Queue Management Research Articles

The design approach of an artificial intelligent (AI) medical system based on electronical health records (EHR) and priority segmentations

AbstractIn the rapidly evolving landscape of digital systems and modern technology, including computing and device peripherals, a paradigm shift is evident across various engineering fields. This transformation is characterized by the integration of cutting‐edge concepts such as Artificial Intelligence (AI), Machine Learning, Deep Learning, Cloud Computing, and Smart Digital Systems. Notably, these advancements hold significant promise in Biomedical Engineering, where they play crucial roles in comprehending human conditions and anatomy. Given the paramount importance of health, especially amidst rising diseases and health‐related challenges, there arises a pressing need for optimal solutions. This research focuses on the integration of Electronic Health Record (EHR) systems with AI to deliver efficient solutions. Specifically, the research targets issues related to prioritization and segment queue management, with the aim of enhancing overall proficiency and efficiency in healthcare operations. The research presents a prototype version developed, deployed, and experimentally evaluated to assess its performance in achieving the stated objectives. Through this investigation, the research seeks to contribute to the advancement of EHR systems and AI integration in healthcare, ultimately aiming to enhance patient care and healthcare delivery processes.

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.

Optimizing Restaurant Queue Management: A Simulation-Based Approach Using Arena Software

Examining the effectiveness of models in terms of utilization and waiting time is one of the anticipated benefits of analyzing queuing systems. This study uses Arena simulation software to examine the queue system of a particular restaurant. The primary objective is to assess and enhance the restaurant’s service efficiency through the modelling of customer arrivals, service procedures, and queue dynamics. Data on customer arrivals and service times were collected for the restaurant’s current system. The necessary expressions were developed from the observed data using the input analyzer. A conceptual model of the original queuing system was created, and two alternative Arena models were developed to reduce customer waiting times in the restaurant. The developed models were run to evaluate their performance. During a 24-hour simulation study, the average waiting time in the overall model was found to be 24.49 minutes, with a maximum of 48.69 minutes. The waiting time for server 1 was 25.30 minutes, for server 2 was 23.27 minutes, for server 3 was 25.11 minutes, and for server 4 was 22.30 minutes. The number of customers in line at server 1 was 12.65, at server 2 was 6.98, at server 3 was 8.29, and at server 4 was 8.25. Resource utilization was 57% for server 1, 35% for server 2, 27% for server 3, and 15% for server 4. By decreasing the queuing length and average waiting time in the restaurant, customer satisfaction can be increased, leading to a reduction in time wasted. The findings indicate significant potential for improving overall service quality and reducing client wait times. This research provides valuable insights for restaurant managers seeking to enhance customer satisfaction and operational efficiency.

DESiRED — Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry

Active Queue Management (AQM) is a mechanism employed to alleviate transient congestion in network device buffers, such as routers and switches. Traditional AQM algorithms use fixed thresholds, like target delay or queue occupancy, to compute random packet drop probabilities. A very small target delay can increase packet losses and reduce link utilization, while a large target delay may increase queueing delays while lowering drop probability. Due to dynamic network traffic characteristics, where traffic fluctuations can lead to significant queue variations, maintaining a fixed threshold AQM may not suit all applications. Consequently, we explore the question: What is the ideal threshold (target delay) for AQMs? In this work, we introduce DESiRED (Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise network feedback from In-band Network Telemetry (INT) to feed a Deep Reinforcement Learning (DRL) model. This model dynamically adjusts the target delay based on rewards that maximize application Quality of Service (QoS). We evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH service. Our findings demonstrate up to a 90x reduction in video stall and a 42x increase in high-resolution video playback quality when the target delay is adjusted dynamically by DESiRED.

