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A paper published in 1983 in this journal by A. de Palma et at. uses a deterministic queueing supply model in combination with a random utility demand model to predict the pattern of traffic flows during a peak period. Two different versions of the supply model are included, a "basic model" which is identical to the supply model used by a number of other authors investigating the same general problem (e.g., M. J. Smith, 1984), and a more complicated "extended model." As detailed below, there is a mathematical discrepancy in the analysis. Because of this discrepancy, some of the conclusions about the traffic flow pattern in the case of the extended model are incorrect and all are unproven. For the simpler-and seemingly more realistic-basic model the discrepancy vanishes and the conclusions remain valid. [ABSTRACT FROM AUTHOR]

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions. [ABSTRACT FROM AUTHOR]

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper's model can save the distribution cost of electric vehicles. [ABSTRACT FROM AUTHOR]

The rate at which powerline communication (PLC) impulsive noise arrives and lasts in the channel determines the severity of signal degradation, with impulsive noise bursts capable of causing complete signal loss. Consequently, the PLC impulsive noise requires an appropriate description to enhance the reliability and effective utilisation of the PLC channel. This paper employs the queuing theory approach to analyse and model the PLC impulsive noise inter-arrival and service time distribution, where the impulsive noise is categorised into single-impulse noise events and burst-impulse noise events. The Erlang-k distribution is proposed for modelling both the inter-arrival and service time distributions for the PLC impulsive noise with the process viewed as an infinite queue with a single server. The impulse noise events are assumed to traverse k stages before entering the PLC network and also pass through k stages before leaving the PLC network, with each of the stages following an exponential distribution. The proposed models are then validated through measurements from different indoor environments and compared to the exponential distribution model, commonly employed in modelling inter-arrival and duration of PLC impulsive noise. The Ek/Ek/1 queue model is determined to adequately model the burst-impulse noise events. In regards to the single-impulse noise events, the exponential distribution is observed to provide a suitable fit for the inter-arrival time distribution. The occurrence of PLC impulsive noise events is also found to achieve a state of equilibrium for all the measurement data under study. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system's average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles' data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system's AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system. [ABSTRACT FROM AUTHOR]

Ophthalmic units use sophisticated equipment to enable accurate diagnosis of refractive errors. This equipment is subject to two types of breakdowns. One is simple breakdown which can be repaired in-house, and the other is complex breakdown which requires repair by the original equipment manufacturer (OEM) and results in a delay time between the server breakdown and the start of the repair. In this paper, we model this scenario as an M n /M/1 queuing system with two types of breakdowns and delayed repairs due to complex breakdowns, where the delay time and repair times for simple and complex breakdowns are generally distributed. We obtain the steady-state probabilities and provide the recursive formulas for the Laplace–Stieltjes transforms (LSTs) of conditional residual delay time and repair times given the system state. For the fully observable case, we derive the equilibrium joining strategies of customers who decide to join or balk based on their observation of the system state. Moreover, two numerical experiments are conducted to explore the equilibrium joining probabilities. [ABSTRACT FROM AUTHOR]

Operations management (OM) in the public policy context is extremely complex with many mutually interacting factors characterized by feedback loops, delays and nonlinearities, as well as multiple stakeholders pursuing divergent objectives. Prior researchers have called for a systems approach in these contexts, arguing that standard OM methodologies such as mathematical programming, and queuing theory often cannot fully address these problems. Researchers have employed one such systems approach, system dynamics, successfully for decades for studying OM problems in public policy because it can address such complexity and can also integrate disciplines from outside OM such as political science, epidemiology, ecology, etc. In this paper, we create a roadmap for researchers—both those who are familiar with systems dynamics and those who are not—for the expanded use of system dynamics studying public policy‐related OM problems. We review and organize relevant system dynamics literature in both traditional operations management venues as well as public policy venues unfamiliar to OM audiences. We then identify a set of interesting open questions and potential system dynamics building blocks for answering them by topic. Leveraging this review, we describe under what conditions system dynamics is most appropriate. We then identify several overarching methodological and domain gaps for future research. Finally, we build on previous work to extend a process for using system dynamics with traditional operations management methodologies. It separates model building into two sequential phases: consensus‐building models and detailed operational models. It also incorporates scenario planning and feedback from implementation outcomes. [ABSTRACT FROM AUTHOR]

