Based on Queuing Approaches for Real-time Blending Services in Heterogeneous Access Networks

On Delay-optimal Issues Based on Queuing Approaches for Real-time Blending Services in Heterogeneous Access Networks

用于可信任中继量子密钥分配网络的差异化服务提供机制

In order to provide different quantum key service,trusted relay quantum key distribution networks with quantum key pool was studied.Firstly,relationship between packet delay and quantum key pool was analyzed.The formula was given,which showed that the mean packet delay is related to the quantum key generation rate,the initial length of quantum key,the mean length,arrival time and arrival rate of packets.The theory and simulation results verified the accuracy of the delay analysis.Next,the key providing service was classified into three types:guaranteed,prioritized and best-effort key service.Correspondingly,pre-reserving,hop-by-hop jumping-queue and hop-by-hop queuing approaches were presented to realize the above differentialized key services.Finally,simulation results verified the efficiency of the differentialized service providing scheme.

A new probabilistic coverage model for ambulances deployment with hypercube queuing approach

Aiming at improving the efficiency and reliability of ambulance service, several location models for ambulance stations have been proposed in the facility location literature. Two well-known approaches to this problem are coverage and median models. Coverage model looks for the location to maximize the (deterministic or probabilistic) covered demand of ambulance calls. Hence, this model can be thought of reliability-oriented model. In median model, the objective is to minimize the total traveling distance of the ambulances from the station to the scene of call and thus is the cost-oriented model. In this paper, a new probabilistic coverage model is introduced. It uses the idea of MEXCLP and mixes it with the hypercube queuing model. The model is applied in a real case for one of Tehran city's zones in Iran. The results were satisfactory and show the applicability of the model. Sensitivity analysis was conducted to determine the overall behavior of system responsiveness against parameters of the model. Discussions on sensitivity analysis are presented.

A Queueing-Game Model for Making Decisions About Order Penetration Point in Supply Chain in Competitive Environment

This study is dedicated to Order Penetration Point (OPP) strategic decision making which is the boundary between Make-To-Order (MTO) and Make-To-Stock (MTS) policies. This paper considers two competing supply chains in which a manufacturer produces semi-finished items on a MTS basis for a retailer that will customize the items on a MTO basis. The two-echelon supply chain offers multi-product to a market comprised of homogenous customers who have different preferences and willingness to pay. The retailer wishes to determine the optimal OPP, the optimal semi-finished goods buffer size, and the price of the products. Moreover, the authors consider both integrated scenario (shared capacity model) and competition scenario (Stackelberg queueing-game model) in this paper. A matrix geometric method is utilized to evaluate various performance measures for this system and then, optimal solutions are obtained by enumeration techniques. The suggested queueing approach is based on a new perspective between the operation and marketing functions which captures the interactions between several factors including inventory level, price, OPP, and delivery lead time. Finally, parameter sensitivity analyses are carried out and the effect of demand on the profit function, the effect of prices ratio on completion rates ratio and buffer sizes ratio and the variations of profit function for different prices, completion percents, and buffer sizes are examined in both scenarios.

An integrated operations-marketing perspective for making decisions about order penetration point in multi-product supply chain: a queuing approach

This study is dedicated to strategic decision-making regarding order penetration point (OPP), which is the boundary between make-to-order (MTO) and make-to-stock (MTS) policies. This paper considers a supply chain in which a manufacturer produces semi-finished items on an MTS basis for a retailer that will customise the items based on MTO policy. This two-echelon supply chain offers different products to a market comprised of homogenous customers who have different preferences and willingness to pay. The retailer wishes to determine the optimal OPP, the optimal semi-finished goods buffer size, and the price of the products with assumption of price sensitive demand function. Moreover, we consider both shared and unshared capacity models in this paper. A matrix geometric method is utilised to evaluate various performance measures for this system, and then, optimal solutions are obtained by enumeration techniques. The suggested queuing approach is based on a new perspective between the operations and marketing functions which captures the interactions between several factors including inventory level, product pricing, OPP, and delivery lead time. Finally, parameter sensitivity analyses are carried out and the effect of demand on the profit function, the effect of prices ratio on completion rates ratio and buffer sizes ratio and the variations of profit function for different prices, completion percents, and buffer sizes are examined.

Lot size optimization in manufacturing systems: The surrogate method

In this paper we extend a stochastic discrete optimization approach so as to tackle the lot-sizing problem in manufacturing systems. In practice, with the surrogate methodology, the lot sizes are continuously adjusted on-line by the gradient-based approach. The lot sizing determines the number of parts batched together for production. We utilize the queueing approach that evidences the existence of a convex relationship between batch size and waiting time (including processing). Large lot sizes will cause long lead times (the batching effect), as the lot size gets smaller the lead time will decrease but once a minimal lot size is reached a further reduction of the lot size will cause high traffic intensities resulting in longer lead times (the saturation effect). The congestion phenomenon is due to the increased number of setups (and thus total setup time). In this paper, we consider the Surrogate method and the Stochastic comparison algorithm. According to our findings, the Surrogate method finds the optimal solution of the original discrete problem and exhibits a very fast convergence. Some numerical results are reported.

A soft computing-based approach to optimise queuing-inventory control problem

In this paper, a multi-product continuous review inventory control problem within batch arrival queuing approach (MQr/M/1) is developed to find the optimal quantities of maximum inventory. The objective function is to minimise summation of ordering, holding and shortage costs under warehouse space, service level and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Non-deterministic Polynomial-time hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, both ganetic algorithm and simulated annealing algorithm are utilised. In order to determine the best value of algorithm parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analysed using some numerical illustrations.

