Queuing Scheme Research Articles

An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks

Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. Traditional methods for mitigating congestion primarily focus on routing strategies that utilize first-in-first-out (FIFO) queueing disciplines to determine the processing order of packets in buffer queues. However, these approaches often fail to explore the benefits of incorporating priority mechanisms directly within the routing decision-making processes, leaving significant room for improvement in congestion management. This study introduces an innovative generalized priority queueing (GPQ) strategy, specifically designed as an enhancement to existing FIFO-based routing methods. It is important to note that GPQ is not a new queue scheduling algorithm (e.g., deficit round robin (DRR) or weighted fair queuing (WFQ)), which typically manage multiple queues in broader queue management scenarios. Instead, GPQ integrates a dynamic priority-based mechanism into the routing layer, allowing the routing function to adaptively prioritize packets within a single buffer queue based on network conditions and packet attributes. By focusing on the routing strategy itself, GPQ improves the process of selecting packets for forwarding, thereby optimizing congestion management across the network. The effectiveness of the GPQ strategy is evaluated through extensive simulations on single-layer, two-layer, and dynamic networks. The results demonstrate significant improvements in key performance metrics, such as network throughput and average packet delay, when compared to traditional FIFO-based routing methods. These findings underscore the versatility and robustness of the GPQ strategy, emphasizing its capability to enhance network efficiency across diverse topologies and configurations. By addressing the inherent limitations of FIFO-based routing strategies and proposing a generalized yet scalable enhancement, this study makes a notable contribution to network optimization. The GPQ strategy provides a practical and adaptable solution for improving transportation efficiency in complex networks, bridging the gap between conventional routing techniques and emerging demands for dynamic congestion management.

Optimal multiresource planning for the elderly care system: a case study

This paper investigates an optimal long-term bed and caregiver plan for the elderly care system (ECS) to meet the increasing care demand due to accelerating population aging in China. Care demand is dynamic due to various arrival ratepatterns, heterogeneous lengths of stay, health status, and balking and reneging behaviours, while supply depends on health/age/waiting-time-based priority queueing strategies. We develop a simulation-optimisation model that combines discrete event simulation, a bisection search algorithm and a feasibility detection procedure for this problem. Based on data from the Chinese Longitudinal Healthy Longevity Survey, the Ministry of Civil Affairs of China, and the queue system for senior care institutions in Shenzhen, the results show that the increasing arrival pattern saves operational costs and waiting time. The age-based-priority (ABP) strategy can reduce evaluation and waiting costs and prevent rent-seeking before application because it counts only older adult’ age. Balking helps to reduce reneging. Low ratio of caregivers to seniors will lead to significant underestimation of caregiver capacity. The ECS can purchase beds step by step, apply the ABP strategy for admission, release waiting information to induce balking behaviour, and hire part-time caregivers or rent service robots to reach the optimal plan while serving more older adults.

Age of Information-Aware Networks for Low-Power IoT Sensor Applications

The Internet of Things (IoT) is a fast-growing field that has found a variety of applications, such as smart agriculture and industrial processing. In these applications, it is important for nodes to maximize the amount of useful information transmitted over a limited channel. This work seeks to improve the performance of low-powered sensor networks by developing an architecture that leverages existing techniques such as lossy compression and different queuing strategies in order to minimize their drawbacks and meet the performance needs of backend applications. The Age of Information (AoI) provides a useful metric for quantifying Quality of Service (QoS) in low-powered sensor networks and provides a method for measuring the freshness of data in the network. In this paper, we investigate QoS requirements and the effects of lossy compression and queue strategies on AoI. Furthermore, two important use cases for low-powered IoT sensor networks are studied, namely, real-time feedback control and image classification. The results highlight the relative importance of QoS metrics for applications with different needs. To this end, we introduce a QoS-aware architecture to optimize network performance for the QoS requirements of the studied applications. The proposed network architecture was tested with a mixture of application traffic settings and was shown to greatly improve network QoS compared to commonly used transmission architectures such as Slotted ALOHA.

