Queue Management Scheme Research Articles

Enhancing queuing theory realism: analysis of reneging behavior impact on M/M/1 drive-thru service system

Individuals seeking services through drive-thru queuing and waiting, anticipate speedy service. However, long queues often prompt customers' impatience, leading to reneging and the early departure from a queue due to perceived excessive waiting. Reneging behavior is often disregarded in queuing analysis, undermining the accuracy and management of queues, resulting in a misrepresentation of real-world scenarios. This study delves into an extensive analysis of a 123-h M/M/1 drive-thru service queue, examining reneging probability and its correlation with customers’ sensitivity to arrival times. Findings reveal that the time between reneging events follows an Exponential distribution, challenging the assumption of a Poisson distribution for reneging rates. The traditional calculation of queue intensity neglects reneging and service abandonment, necessitating modifications to core queuing theory equations for practicality. Integrating reneging behavior enhances the theory’s applicability to real-life queuing dilemmas, improving accuracy and interpretability for effective queue management analysis and strategies, thus directly benefiting business management of queuing systems.

Active Queue Management in L4S with Asynchronous Advantage Actor-Critic: A FreeBSD Networking Stack Perspective

Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories.

An incentive strategy for the retention of impatient passengers in ride-sourcing markets

A prominent problem plagued the ride-sourcing service providers is the instantaneous real-time demand–supply imbalance. This entails a terrible user experience and causes adverse sustainable results such as customer churn for the platform, which will damage both drivers’ income and the platform revenue. To reduce passenger cancellation caused by extended wait times, we propose an incentive-based queue management strategy —a discount method— to enhance the waiting experience and encourage passengers to stay in a post-peak period . We formulate the waiting process for a ride-sourcing service as an M/M/c+M queue system with impatient passengers, and model passengers’ reneging behaviors by characterizing the impact of discounted trip fare and updated queue information on their travel utilities. Based on such a behavior model, we can analyze the effect of the discount strategy on the queueing process, which allows us to maximize the platform’s profit growth by tailoring the discount strategy. Our result shows that the discount strategy can effectively increase the platform’s profit. Under the specific condition of the discount strategy, the earlier the strategy is implemented in the post-peak period, the higher platform profit can be achieved. Moreover, we explore the impact of queueing system parameters on the performance of the discount strategy and gain some interesting insights.

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.

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.

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.

TCP-Puerto-Londero: A New Approach for End-to-End Queue Length Control

This paper presents a new TCP protocol called TCPPuerto-Londero, which makes dynamic tuning in the congestion window (cwnd) by means of adaptive control theory aiming to keep stable and small the queue length in the bottleneck link located on the path from source to destination. This adaptive control loop has the relative delay in the forward path measured as an input and the cwnd value as an output. Thus, unlike classic TCP protocols, TCP-Puerto-Londero does not use Round Trip Time (RTT) information. Also, unlike classic Active Queue Management (AQM) strategies and Explicit Congestion Notification (ECN) based protocols, TCP-Puerto-Londero employs end-to-end queue management without the need for ECN resources. Moreover, TCP-Puerto-Londero also aims to attend to the new challenges of the Industrial Internet of Things (IIoT). Its performance has been tested in a Dumbbell network topology shared by TCP and UDP-like Networked Control Systems (NCS) flows. Therefore, TCP-Puerto-Londero performance has been compared with ECN-based protocols like DCTCP, E-DCTCP, TCP-Jersey, and ENCN. Furthermore, this paper employs an approach for modeling, analysis, simulation, and verification of the communication network and NCS employing UPPAAL simulation software tool, where all network constituents (transmitters, channels, routers, receivers, controllers, and plants) were modeled employing timed automata, simplifying a formal verification of the complete studied system. Simulations and statistical verification indicate that even though utilizing fewer resources (as it does not require the AQM/ECN information) TCP-Puerto-Londero overcomes TCP-Jersey, DCTCP, and EDCTCP with regard to throughput and fairness for TCP flows. Also, TCP-Puerto-Londero flows are capable to keep the queue length more stable and smaller than other protocols that were compared, and consequently, it reduces the impact in NCS-UDPlike flows sharing the same network, whose performance was measured employing the Integral Time Absolute Error (ITAE), which is a desirable feature for Industrial IoT.