Surgical waiting lists and queue management in a Brazilian tertiary public hospital

BackgroundCentralized management of queues helps to reduce the surgical waiting time in the publicly funded healthcare system, but this is not a reality in the Brazilian Unified Healthcare System (BUHS). We describe the implementation of the “Patients with Surgical Indication” (PSI) in a Brazilian public tertiary hospital, the impact on waiting time, and its use in rationing oncological surgeries during the COVID-19 Pandemic.MethodsRetrospective observational study of elective surgical requests (2016–2022) in a Brazilian general, public, tertiary university hospital. We recovered information regarding the inflows (indications), outflows and their reasons, the number of patients, and waiting time in queue.ResultsWe enrolled 82,844 indications in the PSI (2016–2022). The waiting time (median and interquartile range) in days decreased from 98(48;168) in 2016 to 14(3;152) in 2022 (p < 0.01). The same occurred with the backlog that ranged from 6,884 in 2016 to 844 in 2022 (p < 001). During the Pandemic, there was a reduction in the number of non-oncological surgeries per month (95% confidence interval) of -10.9(-18.0;-3.8) during Phase I (January 2019-March 2020), maintenance in Phase II (April 2020-August 2021) 0.1(-10.0;10.4) and increment in Phase III (September 2021-December 2022) of 23.0(15.3;30.8). In the oncological conditions, these numbers were 0.6(-2.1;3.3) for Phase I, an increase of 3.2(0.7;5.6) in Phase II and 3.9(1,4;6,4) in Phase III.ConclusionImplementing a centralized list of surgical indications and developing queue management principles proved feasible, with effective rationing. It unprecedentedly demonstrated the decrease in the median waiting time in Brazil.

Response Time of Queueing Mechanisms

We study response time, a key performance characteristic of queueing mechanisms. The studied model incorporates both active and passive queue management, arbitrary service time distribution, as well as a complex model of arrivals. Therefore, the obtained formulas can be used to calculate the response time of many real queueing mechanisms with different features, by parameterizing adequately the general model considered here. The paper consists of two parts. In the first, mathematical part, we derive the distribution function for the response time, its density, and the mean value. This is done by constructing two systems of integral equations, for the distribution function and the mean value, respectively, and solving these systems with transform techniques. All the characteristics are derived both in the time-dependent and steady-state cases. In the second part, we present numerical values of the response time for a few system parameterizations and point out several of its properties, some rather counterintuitive.

An active queue management for wireless sensor networks with priority scheduling strategy

In Wireless Sensor Networks (WSNs), the packet congestion will lead to high delay and high packet loss rate, which severely affects the timely transmission of real-time packets. As a congestion control method, Random Early Detection (RED) is able to stabilize the queue length at a low level. However, it does not classify the data of WSNs to achieve a targeted queue management. Since real-time packets are more urgent and important than non-real-time packets, differential packets scheduling and queue management are necessary. To deal with these problems, we propose an Active Queue Management (AQM) method called Classified Enhanced Random Early Detection (CERED). In CERED, the preemption priority is conferred on real-time packets, and the queue management with enhanced initial drop probability is implemented for non-real-time packets. Next, we develop a preemptive priority M/M/1/C vacation queueing model with queue management to evaluate the proposed method, and the finite-state matrix geometry method is used to solve the stationary distribution of the queueing model. Then we formulate a non-linear integer programming problem for the minimum delay of real-time packets, which subjects to constraints on the steady state and system cost. Finally, a numerical example is given to show the effectiveness of the proposed method.