Assessing and Implementing Efficient Queuing Methods to Address Traffic Congestion at Shekhi-Choli Intersection in Erbil City

Queuing theory is the best way for estimating waiting lines for many phenomena in different fields. Queuing models are used to make decisions on determining the traffic flow in order to avoid congestions in the streets; therefore, it is a useful tool for resolving these issues, which is the objective of this paper. The intersection of Shekhi-Choli in Erbil was undertaken to determine the appropriate queuing models for incoming street lines at the intersection, as well as to solve the problem of a constrained queuing line with excessive wait durations. This intersection is important because it connects three roads that provide access to and from Erbil's big bazaar. Data were obtained from the chosen intersection for 12 days from 3 to 5 PM daily. EasyFit software version 5.4 and TORA software version 2 were used to analyze the data. The primary performance metrics were identified, and optimal queuing models were chosen to service drivers. The empirical distribution of automobile arrivals and departures is a Poisson distribution, with traffic intensities of 92%, 88%, and 80% for the first line (Barzani Namr str.), second line (Shekhi Choli str.), and last line (Sultan Muzaffar str.), respectively. Queuing models involve key features, such as population size, queuing discipline (SIRO, LCFS, FCFS, GD), arrival process, waiting process, and service channels. The Shekhi-Choli Intersection's current queuing scheme to service drivers is (M/M/1):(FCFS/∞/∞) model. As a result, the Erbil traffic police division must decide to provide suitable instruction for the first and second lines in order to minimize driver waiting time. To obtain more reliable information and findings, new studies should be conducted on the same site for three periods of time (morning, afternoon, and evening), in addition to other crowded intersections in Erbil city.

Advances in Navigability and Mooring

Considerable technological progress has been made in ship handling and mooring in recent years, especially progress generated by the needs imposed by the introduction of ever larger ships. These advancements exploit the economic scale and environmental efficiency of larger vessels, but also present unique challenges, particularly in narrow waterways and harbour approaches. Precise navigation in these environments requires highly accurate hydrographic measurements, high-quality electronic charts, and advanced navigation systems, such as modern electronic chart display and information systems (ECDIS). Safe and efficient port operations also depend on the optimised allocation of port resources and comprehensive queuing strategies. Modern ships are increasingly susceptible to interference with Global Navigation Satellite Systems (GNSS) and Automatic Identification Systems (AIS), necessitating the development of resilient technologies and procedures to ensure navigational safety. In addition, climate change is exacerbating the challenges of ship handling in ports, as larger vessels are particularly vulnerable to sudden gusts of wind and have difficulty maintaining their position in the quay in strong crosswinds. Training and simulation are crucial to overcoming these challenges. Ship-handling simulators are invaluable for training purposes, but development is still needed to accurately simulate tilt and lean effects, especially when ships are sailing in narrow channels with following currents and changing winds. Improving the accuracy of these simulators will improve the preparation of seafarers for real-life conditions and ultimately contribute to safer and more efficient ship operations.

PROVING THE CORRECTNESS OF THE EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME IN THE CLIENT-SERVER SYSTEM

Numerous studies have been conducted to develop concurrency control schemes that can be applied to client-server systems, such as the Extended Serial Graph-Validation Queue (SG-VQ) scheme. Extended SG-VQ is a control concurrency scheme in client-server system which implements object caching on the client side and locking strategy on the server side. This scheme employs validation algorithms based on queues on the client side and graphs on the server side. This research focuses on the mathematical analysis of the correctness of the Extended SG-VQ scheme using serializability as the criterion that needs to be achieved. Implementing a cycle-free transaction graph is a necessary and sufficient condition to achieve serializability. In this research, the serializability of the Extended SG-VQ scheme has been proven through the exposition of ten definitions, two propositions, three lemmas, and one theorem.

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Effective algorithms for queue management are crucial in place of guaranteeing maximum efficiency in gateway routers since network traffic continues to expand dramatically. An online researcher has suggested the Active Queue Management (AQM) strategy regarding the upcoming generation of gateway switches. The common active queue scheme remains (RED) Random Early Detection. Random early detection is susceptible to parameterization issues and lacks a self-adaptation mechanism. Several RED variants have been formed; nevertheless, variations in traffic load have an adverse effect on all of them. Due to the fact that each has a static drop pattern, to address the RED and its variation schemes, the SARED system, or the design of self-adaptive random early detection was created. But in order to prevent congestion, during the time when the queue length surpasses a present maximum threshold limit, SARED aggressively removes packets. This causes networks having a lot of traffic situations the average is expected to increase queue delay, so in those cases, SARED should be less aggressive. This paper develops a priority-based queuing congestion control method for IoT gateways to manage network congestion. Our method (priority-based algorithms) performs substantially better with regard to throughput, delay, and packet loss than the present methods of SARED. The outcomes of the conducted simulation experiments have shown that in scenarios with heavy traffic loads, priority-based self-adaptive random early detection (PSARED) has greatly decreased average queuing delay by 3%, minimized average throughput by 1%, and decreased the rate of packet loss by 10% in contrast to SARED.