Waiting Time in a General Active Queue Management Scheme

Waiting Time in a General Active Queue Management Scheme

Transmission Control Protocol Analysis Using NS3

The NS-3 Test Framework provides a focus on multidisciplinary development and high-level design, and is now being used by various experts around the world. Surprisingly, its Data Collection Protocol (DCP) implementation is late and is not planned to be used as a reference point for Transmission Control Protocol (TCP), focused research, where the Herald has provided NS-3 to accept NS-2 submissions. The latest-best configuration consists of a number of PCs connected to each other by two sweets and switches. As a result of the overlapping character, being a significant number of PCs, channels can create a complex problem. If a case of sensitivity is sent to the TCP bundles, each word flow should be taken with the appropriate treatment when there is a deadline. Drop-tail is a common scheme for using the power of measurement, and with this provision, the feeling cannot be limited to the illumination of long lines, which increases the delay in these methods. As a result, the flow efficiency decreases. The Board Active Queue Management (AQM) is largely oblivious to the fact that conspiracy and sensitivity are not the basic mechanisms that must be considered in solving this major problem. Another area to look for is depression, and the range of serious problems. In this work, a framework was developed based on drop- tail and different active queue management schemes such as DLP, Drop-Tail delivers, Packet First in First Out (PFIFO), Random Early Detection (RED) and Code Exploration Introduction. The evaluation is performed using an open-framework framework (NS-3) assessment framework. In the end, the results of the review suggest that Code and RED, a unique line of management figures, have shown much needed performance.

МОДЕЛІ ІЄРАРХІЧНОЇ БАГАТОАГЕНТНОЇ СИСТЕМИ ДЛЯ ВИКОНАННЯ БІЗНЕС-ПРОЦЕСІВ

Purpose. Developing a set of models which formally describe the operation environment of the organizational and technical system and the interaction of software agents of different roles in performing business operations. The pres-ence of such a set of models allows you to create information technology for monitoring and routing business processes and reduce costs for business operations. Methodology. The research methods are based on systems analysis methods. Findings. A set of models of hierarchical multi-agent system for business processes has been developed. The complex comprises a static description of the operation environment of the organizational and technical system, where there are models of business process, business operation and its components. Originality. Organization of total monitoring of the operation environment of the organizational and technical system, i.e. the current state of the processes of processing applications, queues and workstations requires the development of static and dynamic business process models. Based on the static description, a model of the dynamics of business processes promoting throughout many workstations has been developed. This makes possible to monitor the status of workstations, queues and applications for business opera-tions. In particular, a formal description of the software agent, its competencies and a model of interaction of three-level agents which perform the functions of business operation executors, monitors and dispatchers has been developed. Having such tools, the second important aspect is the development of a universal hierarchical structure of the multi-agent system, in which different agents perform the roles of performers, monitors and dispatchers. Such a structure should include the regulation of the agent functions, models of agents interaction at all three levels, ways of agents-people communication. Practical value. Tests of the monitoring and scheduling system in different conditions (electronic document management, manufacturing company, human resources management department) showed a decrease in time of business operations, losses on waiting and increase in rhythm of business processes. The results of the work has enabled the development of a queue management strategy, which has showed a reduction in time of operations and a more balanced workload.

Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

On the validity of numerical simulations for control-theoretic AQM schemes in computer networks

Active Queue Management (AQM) schemes complement Transmission Control Protocol (TCP) congestion-control mechanisms in reducing queuing delay, packet loss, and in improving network throughput. This paper presents a detailed mathematical review of the control-theoretic AQM feedback structure. We highlight and clarify the misconceptions regarding its usage, and furthermore, we verify and validate the numerical time-domain and event-domain simulations of existing AQM schemes using the MATLAB-Simulink platform and the NS-3 simulator platform respectively. Finally, conclusions and some future research directions are briefly discussed.