A Dynamical Queue Approach to Intelligent Task Management for Human Operators

Formal methods for task management for human operators are gathering increasing attention to improve efficiency of human-in-the-loop systems. In this paper, we consider a novel dynamical queue approach to intelligent task management for human operators. We consider a model of a dynamical queue, where the service time depends on the server utilization history. The proposed queueing model is motivated by, but not restricted to, widely accepted empirical laws describing human performance as a function of mental arousal. The focus of the paper is to characterize the throughput of the dynamical queue and design corresponding maximally stabilizing task release control policies, assuming deterministic arrivals. We focus extensively on threshold policies that release a task to the server only when the server state is less than a certain threshold. When every task brings in the same deterministic amount of work, we give an exact characterization of the throughput and show that an appropriate threshold policy is maximally stabilizing. The technical approach exploits the optimality of the one-task equilibria class associated with the server dynamics. When the amount of work associated with the tasks is an independent identically distributed (i.i.d.) random variable with finite support, we show that the maximum throughput increases in comparison to the case where the tasks have the same deterministic amount of work. Finally, we provide preliminary empirical evidence in support of the applicability of the proposed approach to systems with human operators.

A queuing approach for making decisions about order penetration point in multiechelon supply chains

This study is dedicated to order penetration point (OPP) strategic decision making which is the boundary between make-to-order (MTO) and make-to-stock (MTS) policies. A multiproduct multiechelon production supply chain is considered where the first production stage manufactures semifinished products based on an MTS policy to supply the second production stage which operates on the MTO policy. The producer desires to find the optimal fraction of processing time fulfilled by supplier and optimal semifinished products buffer capacity in OPP. To calculate system performance indexes, the matrix geometric method is employed. Afterward, optimal solutions are obtained by enumeration and direct search techniques. Moreover, the system behavior is analyzed by the numerical example. It is shown that system total cost is a concave function of increasing completed percentage in first production stage. According to the total cost function elements, managers desire to locate OPP where to balance the order fulfillment delay cost, holding cost and the cost of disposing unsuitable items. Finally, the impact of different amounts of storage capacity on OPP and total cost are analyzed. Also, the manner of expected numbers of unsuitable products, semifinished products, and expected order completion delay are analyzed versus various quantities of storage capacity and production rate.

Multi Queue Approach for Network Services Implemented for Multi Core CPUs

Multiple core processors have already became the dominant design for general purpose CPUs. Incarnations of this technology are present in solutions dedicated to such areas like computer graphics, signal processing and also computer networking. Since the key functionality of network core components is fast package servicing, multicore technology, due to multi tasking ability, seems useful to support packet processing. Dedicated network processors characterize very good performance but at the same time high cost. General purpose CPUs achieve incredible performance, thanks to task distribution along several available cores and relatively low cost. The idea, analyzed in this paper, is to use general purpose CPU to provide network core functionality. For this purpose parameterized system model has been created, which represents general core networking needs. This model analyze system parameters influence on system performance.

Asynchronous Control with ATR for Large Robot Teams

In this paper, we discuss and investigate the advantages of an asynchronous display, called “image queue”, tested for an urban search and rescue foraging task. The image queue approach mines video data to present the operator with a relevant and comprehensive view of the environment by selecting a small number of images that together cover large portions of the area searched. This asynchronous approach allows operators to search through a large amount of data gathered by autonomous robot teams, and allows comprehensive and scalable displays to obtain a network-centric perspective for unmanned ground vehicles (UGVs). In the reported experiment automatic target recognition (ATR) was used to augment utilities based on visual coverage in selecting imagery for presentation to the operator. In the cued condition a box was drawn in the region in which a possible target was detected. In the no-cue condition no box was drawn although the target detection probability continued to play a role in the selection of imagery. We found that operators using the image queue displays missed fewer victims and relied on teleoperation less often than those using streaming video. Image queue users in the no-cue condition did better in avoiding false alarms and reported lower workload than those in the cued condition.

Planning data transfers in grids: a multi‐service queueing approach

SUMMARYGrid applications move large amounts of data between distributed resources, and the efficiency of a Grid depends on their timely delivery within given bounds (deadlines). In most cases, the data volume and deadline are known in advance, allowing for both network planning and connection admission control (textrmCAC). We formally define the problem and, based on this formalization, describe the operation of a feasible procedure for network reservations of deadline‐constrained bulk data transfer requests. The procedure guarantees a minimum bandwidth to meet the deadlines and allows for opportunistic utilization of residual network capacity. We propose a novel analytical model based on the solution of an M/M(nc)/1/k(s)−RPS queue. The analytical model is validated against ns−2 simulations taking into account network level details (IP and TCP protocols), showing remarkably good coherence even under heavy loads. The model is orders of magnitude faster than simulation, which enables its application to plan the capacity of Grid networks, and to enforce CAC under the hypothesis of a dominating bottleneck on the transfer route. Copyright © 2011 John Wiley & Sons, Ltd.

Using Queuing Approach for Locating the Order Penetration Point in a Two-Echelon Supply Chain with Customer Loss

An order penetration point (OPP) is the boundary between make-to-order (MTO) policy and make-to-stock (MTS) policy. This study is dedicated to OPP strategic decision making which is concerned with the upstream and downstream decisions of OPP in supply chains. In this paper, we consider a two-echelon production supply chain in which in first production stage, supplier manufactures the semi-finished products on a MTS policy for a manufacturer in second production stage that will customize the products on a MTO policy. The manufacturer desires to find the optimal fraction of processing time fulfilled by its supplier and its optimal semi-finished products buffer storage capacity in OPP. Also, impatient customers are considered in the study. In order to calculate system performance indexes, we employ matrix geometric method and queuing steady state equations. Afterward, optimal solutions are obtained applying an optimization mathematical model with enumeration and direct search techniques.