Distance-Based Queuing for Scalable and Reliable Linear Wireless Sensor Networks in Smart Cities.

The reliability and scalability of Linear Wireless Sensor Networks (LWSNs) are limited by the high packet loss probabilities (PLP) experienced by the packets generated at nodes far from the sink node. This is an important limitation in Smart City applications, where timely data collection is critical for decision making. Unfortunately, previous works have not addressed this problem and have only focused on improving the network's overall performance. In this work, we propose a Distance-Based Queuing (DBQ) scheme that can be incorporated into MAC protocols for LWSNs to improve reliability and scalability without requiring extra local processing or additional signaling at the nodes. The DBQ scheme prioritizes the transmission of relay packets based on their hop distance to the sink node, ensuring that all packets experience the same PLP. To evaluate the effectiveness of our proposal, we developed an analytical model and conducted extensive discrete-event simulations. Our numerical results demonstrate that the DBQ scheme significantly improves the reliability and scalability of the network by achieving the same average PLP and throughput for all nodes, regardless of traffic intensities and network sizes.

Palladium-catalyzed multi-component Heck alkynylcarbonylation of unactivated alkenes for synthesis of β,γ-alkynoates

A general and unprecedented Pd-catalysed four-component inter-molecular Heck alkynylcarbonylation involving haloalkynes, alkenes, carbon monoxide and alcohols based on a molecular queuing strategy is presented.

Evolutionary Game Analysis of Queue‐Jumping and Yielding Behaviours of Drivers With Type A and Type B Personality Traits

Although Type A personality traits have been confirmed to be more frequently engaged in risky driving behaviours, the existing research on the decision‐making of queue‐jumping behaviours has not considered this personality trait. This study aimed to explore the decision‐making processes of the subject driver’s queue‐jumping and the follower vehicle driver’s yielding behaviours with Type A and Type B personalities. First, the decision‐making utility variables for both players were selected, and a payoff matrix considering utility variable weights was constructed. Next, a decision‐making utility evaluation questionnaire was designed, and this questionnaire and the existing Type A behaviour pattern scale were investigated simultaneously. Then, the weight coefficients of the decision‐making utility variables were calculated; the replicated dynamic equations of four game combinations were constructed and the local stability principle of a dynamic system was used to determine the evolutionarily stable strategy for each game combination. Finally, the evolutionary process in which subject vehicle drivers select jumping the queue strategy and follower vehicle drivers select giving way strategy was simulated using MATLAB software based on empirical data to verify the validity of the constructed evolutionary game model. The results indicated some differences in the weight coefficients of decision utility variables between Type A and Type B personalities. The constructed game model can effectively reflect the decision‐making processes of subject and follower vehicle drivers of different personality types. The dynamic evolution processes of strategy selection were different for the four game combinations. This study revealed the evolutionary game process between subject and follower vehicle drivers, laying a theoretical foundation for traffic management departments to manage queue‐jumping behaviours.

Optimization of Teller Services Using Queuing Theory at XYZ Bank

Bank XYZ is one of the largest state-owned banks in Indonesia that provides various financial services. Currently Bank XYZ has 3 teller services only. This study aims to determine the use of customer queuing schemes currently used in XYZ bank teller services. The data processed is the arrival of customers who transact at the teller area of Bank XYZ. This study uses the Queuing Theory method with the M / M / S (Multiple Channel Single Phase) model and also uses FlexSim software. Then analyze with the addition of the number of tellers (services). Finally, a comparison is made between manual and software calculation methods. Based on the results of the data analysis, the optimal improvement in the application of the queuing system is to add 1 teller service to 4 teller services. The improvement in the performance of the queuing system is characterized by a reduction in the number of customers in line at peak hours 10.00 - 11.00 which initially amounted to 11 people reduced to 3 people and the average waiting time of customers in the queue from 10.08 minutes to 4.02 minutes. Therefore, optimal queuing system performance can be ensured by increasing the number of tellers to four.

A Clustering Routing Algorithm Based on Improved Ant Colony Optimization for an Urban Rail Transit Ad Hoc Network

As traffic pressure increases, urban rail transit has attracted attention because of its high speed and growing capacity in recent years. However, the traditional vehicle–ground communication systems of urban rail transit have been unable to meet the increasing requirements for communication. In this paper, we introduce an ad hoc network for urban rail transit to improve communication reliability, reduce end-to-end latency, and improve throughput of vehicle–ground communication. A novel clustering algorithm based on improved ant colony optimization is presented, which integrates various variables to elect cluster heads and adopts a low-latency queuing strategy. In addition, the algorithm can also replace the cluster heads adaptively to optimize the communication quality. Through the simulation results, the proposed algorithm can reduce the packet loss rate by about 60%, reduce the delay by about 50%, and improve the throughput by about 40% compared with the classical clustering routing protocol.