Analisis Respon Konsumen Terhadap Antrean Yang Terjadi Di SPBU 3440506 Cipatik Kecamatan Cihamplas Kabupaten Bandung Barat

This study aims to analyze consumer responses to the queue phenomenon that occurs at Cipatik Public Fuel Filling Station (SPBU). Queues at gas stations are often a major concern for consumers, affecting customer satisfaction and the operational performance of gas stations. This study identifies the main factors that influence consumer response to queues, focusing on queue cases, waiting time, and consumer feelings regarding service. The research method used a quantitative approach through a consumer survey involving a representative sample of Cipatik gas station users. Data was collected through a questionnaire covering aspects such as consumer perception of service speed, queue efficiency, and overall satisfaction level. The results of data analysis show that there is a lack of consumer satisfaction with the queues that occur at Cipatik Service Station, West Bandung Regency. Therefore, it is necessary to improve queue management by the gas station. The addition of filling pump lines needs to be addressed so that the service level can increase. In addition, shorter waiting times and a more efficient service experience are considered by consumers as key factors in increasing their satisfaction. The findings of this study provide valuable insights for Cipatik Service Station management and relevant stakeholders to improve queue management and increase customer satisfaction. The practical implications of this study can assist policy makers in developing more effective operational strategies to address queuing issues and improve consumer experience at the gas station.

Analysis and Design of Queue Service Information System Integrated with WhatsApp using UML Method

In the rapidly developing digital era, information systems have become an important element in increasing efficiency and effectiveness in various activities. As an important part of public services, queuing services withstand significant challenges in managing rapid population growth. Long waiting times and inefficient queue management can result in user dissatisfaction, increased workload for service providers, and potentially harm productivity. This research aims to overcome these challenges by identifying, pivoting, and creating an innovative information system integrated with the popular cross-platform application, WhatsApp. The main focus is automating queue service management to increase efficiency and user convenience. The resulting system is expected to provide significant changes in public services, reduce waiting times, and make queue management easier. The Unified Modeling Language (UML) methodology is used in the design of this system, providing systematic guidance to achieve these goals. In this research, we identify problems, user needs, functional requirements, and non-functional systems, including queue registration, queue management, user management, and service management. The results include use case diagrams, activity diagrams, sequence diagrams, and class diagrams, which provide a detailed overview of various aspects of the system. The results of this research can serve as a guide for application developers and demonstrate the benefits of UML in designing complex information systems.

Congestion tracking control of multi-bottleneck TCP networks with input-saturation and dead-zone

&lt;abstract&gt;&lt;p&gt;This paper discusses the congestion control challenges in a network employing multi-bottleneck Transmission Control Protocol/Active Queue Management (TCP/AQM). The study specifically focuses on networks characterized by input nonlinearity and unknown disturbances. We regard the network as a whole, and consider the influence between multiple nodes and unknown disturbance, a dynamic model of multi-bottleneck network is established. And the impact of dead zone and saturation on the system is taken into account for the first time in the model, the builded TCP/AQM model is more practicable. Based on the characteristics of fuzzy logic systems (FLS), combined with backstepping technology and Lyapunov function, an adaptive congestion control algorithm is designed to make full use of the link resources of each node and improve the network utilization. Ultimately, the proposed algorithm's efficacy and superiority are substantiated through simulation.&lt;/p&gt;&lt;/abstract&gt;

Toward an Integrated Model for Examining the Factors Affecting the Acceptance of Queue Management Solutions in Healthcare

Toward an Integrated Model for Examining the Factors Affecting the Acceptance of Queue Management Solutions in Healthcare

A four-state Markov model for modelling bursty traffic and benchmarking of random early detection

Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model.

QUEUING MANAGEMENT SYSTEM IN MANUEL S. ENVERGA UNIVERSITY FOUNDATION CANDELARIA INC.

Queue management in schools is becoming more and more important for improving the general student experience and making administrative tasks run more smoothly. This abstract shows everything you need to know about the Queuing Management System in Manuel S Enverga University Foundation Candelaria, Inc. In order to improve user satisfaction, shorten wait times, streamline the queuing process, and boost productivity, this study examines the use and impact of queueing management systems in educational institutions. By digitiizing the queuing process, these systems offer several benefits. Students can join virtual queues, eliminating the need to physically wait in line, which saves them time and enhances their satisfaction with administrative processes. Instead of focusing their efforts on staffing lines, faculty and administrative personnel can handle queues more effectively and help students. The technique also improves data accuracy and offers insightful information about queue patterns, which aids in the decision-making process for schools looking to improve services. Delivering a fully functional and flexible Queue Management System that greatly improves the effectiveness of queue management across multiple lines and apps on campus is the primary objective of this study.

