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This scientific paper explores the operation of a multiprocessor task servicing system. Tasks are received into the system at random intervals and are characterized by several stochastic parameters, including the number of processors required for their execution, the maximum allowable busy time for these processors, and the permissible waiting time in the task queue. The organization of task servicing in this system follows a first-in, first-out (FIFO) approach, ensuring uninterrupted processing. The key servicing process involves periodically selecting the first task in the queue and assessing its feasibility for immediate execution. If the task meets the necessary criteria, it is dispatched for processing. This process continues iteratively until a task is found, the parameters of which prevent immediate servicing. It is important to note that tasks in the queue have a limited window of time within which they can be serviced; otherwise, they may exit the system without service. This paper focuses on systems characterized by exponential distributions for random variables related to task arrivals, servicing times, and waiting restrictions. A system of equations is derived that describes the system's steady-state behavior. These equations enable the calculation of probabilities associated with the system's various states. Additionally, the paper provides insights into the probability distributions of virtual waiting times for tasks that arrive in the system at any given moment. [ABSTRACT FROM AUTHOR]

Daryl John Daley, a renowned mathematician and researcher in applied probability, passed away in 2023. He made significant contributions to the field of queueing theory and published numerous papers and books on point processes and epidemic modeling. Daley was also involved in the Applied Probability Group at the University of Melbourne and served as a trustee for the Applied Probability Trust. In addition to his mathematical career, he was an accomplished musician and hiker, and he provided guidance and support to students throughout his life. The document is a list of academic publications by mathematician Peter Taylor, covering various topics including probability theory, queueing theory, population models, and statistical analysis. The list includes articles published in different journals and conference proceedings, and it also acknowledges individuals who contributed to the material. [Extracted from the article]

An introduction is presented in which the editors discuss various papers within the issue on topics including analysis of queueing models, development of approximations for systems with abandonment, and link between empirical data and abandonment models.

Copyright of Electrotechnical Review / Elektrotehniski Vestnik is the property of Electrotechnical Society of Slovenia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist's reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs. [ABSTRACT FROM AUTHOR]

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Motivated by the applications of e-commerce platform services, a double-ended queueing system is considered in this paper. Clearly, the customers at both ends are strategic and their behaviours can affect the system performances. However, literatures almost only investigate one-side customers' strategic behaviours and ignore customers' behaviours on the other side. In this paper, we focus on the strategic behaviours of customers at both sides with asymmetrical information levels and study the interactions between them. Type-T customers are supposed to be insightful, who can observe system states and join the system following a pure threshold strategy. Type-P customers have no knowledge about the system states and join the system with equilibrium joining probability. An algorithm is provided to calculate customers' strategy behaviours. An application for the double-ended queueing system to a kitting process in the assembly system are investigated by numerical examples. [ABSTRACT FROM AUTHOR]

Queuing models consisting of servers which may not work with full efficiency are modelled with the help of fractional differential equations. Such problems with partial activity of the server are designed with the help of differential-difference equations involving fractional derivatives whose solutions are expressed in terms of Mittag-Leffler function. In this paper, we propose an alternative approach which gives a transient solution of fractional M/M/1 queue in matrix form. The results obtained by this new approach are justified by comparing them with solutions of classical queue which are available in the literature. Efficacy of the model can be assessed by computing its state probabilities and also measures such as expected number of customers in the system etc. Also, the variations in these measures with respect to partial activity of the server have been presented graphically and numerically. Further, a mathematical procedure to find optimal efficiency of the server has been discussed. [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]

Summary: Applications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%. [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]

The basic requirement for cognitive radio networks is that secondary users sense spectrum to detect whether primary users have arrived the system. However, imperfect sensing always occurs due to the interference of unreliable channel and signal, which can negatively affect the performance of both primary users and secondary users. To better study the practical problems of cognitive radio networks, we combine queueing theory with it. In this paper, we consider cognitive radio networks with imperfect sensing based on retrial queues with feedback and priority. Using the method of probability generating function, the performance measures are solved. A bi-objective optimization model is established to minimize both system cost and users' sojourn time. From the perspective of secondary users, we analyze both individual equilibrium strategy and socially optimal strategy. Meanwhile, in order to eliminate the differences between two strategies, we impose admission fees on secondary users and obtain the optimal pricing strategy. [ABSTRACT FROM AUTHOR]

In this article, we are going to extend the definition of literal neutrosophic random variables to literal refined neutrosophic random variables and we will study its new properties. We derive and present various concepts including refined neutrosophic probability distribution function in the continuous case, refined neutrosophic cumulative distribution function of continuous refined neutrosophic random variables, refined neutrosophic expected value, refined neutrosophic variance, refined neutrosophic standard deviation, refined neutrosophic mean deviation, refined neutrosophic rth quartiles, refined neutrosophic moments generating function, and refined neutrosophic characteristic function. Additionally, the paper includes many solved examples and applications. This paper opens the road to generalizing many concepts in neutrosophic probability theory specially neutrosophic reliability theory, neutrosophic stochastic processes, and neutrosophic queuing theory. [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]

In this paper, a quantitative research survey is carried out on discrete-time queueing models. In real-life scenarios, the idea of discrete-time queues has taken on a new meaning. This survey mainly focuses on the unfolding of discrete-time queueing models in recent decades, challenges implied on them and their influence in various fields. The ultimate goal of this paper is to provide enough information to all the researchers and analysts who toil in this field and wish to know more about these models. A few open issues and intriguing future research paths has been discussed. [ABSTRACT FROM AUTHOR]

