Queue Management Research Articles

End-to-end active queue management with Named-Data Networking

The innovative information-based Named-Data Networking (NDN) architecture provides a good opportunity to rethink many of the design decisions that are taken for granted in the Internet today. For example, active queue management (AQM) tasks have been traditionally implemented in the routers to alleviate network congestion before their buffers fill up. However, AQM operations could be performed on an end-to-end basis by taking advantage of NDN features. In this paper, we provide an implementation of an AQM algorithm for the NDN architecture that we use to drive a classical AIMD-based congestion control protocol at the receivers. To accomplish this, we take advantage of the 64-bit Congestion Mark field present in the link layer of NDN packets to encode both rate and delay information about each transmission queue along a network path. In order to make the solution scalable, this information is delivered stochastically, guaranteeing that receivers get accurate and updated information about every pertinent queue. This information is enough to implement the well-known controlled delay (CoDel) AQM algorithm. Simulation results show that our client-located CoDel implementation is able to react to congestion when the bottleneck queuing delay surpasses the 5ms target set by the usual in-network CoDel implementation and, at the same time, get a fair and efficient share of the available transmission capacity.

Optimal congestion management in network routers subject to constraints, disturbances, and noise using the model predictive control approach

AbstractA predictive queue management method is proposed for constrained congestion control in internet routers in the face of communication delays. The proposed method uses the queue and router models, input traffic rate, and queue length to precisely characterise the entire process. The model that has been built is then used to construct an optimal constrained active queue management (CAQM) strategy using the model predictive control method. Important factors, such as link capacity, Transmission Control Protocol (TCP) sessions, round‐trip time, and a few others, have been selected and used to linearise the interconnection of TCP. Then, an efficient MPC‐based structure to manage the CAQM in the face of unknown disturbances is designed. Simulations are used to validate the proposed method's effectiveness and robustness.

Deadline-Constrained Multi-Agent Collaborative Transmission for Delay-Sensitive Applications

Delay-sensitive applications impose the strict deadline-constrained latency requirement for end-to-end transmission. However, due to the high dynamics of link status and the limited network resources, it is still challenging to ensure deterministic end-to-end latency for deadline-constrained flows. To this end, we propose a deadline-constrained multi-agent collaborative transmission mechanism (DMCT). Specially, DMCT is featured by combining dynamic routing and packet active dropping control to avoid additional end-to-end latency. Firstly, we design a specific packet structure to detect network status and aware the deadline of each flow. Secondly, to enhance the precision of routing decision, we further formulate deadline-constrained flow scheduling as a joint minimization problem that considers the deadline of flows, queuing delay, and network status. Then, we propose a multi-agent collaborative routing algorithm to find the solution to the joint minimization problem. Finally, we propose a local-agent deadline-constrained active queue management algorithm for packet active dropping control and routing adjustment. Simulation results show that the proposed DMCT outperforms the state-of-the-art solutions, e.g., graph convolutional network-based deep reinforcement learning (GCNL) and delay-based shortest path first (D-SPF), in terms of one-way round-trip time, drop rate, and queue length.

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Effective management of waiting in queues

Effective management of queues can significantly improve the performance of the organization in general or the unit where it applies in particular. Effective management of queues is most needed when the service is face to face, ie “tangible”, visible and that actually happens in a certain unit. This work aims at identifying the role of several factors (skills, technology, etc.) in the queuing model. The focus of selective surveillance is on all those organizations that have service systems. Service time in the system depends not only on the management of the model but also on some factors which if evaluated (taken into consideration) can significantly improve service results. After a detailed review of the literature, we created the possibility of designing the most effective techniques and methods for gathering information, which was then processed with various computer programs. The proposed model as well as the decision tree is the main results of the presents work. The study has managed to achieve its purpose, as well as to give at the end some recommendations for all those service systems where the queue is applied.

Adaptive event-triggered control for semi-Markovian switching linear systems with process time-varying delays

This paper investigates an adaptive event-triggered control for semi-Markovian jump linear systems with process time-varying delays. For reducing the data transmission amount from the sensor-to-controller, a switching-mode dependent adaptive event-trigger (AET) is proposed wherein a positive auxiliary function is involved. Utilizing the received non-periodical state information, a switching state feedback control law is investigated. By aid of an exponential inequality technique, the controller design method is conveniently presented. The proposed control method not only guarantees the globally exponentially stability of the closed-loop system but also is capable of regulating the decay rate. An approximation approach for the minimal event-triggered time-interval of the AET is given as well. It has been proved that the unwanted Zeno behavior will not exhibit. The proposed control method is employed to a transmission control protocol based active queue management (TCP/AQM) system and the numerical experiment demonstrates its effectiveness.

Fuzzy-Based Active Queue Management Using Precise Fuzzy Modeling and Genetic Algorithm

Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23.