Debiased momentum contrastive learning for multimodal video similarity measures

The growing potential of multimodal short videos has contributed to a new type of recommendation. It depends on effectively measuring the similarities between the short video pairs, which consist of video frames and descriptions. Previous studies proposed using contrastive learning with in-batch negative sampling. However, such a strategy would take other semantically similar samples in the batch as negatives, leading to a biased issue. This paper proposes a debiased momentum contrastive learning (DMCL) on the unified multimodal Transformer model (VideoSim) for video similarity measures. The proposed DMCL alleviates the bias issue of negative sampling by introducing implicit knowledge of the model itself as soft labels. Instead of simply taking other samples in the batch as negatives, the proposed DMCL applies soft labels as supervision from the ground truth and the contextual semantic similarities between video-text pairs to alleviate the bias caused by negative sampling. In addition, DMCL expands the negative samples by a momentum queue strategy, which allows storing more multimodal representations to contrast more negatives. The experimental results of measuring multimodal video similarity show that the proposed DMCL outperforms all baselines in terms of Spearman’s rank correlation. Ablation studies and extensive analysis further demonstrate the effectiveness of the proposed method.

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling.

Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.

OPTIMAL QUEUING STRATEGIES FOR AN M/G/1 RETRIAL QUEUE SYSTEM WITH RWV AND ISEV POLICIES

AbstractDesigning a reasonable M/G/1 retrial queue system that enhances service efficiency and reduces energy consumption is a challenging issue in Information and Communication Technology systems. This paper presents an M/G/1 retrial queue system incorporating random working vacation (RWV) and improved service efficiency during vacation (ISEV) policies, and examines its optimal queuing strategies. The RWV policy suggests that the server takes random working vacations during reserved idle periods, effectively reducing energy consumption. In contrast, the ISEV policy strives to augment service efficiency during regular working periods by updating, inspecting or maintaining the server on vacations. The system is transformed into a Cauchy problem to investigate its well-posedness and stability, employing operator semigroup theory. Based on the system’s stability, steady-state performance measures, such as service efficiency, energy consumption and expected costs, are quantified using the steady-state solution. The paper subsequently demonstrates the existence of optimal queuing strategies that achieve maximum efficiency and minimum expected costs. Finally, two numerical experiments are provided to illustrate the effectiveness of the system.

An integrated strategy-based game-theoretic model and decentralized queueing system for mobile multi-robot task coordination

Autonomous multi-robot systems are restricted by their coordination ability with one another. Coordination entails the simultaneous division of tasks, the assignment of tasks, the formation of teams, the scheduling of tasks, and the routing of tasks. These activities are called task planning. Task planning is essential for the execution of task allocation problems in multi-robot systems. This study investigates the application of a decentralized queuing mechanism for task coordination in the context of mobile robots. Heuristic and strategic coordination strategies are used in developing the proposed system. The completion time and the distance traveled to process a demand are the two metrics used to evaluate the performance of robots in their respective operation field. The robots that used the stochastic queueing strategy achieved competitive results with those of the other robots.

A multi-objective fuzzy facility location problem with congestion and priority for drone-based emergency deliveries

Emergency medical service (EMS) plays an essential role in modern emergency and health systems; however, the delivery service via traditional ground vehicles faces enormous challenges, e.g., traffic congestion and poor road conditions, especially for time-critical products. Fortunately, drones provide an alternative solution for EMS due to faster speed, fewer road restrictions, and fewer workforce requirements, compared to ground vehicles. This paper develops a drone-based queuing-location model with stochastic demands and congestion for EMS, where drones perform as mobile servers with a generally distributed service time. In practice, delivery decisions are often made in the presence of imprecise information, and customer requests often have different priorities. However, to our best knowledge, limited research has addressed these issues. Thus, this paper employs fuzzy theory to cope with the vague drone endurance and demand arrival rate under a priority queuing strategy. A multi-objective optimization approach is adopted to balance the total cost, system efficiency, and equitable response time. As the resulting model is challenging to solve, we apply chance-constrained, second-order conic, fuzzy, and weighted goal programming approaches to recast the model as a crisp mixed-integer second-order conic program, which can be efficiently solved via off-the-shelf solvers. Results based on a case study show that our method can help decision-makers to better balance various objectives, make more flexible decisions with desirable fuzzy degrees, and significantly improve the service level of high priority demands for EMS.