Chronology of the development of Active Queue Management algorithms of RED family. Part 1: from 1993 up to 2005

This work is the first part of a large bibliographic review of active queue management algorithms of the Random Early Detection (RED) family, presented in the scientific press from 1993 to 2023. The first part will provide data on algorithms published from 1993 to 2005.

Evaluation of TCP Congestion Control Algorithms with traffic control policies in a PEP-based geosynchronous satellite scenario

Much work has been done in recent years comparing TCP variants in many scenarios, including the satellite one. Some of these studies were carried out with the ns-2 and ns-3 simulators, which may give a good idea of the behavior of these TCP variants, although the results cannot be considered completely realistic. Some of the latest studies use virtualization to derive a variety of measures to test the suitability of Congestion Control Algorithms in a more realistic way. Such studies also take into account new variants of TCP, such as Google’s BBR. However, few of these works have evaluated the behavior of these variants in a scenario based on Performance Enhancing Proxies. Furthermore, no emphasis has been placed on those variants of TCP that try to maximize throughput and minimize delay, which is crucial in many services with low latency requirements. Our paper aims to fill this gap, offering a comparative assessment of the performance of these three TCP flavors: BBR, YeAH and CUBIC. We have created four different approaches to mix these TCP flavors with two traffic control techniques, including Active Queue Management (AQM) policies. To perform the testings, we have developed a TCP-Splitting satellite Emulation Framework (TSEF), a platform that allows us to evaluate the performance of TCP flavors on the forward channel of a geosynchronous satellite scenario based on Performance Enhancing Proxies (PEPs). According to our results, we can conclude that the application of AQM policies and ECN marks on aggressive TCP flavors like CUBIC is mandatory to maximize throughput and minimize delay.

P4SQA: A P4 Switch-Based QoS Assurance Mechanism for SDN

Data center networks include a wide variety of service categories, which leads to a complex network topology. As a result, the QoS of different service flows cannot be guaranteed. In this paper, we propose a QoS assurance mechanism based on P4 switches (P4SQA) to make full use of network resources. P4SQA includes two functions: in-network traffic classification and in-network queue management. To reduce the communication overhead and computational pressure on the Software Defined Network (SDN) controller, we offload part of the neural computation from the control plane to the data plane. A fully connected neural network-based traffic classification method is deployed in the programmable data plane to classify different traffic types. In addition, the optimized active queue management algorithm CoDeL is integrated into the P4 switch (PCoDeL) to prevent network congestion while guaranteeing QoS more effectively. The evaluation results show that traffic classification accuracy has improved, especially in terms of the F1_score, which reached 0.97, significantly better than other state-of-the-art methods. Moreover, compared to traditional queue management algorithms, PCoDeL can improve network throughput and reduce transmission delay, which verifies the effectiveness and superiority of P4SQA in ensuring QoS.

RED-SP-CoDel: Random early detection with static priority scheduling and controlled delay AQM in programmable data planes