It is well established that a diverse range of entropic measures, while remarkably adaptable, must inevitably be complemented by innovative approaches to enhance their effectiveness across various domains. These measures play a crucial role in fields like communication and coding theory, driving researchers to develop numerous new information measures that can be applied in a wide array of disciplines. This paper introduces a pioneering isolated entropic measure and its solicitations to queueing theory the study of dissimilarities of uncertainty. By creating the newly developed discrete entropy, we have articulated an optimization principle where the space capacity is predetermined and solitary evidence accessible is around the mean size. Additionally, we have conveyed the solicitations of "maximum entropy principle" to maximize the entropy probability distributions. [ABSTRACT FROM AUTHOR]

The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel deep learning-based EV charging load prediction framework to assess the impact of EVs on the MES. First, to model the EV traffic flow, a modified weight fusion spatial–temporal graph convolutional network (WSTGCN) is proposed to capture the inherent spatial–temporal characteristics of traffic flow. Specifically, to enhance the WSTGCN performance, the modified residual modules and weight fusion mechanism are integrated into the WSTGCN. Then, based on the predicted traffic flow, an improved queuing theory model is introduced to predict the charging load. In this improved queuing theory model, special consideration is given to subjective EV user behaviors, such as refusing to join queues and leaving impatiently, making the queuing model more realistic. Additionally, it should be noted that the proposed charging load predicting method relies on traffic flow data rather than historical charging data, which successfully addresses the data insufficiency problem of newly established charging stations, thereby offering significant practical value. Experimental results demonstrate that the proposed WSTGCN model exhibits superior accuracy in predicting traffic flow compared to other benchmark models, and the improved queuing theory model further enhances the accuracy of the results. [ABSTRACT FROM AUTHOR]

We revisit the problem of the computation of the limiting characteristics of (in)homogeneous continuous-time Markov chains with the finite state space. In general, it can be performed only numerically. The common rule of thumb is to interrupt calculations after quite some time, hoping that the values at some distant time interval will represent the sought-after solution. Convergence or ergodicity bounds, when available, can be used to answer such questions more accurately; i.e., they can indicate how to choose the position and the length of that distant time interval. The logarithmic norm method is a general technique that may allow one to obtain such bounds. Although it can handle continuous-time Markov chains with both finite and countable state spaces, its downside is the need to guess the proper similarity transformations, which may not exist. In this paper, we introduce a new technique, which broadens the scope of the logarithmic norm method. This is achieved by firstly splitting the generator of a Markov chain and then merging the convergence bounds of each block into a single bound. The proof of concept is illustrated by simple examples of the queueing theory. [ABSTRACT FROM AUTHOR]

This paper studies a continuous-review stochastic replenishment model for a multi-component system with regular and emergency orders. The system consists of N parallel and independent components, each of which has a finite life span. In addition, there is a warehouse with a limited stock of new components. Each broken component is replaced by a new component from the stock. When no component is available, an emergency supply is ordered. The stock is managed according to an ((s , S) , (0 , Q e)) policy, which is a combination of an (s , S) policy for the regular order and a (0 , Q e) policy for the emergency order. The regular order is delivered after an exponentially distributed lead time, whereas the emergency order is delivered immediately. We study three sub-policies for emergency orders, which differ from each other in size and in relation to the regular order. Applying the results from queueing theory and phase-type properties, we derive the optimal thresholds for each sub-policy and then compare the economic benefit of each one. [ABSTRACT FROM AUTHOR]