Sterile services departments are special units designed to perform sterilisation operations in an efficient way within a hospital. The delays in sterilisation services cause significant disruptions on surgery schedules and bed management. To prevent the delays, an upper time limit can be imposed on the time spent in the sterilisation services. In this paper, we propose a mathematical modelling approach for the optimum capacity planning of a sterilisation service unit considering the uncertainties in the sterilisation process. The model aims to find the optimum capacity on four tandem steps of the sterilisation whilst at the same time minimising the total cost and keeping the maximum time in the system below a limit. Assuming general distributions for service and interarrival times, an approximation structure based on robust optimisation is used to formulate the maximum time spent in the system. We analysed the structural property of the resulting model and found that the relaxed version of the model is convex. The real data from a large sterilisation services unit is used for computational experiments. The results indicated that the approximation fits well against the simulated maximum time in the system. Other experiments revealed that an upper limit of 7 hours for the sterilisation services balances the cost vs. robustness trade-off. [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]

This paper describes the role of queuing theory in developing queuing networks in companies. Queuing networks can be considered as a collection of nodes where each node stands for a service facility. It is a powerful and versatile tool for modeling facilities in manufacturing products. In the realm of service industries like scooter manufacturing, the queuing theory and simulation play a vital role. These concepts help in predicting queue lengths and waiting durations when multiple scooters are manufactured and distributed using first come first serve discipline. Tables are used to explore the availability of furnished scooters in the companies and their comparative study analyzes the waiting scooters and space availability in the companies. [ABSTRACT FROM AUTHOR]

In this paper, we analysed the multiple working vacation queuing model with reneging under a single server policy. Reneging describes the situation where a customer or entity decides to leave the queue before being served. The presence of reneging behaviour affects queue and service efficiency, as customers leaving the queue prematurely can impact overall system performance and customer satisfaction. In this model, customers arrive at a service facility and form a queue to be served by a single server. The arrival follows the Poisson distribution, and the service follows the exponential process. Batches of customers are served under the General Bulk Service Rule. In GBSR, rather than individual customer arriving at a queue one by one, customers arrive in groups or batches. Thus, each batch of service contains a minimum of 'a' units and a maximum of 'b' units of customers. The steady-state equation, the various performance measures for the system, and particular cases of the described model are derived. [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 inability to choose an excellent service recommended by a system simply because it is not available is common when people use service recommendation systems. Traditional research on recommendation systems has focused on the user profile, QoS(Quality of Service), and SLA (Service-Level Agreement). However, if the recommended resources are not immediately available, users will not hesitate to move on because they are not tolerant of long waiting times or waiting queues. In the long run, the frequent occurrence of such cases affects the users' perception of the recommendation system and even the recommended services offered, which is the major hindrance to improving the rate of conversion of the results of a recommendation system. This paper proposes a model to assess service availability based on user tolerance. The availability of a given service is calculated by using the waiting time for it as well as the varying tolerances that different people have to the waiting time. We carried out a controlled experiment on a representative population that helped obtain the representations of the user tolerance of different groups by using disordered multi-nomial classification-based logistic regression. Following this, the traditional queuing model is improved by using this representation to formulate a more personalized method to analyze service availability. The results of this analysis can also be used to improve the traditional recommendation algorithm. To prove the effectiveness of this modification, the authors conducted validation on the same controlled population as above through computational experiments. The results show that the SAAM (Service Availability Assessment Model Based on User Tolerance) can significantly reduce the rate of user loss and waiting times compared with the traditional queuing model, which does not consider user tolerance. [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 transactional data typically collected/available on queueing systems are often subject to censoring as unsuccessful arrivals due to balking and/or unserved entities due to reneging are not recorded. In fact, in many situations, the true arrival, balking, and reneging events are unobservable, making it virtually impossible to collect data on these stochastic processes—information that is crucial for capacity planning and process improvement decisions. The objective of this paper is to estimate the true (latent) external arrival, balking, and reneging processes in queueing systems from such censored transactional data. The estimation problem is formulated as an optimization model and an iterative simulation-based inference approach is proposed to find appropriate input models for these stochastic processes. The proposed method is applicable in any complex queueing situation as long as it can be simulated. The problem is investigated under both known and unknown reneging distribution. Through extensive simulation experiments, general guidelines are provided for specifying the parameters of the proposed approach, namely, sample size and number of replications. The proposed approach is also validated through a real-world application in a call center, where it successfully estimates the underlying arrival, balking, and reneging distributions. Finally, to enable reproducibility and technology transfer, a working example, including all codes and sample data, are made available in an open online data repository associated with this paper. [ABSTRACT FROM AUTHOR]

New computing and communications paradigms will result in traffic loads in information server systems that fluctuate over much broader ranges of time scales than current systems. In addition, these fluctuation time scales may only be indirectly known or even be unknown. However, we should still be able to accurately design and manage such systems. This paper addresses this issue: we consider an M/M/1 queueing system operating in a random environment (denoted M/M/1(R)) that alternates between HIGH and LOW phases, where the load in the HIGH phase is higher than in the LOW phase. Previous work on the performance characteristics of M/M/1(R) systems established fundamental properties of the shape of performance curves. In this paper, we extend monotonicity results to include convexity and concavity properties, provide a partial answer to an open problem on stochastic ordering, develop new computational techniques, and include boundary cases and various degenerate M/M/1(R) systems. The basis of our results are novel representations for the mean number in system and the probability of the system being empty. We then apply these results to analyze practical aspects of system operation and design; in particular, we derive the optimal service rate to minimize mean system cost and provide a bias analysis of the use of customer-level sampling to estimate time-stationary quantities. [ABSTRACT FROM AUTHOR]