Application of Queuing Theory to Analysis of Waiting Time in the Hospital

The main problem that healthcare workers face in many hospitals is how long it takes patients to receive services. This tendency is becoming more prevalent, posing a threat to healthcare services. The repercussions of keeping people in a long line for medical care can result in a variety of issues, including death. The many server queuing models were utilized to examine the government hospital's service efficiency in this study. Over two weeks, primary data was collected at the hospital using observation and questionnaire methods to find the queuing model that minimizes patient waiting time. The findings revealed that most of the patients were dissatisfied with the hospital's queue management tactics.

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.

Bottlenecks Detection for Improvement of The Emergency Department Process Flow in A Healthcare Facility

The emergency room of a healthcare facility is one of the most complicated and challenging service production processes. People often talk about overcrowding, long waiting times for treatment, lower quality and safety of care, as well as ineffective use of resources. It was discovered that delays between diagnosis and treatment are patients' biggest complaints. Nevertheless, limited studies were found to investigate the causes of delays in the emergency department of a healthcare facility. This study thus aims to investigate how procedures are carried out in the emergency department of a healthcare facility in Klang Valley, particularly to identify the root causes of long waiting times between diagnosis and treatment, as well as to provide some solutions to address the causes. The qualitative method is applied in the present study. Observation and semi-structured interviews with five important internal resource persons/respondents were used to gather the data. The results show that bottlenecks in the Triage Stage, caused by 1) incorrectly classifying a patient's need, 2) lengthy consultation by a Medical Officer, and 3) a longer time to get the review result of a patient's admission, cause delays in the emergency department of the Healthcare Facility. The findings discovered that the Healthcare Facility should improve staff-patient communication, establish a more effective Queue Management System (QMS), and reassign duties to improve emergency department process efficiency as well as increase patient satisfaction. The findings from this study, specifically focusing on the comprehensive root-cause analysis, offer the healthcare facility a significant opportunity to drive transformative improvements in patient care and operational efficiency. By leveraging the insights gained through the study, the management can implement the proposed solutions to reduce delays and proactively address emergency department bottlenecks.

A Simulation-Based Comparative Study of Controlled Delay (Codel) with Random early Detection (Red) for Network Performance Evaluation

The rising need to use the internet for time/delay-sensitive applications with different Quality of Service (QOS) requirements has made network management and control even more challenging. The current congestion avoidance and control mechanisms for Transport Control Protocol (TCP) are insufficient to offer good service in all circumstances. A few decades ago, the TCP successfully regulated Internet congestion control. However, it is already widely acknowledged that TCP has reached its limits and that new congestion control protocols will be required in the near future. This has prompted a significant amount of research on novel congestion control designs that will meet the demands of the future Internet. With widespread public attention and study, the full buffer problem has not gone away, but rather worsened. As a result, there has been a surge in interest in using Active Queue Management (AQM) in Internet routers to minimize queue latency. The effectiveness of a recently developed AQM, Controlled Delay (CoDel) algorithm, designed to work in today’s network setups and can be deployed as a main part of the bufferbloat solution, is evaluated in this research study. CoDel's effectiveness is evaluated by running simulations in ns-3 and comparing its results to that of Random Early Detection (RED), another promising network queue management technique.

Balancing Tradeoffs in Network Queue Management Problem via Forward–Backward Sweeping with Finite Checkpoints

Network queue management can be modelled as an optimal control problem and is aimed at controlling the dropping rate, in which the state and control variables are the instantaneous queue length and the dropping rate, respectively. One way to solve it is by using an indirect method, namely forward–backward sweeping based on the Pontryagin minimum principle to derive control the trajectory of the dropping rate. However, there exists some performance balance issues in the network queue, such as memory usage versus runtime of the algorithm, or dropping rate versus network queue length. Many researchers have exploited symmetry for constrained systems, controllers, and model predictive control problems to achieve an exponential memory reduction and simple, intuitive optimal controllers. In this article, we introduce the integration of the checkpointing method into forward–backward sweeping to address such balancing issues. Specifically, we exploit the revolve algorithm in checkpointing and choose a finite number of checkpoints to reduce the complexity. Both numerical and simulation results in a popular network simulator (ns-2) are provided through two experiments: varying bandwidth and offered load, which solidify our proposal in comparison to other deployed queue management algorithms.

Analysis of queue management in theme parks introducing the fast pass system

Prolonged waits reduce tourist satisfaction and have become a major factor in the theme park experience. How to improve tourist satisfaction is one of the main challenges for theme parks, and the key is to develop an appropriate queue management strategy. We develop a theme park queueing model considering the Fast Pass system, derive the optimal pricing strategy under profit maximization, and analyze the Shanghai Disney theme park as an example. The contribution of this paper is to establish a mathematical model of the queuing of theme parks with heterogeneous tourists and incorporate tourist satisfaction into the objective function. The results show that the equilibrium result of the Fast Pass system under profit maximization can optimize both tourist satisfaction (travel utility) and theme park profit, which is more acceptable to theme park operators.