The Hawkes process, which is generally defined for the continuous-time setting, can be described as a self-exciting simple point process with a clustering effect, whose jump rate depends on its entire history. Due to past events determining future developments of self-exciting point processes, the Hawkes model is generally not Markovian. In certain special circumstances, it can be Markovian with a generator of the model if the exciting function is an exponential function or the sum of exponential functions. In the case of non-Markovian processes, difficulties arise when the exciting function is not an exponential function or a sum of exponential functions. The intensity of the Hawkes process is given by the sum of a baseline intensity and other terms that depend on the entire history of the point process, as compared to a standard Poisson process. It is one of the main methods used for studying the dynamical properties of general point processes, and is highly important for credit risk studies. The baseline intensity, which is instrumental in the Hawkes model, is usually defined for deterministic cases. In this paper, we consider a linear Hawkes model where the baseline intensity is randomly defined, and investigate the asymptotic results of the large deviations principle for the newly defined model. The Hawkes processes with randomized baseline intensity, dealt with in this paper, have wide applications in insurance, finance, queue theory, and statistics. [ABSTRACT FROM AUTHOR]

Low earth orbit (LEO) satellite communication networks require huge load capacity and information processing speed to carry global communication traffic. Inter-satellite links and the on-board processing are the key technologies to achieve this goal, but the new network architecture leads to great challenges on satellite routing. This paper designs a hybrid inter-satellite link with the same-orbit laser and the different-orbit microwave to increase the link capacity and adopts a CPU centralized scheduling to improve the utilization of computing resources. Then, this paper establishes minimum delay function by considering the inter-satellite transmission delay and the on-board processing delay. The transmission delay model bases on the orbital period, and the processing delay adopts the multi-services model, the limited-capacity single-service model, and the unlimited-capacity single-service model in the queuing theory to model the on-board CPU centralized scheduling, photoelectric converters, and electro-optical converters, respectively. Based on this model, this paper proposes an inter-satellite routing strategy with modified Q-routing algorithm. The modified algorithm uses Dijkstra algorithm to accelerate the convergence of Q-routing algorithm and retains the strong real-time performance of Q-routing algorithm. Simulations show that the delay of the modified algorithm is 83.3 % lower than that of the Dijkstra algorithm, and the larger the network and the traffic, the more obvious the advantage. [ABSTRACT FROM AUTHOR]

The manufacturing industry, with globalization and information technology, is facing cost pressure, low profits, technology difficulties and many other challenges. Enterprises need to strengthen cost management, improve production efficiency, and meet customer demand to cope with the increasingly complex competitive market. Here, our research in this area becomes important. This paper presents a new approach for building a smart decision-making process specifically in manufacturing organizations in terms of profitability. It emphasizes the importance of profitability-based decision making. The basic steps and modules required to build a profitability-based decision process are described. The advantages of this decision-making system are that it can help companies maximize profits, increase productivity, and meet customer demand for order adjustments. [ABSTRACT FROM AUTHOR]

Some discrepancies in an earlier paper describing a bulk service queuing situation with customer balking or reneging are pointed out. Necessary modifications to make the model more valid are incorporated in a reformulation and an approach for developing the state-time probabilities of the system is suggested. [ABSTRACT FROM AUTHOR]

The paper presents a proposed queuing model based on Kendall's notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios. [ABSTRACT FROM AUTHOR]

With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced. [ABSTRACT FROM AUTHOR]

The article presents the findings of a study that evaluates the feasibility of using an analytical model for a multi-service queuing system with a SDFIFO queuing service discipline and finite compression mechanism to approximate queuing systems with different queuing service disciplines (e.g., FIFO, cFIFO) while also incorporating finite compression. The evaluation involves comparing results obtained from an analytical model with those of simulation studies. The study considers the blocking probability and average queue length as factors. Additionally, two types of compressed traffic were analysed: elastic and adaptive. These are characteristic of modern telecommunications networks, particularly in multimedia applications. This paper is an extended version of our paper published in 4th CoBCom 2022. [ABSTRACT FROM AUTHOR]