Emerging network application paradigms, such as the Tactile Internet, re-emphasize the need for different Quality of Service (QoS) levels. Due to the large packet buffers in the underlying network data plane, Active Queue Management (AQM) is generally required to curtail packet latencies for flows requiring high QoS levels. At the same time, programmable data planes, such as P4, enable packet processing at line-speed, albeit with limited packet processing functionalities. However, the existing AQM mechanisms that support QoS differentiation are too complex to readily run on P4, while the existing AQM mechanisms that run on P4 do generally not support effective QoS differentiation. We address this gap by developing to the best of our knowledge the first AQM mechanism that supports effective QoS differentiation while running on P4. Specifically, we propose SP-CoDel, which combines the well-known Controlled Delay (CoDel) AQM mechanism with Static Priority (SP) scheduling for QoS differentiation. Also, we propose RED-SP-CoDel, which adds a RED AQM component to SP-CoDel so as to make the AQM with priorities essentially parameterless. As a community resource contribution, we substantially extend the existing P4Simulator to the novel fused P4-NS3 Simulator so as to enable the evaluation of packet processing mechanisms through the combined functionalities of P4 device emulation and NS3 packet simulation. Evaluations conducted with the P4 reference switch model in the P4-NS3 Simulator indicate that SP-CoDel and RED-SP-CoDel provide high QoS to high-priority data streams, i.e., significantly reduce latency and packet loss compared to CoDel, while effectively mitigating bufferbloat.

Development of queue management model for effective service delivery in automobile repair shops

In this study, a queue management model/software was developed for automobile repair shops. This software minimizes both waiting time of customers, and idle time of workers. The software groups all jobs done in an automobile repair shop into three sections: mechanical, electrical and air conditioning (A/C) sections. The developed software prioritizes all jobs performed in a typical automobile repair shop in terms of urgency, using the ‘shortest processing time’ scheduling approach. The jobs are then ranked and assigned to workers based on number of available workstations. Also, expected time of delivery of the job is estimated by the software. The software was tested using data obtained over a two weeks period and percentage reduction in waiting time was found to range between: 0.889-0.122, 0.895-0.25 and 1-0.167 for the mechanical, electrical and A/C sections respectively. In the mechanical section, servicing/replacement of piston and rings and changing/servicing of gear box were discovered to be the jobs with maximum delivery time (15 hrs) while changing of engine belt was seen as the job with the least delivery time (0.3 hrs). For the electrical section, changing of alternator, with delivery time of 3 hrs 30 mins and changing of headlamp and fuse breakage with delivery time of 0.15 hrs are the jobs with maximum and minimum delivery times respectively. For the A/C section, changing of freezer with delivery time of 10 hrs and refilling of gas with delivery time of 0.10 hrs are the maximum and minimum respectively. Implementation of this research will reduce time wastage experienced by customers, idle time at service facilities, frustration caused by unnecessary queue, and encourage allocation of resources accordingly.

Machine Learning based Integrated Hospital Appointment Booking and Queue Management System

Hospitals have a growing challenge of long queues and consequently more waiting time for patients. Hospitals can better manage resources and anticipate more inflow of patients through estimations on patient count. In this paper, we propose a web-based Machine Learning based Integrated Hospital Appointment Booking and Queue Management System that aims to reduce the queue time of patients and forecast the number of patients present at a given hour of the day. Queue time reduction is done by generating a unique token number for every patient which is used to lead him directly to the respective department and patient forecasting is done using Demand Forecasting which is a Machine Learning Regression Algorithm to predict future patient count at a particular hour of the day. The results can help patients make a better decision of choosing a less crowded time. This will also help the hospital to manage the patient crowd, logistics and resources more efficiently. Keywords — Hospital, queue, patient, integration, Integrated, doctor, web-based, demand forecasting, machine learning, token.

Double Function Random Early Detection (DFRED): A Revised RED-Oriented Algorithm

Dropping packets with a linear function between two configured queue thresholds in Random Early Detection (RED) model is incapable of yielding satisfactory network performance. In this article, a new enhanced and effective active queue management algorithm, termed Double Function RED (DFRED in short) is developed to further curtail network delay. Specifically, DFRED algorithm amends the packet dropping probability approach of RED by dividing it into two sub-segments. The first and second partitions utilizes and implements a quadratic and linear increase respectively in the packet dropping probability computation to distinguish between two traffic loads: low and high. The ns-3 simulation performance evaluations clearly indicate that DFRED algorithm significantly controls the average queue occupancy and yields a reasonable gain in end-to-end-delay under different network conditions.