A Triplex Region - Random Early Detection (TR-RED) Algorithm for Active Queue Management in Internet Routers

A Triplex Region - Random Early Detection (TR-RED) Algorithm for Active Queue Management in Internet Routers

Intelligent queue management of open vSwitch in multi-tenant data center

Intelligent queue management of open vSwitch in multi-tenant data center

Improving TCP performance over a common IoT scenario using the Early Window Tailoring method

The Internet of Things (IoT) is already a reality in homes, transforming the home network into a complex composition of different equipment where each one has its own attributes in terms of TCP protocol variants, transmission speed, reliability and delay. In home network scenarios where IoT devices are used, it is common to have a 802.11 environment with several devices sharing the same Access Point (AP). When TCP is used in this environment its performance is affected by many things, such as wireless link losses, queue saturation on access points, and fairness. To improve the TCP performance in these scenarios, this paper presents a new version of the Early Window Tailoring (EWT) approach. The EWT-IoT do not require any modification in the TCP, and it does not require that all routers in the path use the same Active Queue Management (AQM), the use in the AP is sufficient. To evaluate its performance, comparison tests against drop-tail, RED, ARED and EWA were performed. The EWT-IoT proved to be effective in congestion control, achieving greater performance than the other approaches, reducing the transfer time, having a superior goodput, maintaining a satisfactory fairness, and presenting no losses. Also, the EWT-IoT allowed the existence of a number of flows on average 65.2% better than its best competitor and 71.3% better when no AQM scheme was used.

Efficient congestion control in communications using novel weighted ensemble deep reinforcement learning

In this paper, we introduce Deep Reinforcement Learning (DRL) for congestion control in the Transmission Control Protocol/Internet Protocol (TCP/IP) networks. We propose a weighted ensemble DRL model that combines four DRL models Deep Q-Learning (DQN), Proximal Policy Optimisation (PPO), Deep Deterministic Policy Gradient (DDPG), and Twin Delay DDPG (rlTD3). These four models are designed with varying action spaces for efficient congestion control in an Active Queue Management (AQM) system. The proposed model outperformed single DRL models and established congestion control algorithms like Random Early Detection (RED) in normal and stress testing. The proposed model improved throughput by 4% and delays by 10.52% compared to DQN and 2.92% compared to DDPG. The proposed model has shown promising results in managing congestion in dynamic network environments and handling high-traffic loads.

Congestion-aware delay-guaranteed scheduling and routing with renewal optimization

The emergence of time-critical applications imposes great challenges on traditional best-effort data networks. Such applications demand delay-guaranteed services with different granularity. In this paper, we propose a novel fully distributed approach that provides delay-guaranteed services at the packet level within an autonomous system. Specifically, we adopt priority queues with fixed buffer sizes to provide differentiated services and per-hop delay upper bound and explore path diversity in the network to achieve multiplex gain. Network congestion is a major cause of long delay. To address this issue, a virtual queue manager is deployed at each node in the network to exchange their local queue information with neighbors periodically. The exchanged queue information reflects the congestion status of the neighborhood so the nodes can avoid congested routes in making routing decisions. Given the rich control space including routing and queuing decisions, we aim at maximizing the overall network utility. Due to the randomness caused by network dynamics, we transform the utility maximization problem into renewal optimization which is solved at each node. A delay laxity-based reward function and a weighted queue time cost are designed to characterize each decision. To solve the renewal optimization problem, an algorithm named DSROpt is proposed using an iterative approach. Extensive experiments are conducted to verify the performance of the proposed solution using NS-3. Simulation results show that the proposed solution can guarantee packet-level delay while achieving significant performance improvements in goodput and network utility over the state-of-the-art.

Simulation model of ACO, FLC and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink

<span lang="EN-US">The current work aims to develop a suitable design for control systems as part of a queue management system using the transmission control protocol/and active queue management (TCP/AQM) protocol to handle the expected congestion in the network. The research also aims to make a comparison between the different control methods, including the traditional proportional integral derivative (PID) and the expert fuzzy logic control (FLC), as well as the optimal ant colony optimization (ACO) that is used according to the performance improvement criteria to reach the best values for parameters the traditional controller (kd, ki, k p), where the addition of the performance indicator time-weighted absolute error (ITAE) was adopted. The use of this method without any other optimization algorithm that can be applied to adjust the parameters of the PID to verify the possibility of improving performance and enhance that with experience and to know the level of improvement for this particular system being the subject of the study. The results showed the superiority of the optimal ACO over both the FLC expert and the conventional PID, as well as the superiority of the FLC expert over the traditional PID.</span>