This paper displays the design and application of a model that simulates the queuing system of a fictional post office. Starting point for solving more complicated optimization tasks is to create a system model that consists of elements of reality and the relationships between these elements. The key part of the paper includes the model of a queuing system of a post office created in Anylogic simulation software. The model of the post office displays post office with 5 postal counters, a certain input flow and a certain service time with an exponential probability distribution. The model also includes statistics and cost calculation. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) with non-orthogonal multiple access (NOMA) has been anticipated to offer diverse real-time applications, wherein the crux is to guarantee the age of information (AoI) for dynamic traffic. However, the traffic temporal variation provokes the interdependence between queue status and interference, in which context the AoI performance remains to be further explored. In this paper, an analytical framework is established to characterize the AoI performance in NOMA-IoT networks with random Bernoulli and deterministic periodic arrivals. Particularly, a numerical algorithm is devised to obtain the queue service rate, and tractable expressions for AoI violation probability and average AoI under both the first-come first-served (FCFS) and the preemptive last-come first-served (LCFS-PR) service disciplines are derived. Simulations are conducted to validate the proposed analysis. The results unveil that LCFS-PR conduces to better AoI performance than FCFS, and yet the gain diverges for each device with different traffic arrival configurations. In addition, the result shows that with sporadic traffic arrival, the periodic pattern outperforms the Bernoulli pattern, whereas this advantage gradually diminishes with more frequent packet arrival. [ABSTRACT FROM AUTHOR]

Recent advances in aerial robotics and wireless transceivers have generated an enormous interest in networks constituted by multiple compact unmanned aerial vehicles (UAVs). UAV adhoc networks, i.e., aerial networks with dynamic topology and no centralized control, are found suitable for a unique set of applications, yet their operation is vulnerable to cyberattacks. In many applications, such as IoT networks or emergency failover networks, UAVs augment and provide support to the sensor nodes or mobile nodes in the ground network in data acquisition and also improve the overall network performance. In this situation, ensuring the security of the adhoc UAV network and the integrity of data is paramount to accomplishing network mission objectives. In this paper, we propose a novel approach to secure UAV adhoc networks, referred to as the blockchain-assisted security framework (BCSF). We demonstrate that the proposed system provides security without sacrificing the performance of the network through blockchain technology adopted to the priority of the message to be communicated over the adhoc UAV network. Theoretical analysis for computing average latency is performed based on queuing theory models followed by an evaluation of the proposed BCSF approach through simulations that establish the superior performance of the proposed methodology in terms of transaction delay, data secrecy, data recovery, and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we consider a tandem dual queuing system consisting of multi-server stages. Stage 1 is characterized by an infinite buffer, one-by-one service of customers, and an exponential distribution of service times. Stage 2 is characterized by a finite buffer and a phase-type distribution of service times. Service at Stage 2 is provided to groups of customers. The service time of a group depends on the size of the group. The size is restricted by two thresholds. The waiting time of a customer at each stage is limited by a random variable with an exponential distribution, with the parameter depending on the stage. After service at Stage 1, a customer can depart from the system or try to enter Stage 2. If the buffer at this stage is full, the customer is either lost or returns for service at Stage 1. Customer arrivals are described by the versatile Markov arrival process. The system is studied via consideration of a multi-dimensional continuous-time Markov chain. Numerical examples, which highlight the influence of the thresholds on the system performance measures, are presented. The possibility of solving optimization problems is illustrated. [ABSTRACT FROM AUTHOR]