Stability of SDE-LJN System in the Internet to Mitigate Constant-Rate DDoS Attacks

The Internet is nowadays suffering dramatically serious attacks, with the distributed denial of service (DDoS) attacks being the representative and dominant ones. It is seen that, to stabilize the buffer queue length around a given target under DDoS attacks in the relevant routes is vitally important and helpful to mitigate the attacks and to improve the quality of service (QoS) for normal users. In the current paper, a stochastic queue dynamic model with L e ´ vy jump noise, which is affected by the continuous Brownian motion and the discontinuous Poisson process, is worked out to develop a novel and accurate mathematical framework for the stability of a route queue that deals with constant-rate DDoS attacks. This article proposes a security defensive mechanism in the network for solving the network collapse that can possibly be caused by DDoS attacks, otherwise. Particularly, based on the formulation of a stochastic queue dynamic with L e ´ vy jump noise, the mechanism that characterizes the behavior of the queue at routers is presented for stabilizing the queue length under constant-rate DDoS attacks. By applying the stochastic control theory into analyzing the performance of queue dynamic under constant-rate DDoS attacks, some explicit conditions are established under which the instantaneous queue length converges to any given target in a route. Simulation results demonstrate the satisfaction of the proposed defense mechanism with sharp contrast to the state of the art active queue management (AQM) schemes.

Intelligent Active Queue Management for Stabilized QoS Guarantees in 5G Mobile Networks

The fifth-generation (5G) new radio standard defines various functions for forwarding treatments under differentiated quality of services requirements. In particular, a gNB base station helps combat overbuffering due to massive packet flows from associated mobile devices. Several active queue management (AQM) schemes have been proposed previously, with controlled delay (CoDel) proving to be an efficient method that can properly handle excessive buffering, that is called bufferbloat. This article proposes a novel and enhanced AQM scheme based on CoDel for gNB that guarantees low queuing delays, minimizes packet drop rate, and furthermore stabilizes the built-in queue. According to data-intensive performance evaluation results, the proposed scheme verifies that the queuing delay for served packets are decreased and supports ultrareliable low latency communication applications. Especially, the proposed algorithm achieves the packet drop rates and queuing delays tradeoff as [ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/V), O(V)] where V is a tradeoff parameter; providing efficient control decisions to improve performance in terms of time-average packet drop rate minimization subject to queue stability on demand in 5G mobile system.

Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets

Formal modeling is considered one of the fundamental phases in the design of network algorithms, including Active Queue Management (AQM) schemes. This article focuses on modeling with Petri nets (PNs) a new scheme of AQM. This innovative AQM is based on a discrete dynamical model of random early detection (RED) for controlling bifurcations and chaos in Internet congestion control. It incorporates new parameters (α,β) that make possible better stability control over oscillations of an average queue length (AQL) at the router. The PN is validated through the matrix equation approach, reachability tree, and invariant analysis. The correctness is validated through the key properties of reachability, boundedness, reversibility, deadlock, and liveness.

SQM-LRU: A Harmony Dual-Queue Management Algorithm to Control Non-Responsive LTF Flow and Achieve Service Differentiation.

The increase in network applications diversity and different service quality requirements lead to service differentiation, making it more important than ever. In Wide Area Network (WAN), the non-responsive Long-Term Fast (LTF) flows are the main contributors to network congestion. Therefore, detecting and suppressing non-responsive LTF flows represent one of the key points for providing data transmission with controllable delay and service differentiation. However, the existing single-queue management algorithms are designed to serve only a small number of applications with similar requirements (low latency, high throughput, etc.). The lack of mechanisms to distinguish different traffic makes it difficult to implement differentiated services. This paper proposes an active queue management scheme, namely, SQM-LRU, which realizes service differentiation based on Shadow Queue (SQ) and improved Least-Recently-Used (LRU) strategy. The algorithm consists of three essential components: First, the flow detection module is based on the SQ and improved LRU. This module is used to detect non-responsive LTF flows. Second, different flows will be put into corresponding high or low priority sub-queues depending on the flow detection results. Third, the dual-queue adopts CoDel and RED, respectively, to manage packets. SQM-LRU intends to satisfy the stringent delay requirements of responsive flow while maximizing the throughput of non-responsive LTF flow. Our simulation results show that SQM-LRU outperforms traditional solutions with significant improvement in flow detection and reduces the delay, jitter, and Flow Completion Time (FCT) of responsive flow. As a result, it reduced the FCT by up to 50% and attained 95% of the link utilization. Additionally, the low overhead and the operations incur O(1) cost per packet, making it practical for the real network.