Equilibrium strategies and pricing for a congested system with opaque price and loss-averse customers

In this paper, we study a service provider with opaque price strategy who sells services to loss-averse customers in a congestion-prone facility. The opaque price strategy means that the service provider discloses a price range of services to potential customers through social media. Loss aversion aims to characterize customers' reference-dependent preferences. We analyze a queueing game model with the help of an M/M/1 queueing system and adopt the cutoff structure characterized by reservation price to represent the loss-averse customers' joining decision. Then, we derive the loss-averse customer's reservation price and queueing strategies at equilibrium. In addition, the service provider sets the price to maximize her profit. We obtain some insights into how customers' degree of loss aversion affects the service provider's optimal price. For example, customers with a higher degree of loss aversion on the waiting time and the payment would push the service provider to decrease the optimal price. Customers with a higher degree of loss aversion about the service reward would urge the service provider to increase the optimal price. Furthermore, considering the attachment effect, we obtain the lowest possible expected profit of the service provider.

Impatient Queuing for Intelligent Task Offloading in Multiaccess Edge Computing

Multi-access edge computing (MEC) emerges as an essential part of the upcoming Fifth Generation (5G) and future beyond-5G mobile communication systems. It adds computational power towards the edge of cellular networks, much closer to energy-constrained user devices, and therewith allows the users to offload tasks to the edge computing nodes for low-latency applications with very-limited battery consumption. However, due to the high dynamics of user demand and server load, task congestion may occur at the edge nodes resulting in long queuing delay. Such delays can significantly degrade the quality of experience (QoE) of some latency-sensitive applications, raise the risk of service outage, and cannot be efficiently resolved by conventional queue management solutions. In this article, we study a latency-outage critical scenario, where users intend to limit the risk of latency outage. We propose an impatience-based queuing strategy for such users to intelligently choose between MEC offloading and local computation, allowing them to rationally renege from the task queue. The proposed approach is demonstrated by numerical simulations to be efficient for generic service model, when a perfect queue status information is available. For the practical case where the users obtain only imperfect queue status information, we design an optimal online learning strategy to enable its application in Poisson service scenarios.

Signaling Service Quality Through Queue Disclosure

Problem definition: We consider a single-server queueing system where service quality is either high or low. The server, who knows its exact quality level, can signal this quality information to customers by revealing or concealing its queue length. Based on this queue disclosure action and the observed queue length in the case of a revealed queue, customers decide whether to join the system. Academic/practical relevance: The queue disclosure action is regarded as a signal indicating the service quality. Methodology: We develop a signaling game and adopt the sequential equilibrium concept to solve it. We further apply the perfect sequential equilibrium as an equilibrium-refinement criterion. Results: In our baseline model, where all of the customers are uninformed of service quality, the pure-strategy perfect sequential equilibrium is always a pooling one, except at several discrete values of market size (measured by the potential arrival rate). When the market size is below a certain threshold, both high- and low-quality servers adopt queue concealment; otherwise, both types of servers adopt queue revelation. We also consider a general scenario in which the market is composed of both quality informed and uninformed customers. Under this setting, when the server conceals the queue, we can fully characterize customers’ equilibrium queueing strategies and the corresponding effective arrival rates. The unique sequential equilibrium outcome is still a pooling one when the market size is either below a lower threshold or above an upper threshold. A separating equilibrium can occur only when the market size falls between two thresholds; under that circumstance, the uninformed customers can infer the server’s quality from its queue disclosure behavior. Managerial implications: Under separating sequential equilibria, uninformed customers can fully infer the quality information and thus behave in an informed way. Unlike studies where queue disclosure is not regarded as a quality signal, our study reveals that the signaling effect of queue disclosure increases (decreases) the effective arrival rate of the high-quality (low-quality) server and also increases the customers’ total utility when the server is of low quality. Funding: P. Guo acknowledges the financial support from the Research Grants Council of Hong Kong [Grant 15502820]. The research of M. Haviv was funded by Israel Science Foundation [Grant 1512/19]. Z. Luo acknowledges the financial support from the Internal Start-up Fund of the Hong Kong Polytechnic University [Grant P0039035] and the National Natural Science Foundation of China [Grant 71971184]. Y. Wang’s work was supported by the Research Grants Council of Hong Kong [Grant 15505019]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1170 .