The layout design of an airfield area plays a crucial role in ensuring the efficiency of aircraft ground operations. In order to minimize delays caused by insufficient capacity and prevent resource wastage due to excessive capacity during the operational phase, this paper developed a prediction model for operational efficiency leveraging queuing network theory and machine-learning models. Our approach involves four key steps: (1) establish a theoretical framework for analyzing the operational efficiency of airfield area layouts based on queuing network theory, (2) employ a combination of discrete modeling and multi-agent modeling to construct a simulation model for ground operations in the airfield area, (3) develop a prediction model, known as PSO-ANN, for forecasting the operational efficiency of the airfield area using the simulation results, (4) conduct computer-based simulation experiments to assess the sensitivity of airfield area parameters, observe traffic-flow phase transitions, and investigate the factors influencing operational efficiency. This methodology enables the rapid assessment of operational efficiency for small- and medium-sized airports, as well as regional multi-airport systems. It is particularly useful for program evaluation during the strategic planning phase. [ABSTRACT FROM AUTHOR]

With the rise of critical machine-to-machine applications, next generation wireless communication systems must meet challenging requirements with respect to latency and reliability. A key question in this context relates to channel state estimation, which allows the transmitter to adapt the code rate to the channel state. In this paper, we characterize the tradeoff between the training sequence length and data codeword length: shorter channel estimation leaves more time for the payload transmission but reduces the estimation accuracy and causes more decoding errors. Using lower coding rates can mitigate this effect, but may result in a higher backlog of data at the transmitter. In order to optimize the training sequence length and the rate adaptation scheme with respect to the delay performance, we employ queuing analysis on top of accurate models of the physical layer. We obtain an analytically tractable solution to the problem by deriving a closed-form approximation for the decoding error probability due to imperfect channel knowledge and finite-blocklength channel coding. The optimized training sequence length and rate adaptation strategy can reduce the delay violation probability by an order of magnitude, compared with suboptimal strategies that do not consider the delay constraints. [ABSTRACT FROM AUTHOR]

This paper focuses on the modeling and analysis of the temporal performance variation experienced by a mobile user in a wireless network and its impact on system-level design. We consider a simple stochastic geometry model: the infrastructure nodes are Poisson distributed while the user’s motion is the simplest possible, i.e., constant velocity on a straight line. We first characterize variations in the signal-to-noise ratio (SNR) process and associated downlink Shannon rate, resulting from variations in the infrastructure geometry seen by the mobile. Specifically, by making a connection between stochastic geometry and queuing theory, the level crossings of the SNR process are shown to form an alternating renewal process whose distribution is completely characterized. For large/small SNR levels, and associated rare events, we further derive simple distributional (exponential) models. We then characterize the second major contributor to such variations, namely, changes in the number of other users sharing the infrastructure. Combining these two phenomena, we study what are the dominant factors (infrastructure geometry or sharing number) when a mobile experiences a very high/low shared rate. These results are then used to evaluate and optimize the system-level quality of experience of the mobile users sharing such a wireless infrastructure, including mobile devices streaming video which proactively buffer content to prevent rebuffering and mobiles which are downloading large files. Finally, we use simulation to assess the fidelity of this model and its robustness to factors which are presently not taken into account. [ABSTRACT FROM PUBLISHER]