SARED: A Self-Adaptive Active Queue Management Scheme for Improving Quality of Service in Network Systems

Considering the phenomenal growth of network systems, congestion remains a threat to the quality of service provided in such systems, hence, research on congestion control is still relevant. Internet research community regards Active Queue Management (AQM) as an effective approach to address congestion in network systems. Most of the existing AQM schemes possess static drop patterns and lack self-adaptation mechanism, as such don’t work well for networks where traffic load fluctuates. This paper proposes Self-Adaptive Random Early Detection (SARED) scheme which smartly adapts its drop pattern based on current network’s traffic load in order to maintain better and stable performance. In light to moderate load conditions, SARED operates in nonlinear modes in order to maximize utilization and throughput, while in high load condition, it switches to linear mode in order to avoid forced drops and congestion. Experiments conducted have revealed that regardless of traffic load’s condition, SARED provides optimal performance.

Optimizing Information Freshness Through Computation–Transmission Tradeoff and Queue Management in Edge Computing

Edge computing applications typically require generated data to be preprocessed at the source and then transmitted to an edge server. In such cases, transmission time and preprocessing time are coupled, yielding a tradeoff between them to achieve the targeted objective. This paper presents analysis of such a system with the objective of optimizing freshness of received data at the edge server. We model this system as two queues in tandem whose service times are independent but the transmission service time is monotonically dependent on the computation service time in mean value. This dependence captures the natural decrease in transmission time due to lower offloaded computation. We analyze various queue management schemes in this tandem queue where the compute queue has a single server, Poisson packet arrivals, general independent service and no extra buffer to save incoming packets. The transmit queue has a single server receiving packets from the compute queue with memoryless service time. We consider the transmit queue in two forms: (i) No data buffer and (ii) One unit data buffer and last come first serve with discarding. We analyze various non-preemptive as well as preemptive cases. We perform stationary distribution analysis and obtain closed form expressions for average age of information (AoI) and average peak AoI. Our numerical results illustrate analytical findings on how computation and transmission times could be traded off to optimize AoI and reveal a consequent tradeoff between average AoI and average peak AoI.

On the Transient Queue with the Dropping Function.

We deal with a queueing system, in which arriving packets are being dropped with the probability depending on the queue size. Such a scheme is used in several active queue management schemes proposed for Internet routers. In this paper, we derive and analyze a selected transient characteristic of the model, i.e., the probability that in a given time interval the queue size is kept under a predefined level. As the main purpose of the discussed queueing scheme is to maintain the queue size low, this is a natural characteristic to study. In addition to that, the average time to reach a given level is derived. Theoretical results for both characteristics are accompanied by numerical examples. Among other things, they demonstrate that the transient behavior of the queue may vary significantly with the shape of the dropping function, even if the steady-state performance remains unaltered.

Interval type-2 and type-1 Fuzzy Logic Controllers for congestion avoidance in internet routers

Active queue management (AQM) scheme is an important research area in congestion control. The AQM mechanism attains the congestion controller by regularize the length of queue from evaluation or dropping the packets at the moderate routers. In this paper the interval type-2 and type-1 fuzzy logic controller are designed to desist the congestion in the networks. Several evolutionary optimization algorithms are used to select the parameters of two controllers as particle swarm optimization (PSO), social spider optimization (SSO) and ant colony Optimization (ACO). The simulation of linearized model of TCP/AQM are introduced in MATLAB (R2018a). Results of the PID like fuzzy type-1controller is compared with PID like interval type-2 fuzzy controller (IT2-FLC) based on optimization algorithms in each controller and shows that IT2-FLC with SSO has been given the best result.