The arrival of 5G will usher in an era of "Internet of Everything." Massive Internet of Things data contains great value in the dynamic analysis of alternative elements of automated packaging systems. From the perspective of the realization of personalized customization functions, this article solves the problem of dynamic analysis of alternative elements in the automated packaging system. We analyze the connection mechanism and interaction method between the cloud service system layer and the mobile terminal service layer, and carry out the corresponding software design. From the perspective of the realization of the intelligent production of the system in this paper, this topic mainly studies the information interaction mechanism and production control mechanism of the cloud service system and the manufacturing system. Based on the hardware of the manufacturing system layer, a flexible production implementation mechanism is formulated to make it the basis for the implementation of intelligent production of the system. Based on the massive data processing capabilities of the cloud service system, the information processing mechanism and the production planning decision-making mechanism are formulated for it, so as to realize the intelligent adjustment of the manufacturing system layer in the production process. For the connection scenario of IoT group paging, based on the application of NB-IoT technology in the next-generation mobile communication network, the focus of network optimization is to ensure the random access performance of IoT devices as much as possible. To this end, this paper proposes a random access optimization strategy for IoT group paging based on time slot scattering. We establish a mathematical model based on queuing theory for the connection scenario of the Io T group paging, then use the mathematical formula to derive the number of IoT devices scattered to each time slot in the initial state, thereby deriving the specific time slot scattering algorithm. This paper establishes a list of credit nodes, changes the participation mode of consensus nodes from static to dynamic, and supports voting to select trusted nodes. We designed a credit evaluation mechanism as a basis for consensus node elections to improve system's fault tolerance rate. The algorithm process was simplified, and the PBFT algorithm process was simplified from a 3-phase protocol to a 2-phase protocol to further reduce communication bandwidth overhead and algorithm time. Simulation analysis shows that, compared with the PBFT algorithm, the proposed algorithm improves node flexibility and fault tolerance while reducing communication bandwidth overhead by about 45%, packaging throughput by about 4%, and latency by about 3%. [ABSTRACT FROM AUTHOR]

This paper investigates a single-server queuing inventory system with impatient customers and multiple vacation strategies, where the first vacation is a working vacation. After a customer completes the service, he or she will bring a product away with probability r or not to bring a product away with probability 1 - r. Based on the M/M/1 queuing model, we construct a three-dimensional Markov process to represent the number of customers, inventory level, and server state in the system. The steady-state conditions of the system are obtained by the Neuts-Rao truncation method. Using the matrix geometric solution method, the steady-state performance indicators are derived. In addition, the cost function is established to do optimization analysis of the system. Finally, numerical experiments are conducted to analyze the effects of variations in system parameters on the performance indicators, and to determine the optimal inventory strategy and minimum cost under specific system parameters. [ABSTRACT FROM AUTHOR]

In the paper, a queueing system with an unlimited number of servers and two phases of service with degradation in the service rate is studied. The problem of service rate degradation emerges in cloud nodes, where there is contention for hardware resources including computational resources such as CPU cores. In a node, we have a limited number of CPU cores that should execute potentially an unlimited number of processes (requests) in parallel. In our model, the term "server" means a process allocated in the node for execution. So, the number of "servers" is unlimited but their individual performances decrease because CPUs should switch between them during the execution. We consider processes executed in the node with two phases of life cycle that reflects periods with different activity of a process; e.g., in the first phase, the process may require intensive usage of CPU cores but low usage in the second one. Our model distinguishes the phases using different service parameters for them as well as different influence on the service rate degradation in the node. In the paper, two analytical methods are proposed: exact solving of the system of the local balance equation and the asymptotic analysis of the global balance equations. Formulas for the stationary probability distribution of the number of customers in the phases are obtained for both cases. Several numerical examples are provided that illustrate some properties and applicability of the obtained results. [ABSTRACT FROM AUTHOR]

This paper is concerned with the asymptotic analysis of sojourn times of random fields with continuous sample paths. Under a very general framework we show that there is an interesting relationship between tail asymptotics of sojourn times and that of supremum. Moreover, we establish the uniform double-sum method to derive the tail asymptotics of sojourn times. In the literature, based on the pioneering research of S. Berman the sojourn times have been utilised to derive the tail asymptotics of supremum of Gaussian processes. In this paper we show that the opposite direction is even more fruitful, namely knowing the asymptotics of supremum of random processes and fields (in particular Gaussian) it is possible to establish the asymptotics of their sojourn times. We illustrate our findings considering i) two dimensional Gaussian random fields, ii) chi-process generated by stationary Gaussian processes and iii) stationary Gaussian queueing processes. [ABSTRACT FROM AUTHOR]

Queuing models where arrival rates go down consequent to increase in the number of customers are called systems with discouraged arrivals. Discouraged arrivals are distinct from balking in the sense that balking implies that arriving customers do not join. In this paper, we model a single server finite buffer Markovian queuing model with discouraged arrival, balking, reneging and retention of reneged customers. The steady state probabilities are obtained using Markov process method. Closed form expression of traditional as well as some freshly designed performance measures are presented. We also perform sensitivity analysis to examine the variations in performance measures with the variations in system parameters. Our results are numerically illustrated through a field level problem with design connotations. [ABSTRACT FROM AUTHOR]

Urban air traffic has gradually attracted attention in recent years, which will bring endless vitality to future urban development. An objective and accurate method of assessing vertiport capacity is the basis for the air traffic flow management of UAVs, which plays an important role in improving the efficiency of urban airspace resources. First of all, this paper establishes a theoretical capacity calculation model for ground facilities such as takeoff and landing platforms, taxiways and aprons, respectively. Next, this paper analyzes the service characteristics of each ground facility and establishes different types of UAV queuing systems to obtain UAV delay curves based on a UAV Poisson flow arrival model. Subsequently, a suitable acceptable delay level is selected to obtain the corresponding UAV flow, which means the actual capacity of UAV operations. Eventually, the validity of the model is verified through actual drone data arithmetic examples. The calculation results show that the combination of "1 landing + 2 takeoffs" can achieve better capacity results and that the landing platform is more prone to congestion than the takeoff platform. Change in average service time has the greatest impact on the apron capacity, and the takeoff platform is the most sensitive to changes in the acceptable delay level. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to provide a framework for the optimal design of queueing systems of call centers with delay information. The main decisions in the design of such systems are the number of servers, the appropriate control to announce delay anticipated. Design/methodology/approach – This paper models a multi-server queueing system as an M/M/S+M queue with customer reactions. Based on customer psychology in waiting experiences, a number of different service-level definitions are structured and the explicit computation of their performance measures is performed. This paper characterizes the level of satisfaction with delay information to modulate customer reactions. Optimality is defined as the number of agents that maximize revenues net of staffing costs. Findings – Numerical studies show that the solutions to optimal design of staffing levels and delay information exhibit interesting differences, especially U-shaped curve for optimal staffing level. Experiments show how call center managers can determine economically optimal anticipated delay and number of servers so that they could control the trade-off between revenue loss and customer satisfaction. Originality/value – Many results that pertain to announcing delay information, customer reactions, and links to satisfaction with delay information have not been established in previous studies, however, this paper analytically characterizes these performance measures for staffing call centers. [ABSTRACT FROM AUTHOR]

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs. [ABSTRACT FROM AUTHOR]

Existing optical and wireless networks designed to support today’s services may not be suitable for the emerging services, such as machine-to-machine applications of the Internet-of-Things paradigm and tactile-haptic applications of the Tactile Internet paradigm. Some of these applications must adhere to stringent quality of service requirements, especially an ultra-low end-to-end latency constraint of approximately 1 ms. In this paper, we investigate the feasibility of the wireless local area network (WLAN) infrastructure, specifically the IEEE 802.11 HCF Controlled Channel Access (HCCA) media access control (MAC) protocol, in supporting low-latency applications. The scenario considered is tactile-haptic data transmission between body-worn data collection devices and a wireless access point. In particular, we focus on uplink direction rather than downlink direction, because uplink data transmission forms a latency bottleneck of this particular network. In this paper, we first formulate an analytical model of the HCCA using a queuing theory to evaluate the average uplink latency. We then carry out global sensitivity analyses to understand the implications of various timing and network parameters on the uplink latency. Using these insights, we propose a novel strategic parameter selection (SPS) algorithm that effectively reduces uplink latency of the IEEE 802.11 WLAN. This paper provides insights into WLAN design such that latency-sensitive applications can be supported in the near future. [ABSTRACT FROM AUTHOR]