Congestion Control Problem Research Articles

Command filtered backstepping control of multiple transmission control protocol/active queue management networks with user datagram protocol flows

AbstractIn this study, the congestion control problem for multiple transmission control protocol/active queue management (TCP/AQM) networks with user datagram protocol (UDP) flows is investigated. Taking the UDP stream as the unknown interference and the maximum interference is obtained by the minimax theorem. A novel control algorithm is designed for multi‐TCP/AQM networks through backstepping control and command filtered technology. Simulation studies are carried out to verify the effectiveness and feasibility of the proposed method.

Robust H∞ Static Output Feedback Control for TCP/AQM Routers Based on LMI Optimization

This paper proposes a new static output feedback control method to address the congestion control problem in transmission control protocol networks using active queue management routers. Based on linear matrix inequality optimization, this method determines a static output feedback control law to minimize the norm of the transfer function between the controlled queue length of the buffer and the exogenous disturbance affecting the available link bandwidth. A linear matrix inequality formulation is presented as a sufficient condition to guarantee the closed-loop system’s asymptotic stability while maintaining disturbance rejection within a specified level, regardless of round-trip time delays. The proposed robust static output feedback control eliminates the need to measure or estimate all system states, thus simplifying practical implementation. The effectiveness of the proposed design method is demonstrated by applying it in a practical process, as illustrated through a numerical example.

PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial Networks

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6 G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transport Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PEPesc</i> which has two distinctive features. First, it features <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retransmission-free</i> loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iperf</i> and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

Deep-Reinforcement-Learning-Aided Loss-Tolerant Congestion Control for 6LoWPAN Networks

The IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) protocol stack is a promising solution to connect Wireless Sensor Networks (WSNs) with the Internet for realizing a ubiquitous network interconnection of all things. However, 6LoWPAN networks face a critical challenge to control congestion caused by the burst of data traffic from wireless sensors. Packet loss will occur when buffer overflows. This paper focuses on the loss-tolerant congestion control problem in 6LoWPAN networks, which has not been addressed in existing works. We formulate the congestion control problem as a non-cooperative Markov game framework and conceive a novel congestion control method, namely Deep reinforcement learning aided Loss-tolerant Congestion Control (DLCC), to alleviate congestion while maintaining a tolerable packet loss imposed by buffer overflow. The proposed DLCC employs Deep Reinforcement Learning (DRL) to solve the curse of state dimensionality, while packet loss constraints are handled by utilizing Lagrange multipliers to integrate the reward with loss constraints. By dynamically updating Lagrange multipliers in an online learning procedure, DLCC finds the optimal congestion control policy. Our simulation results show that DLCC maintains the packet loss rate below the tolerable threshold at the presence of congestion. In contrast to existing hybrid congestion control algorithms, the proposed DLCC algorithm is more energy-efficient and provides higher throughput, lower average delay and better fairness.

Review of random early detection optimization for congestion control in vehicular ad hoc networks

&lt;span&gt;The research area of “optimization of random early detection for congestion control in vehicular ad hoc networks (VANETS)” is emerging as a prominent area of research in internet, mobile and communication networks. The paper would draw the attention of development of VANETS in a wider context of internet and communications. It touches upon the problem of congestion that needs to be controlled or avoided for smooth performance of the network. It studies further to come up with methods of control or optimize. An update on these methods will be included. The concept of random early detection (RED) is included and studied in such a way the parameters are identified for defining the optimization problem for congestion control problem (CCP) in VANETS. Current trends of optimization of RED in CCP of VANETS will be reviewed, hence an outlook is created for the problem to be investigated with a final conclusion on the potential way forward for the research work.&lt;/span&gt;

Weighted Sum Metrics – Based Load Balancing RPL Objective Function for IoT

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.

Avoiding Congestion for Coap Burst Traffic

Congestion is an important issue in Internet of Things (IoT) networks with constrained devices and a growing number of applications. This paper investigated the problem of congestion control for burst traffic in such networks. We highlight the shortcomings of the current constrained application protocol (CoAP) in its inability to support burst traffic and rate control. Subsequently, we propose an analytical model for CoAP burst traffic and a new rate-control algorithm for CoAP to avoid congestion. A CoAP sender increases or decreases the transmission rate depending on the congestion detection. Using simulations, we compared the performance of the proposed algorithm with the current CoAP in various traffic scenarios. Experimental results show that the proposed algorithm is efficient for burst traffic and provides better performance in terms of delay, throughput, retransmission, packet duplication, and packet loss compared to CoAP.

End-to-End TCP Congestion Control as a Classification Problem

The traditional rule-based congestion control algorithms cannot set congestion window size flexibly, resulting in the inadaptation of the dynamic networks. This article presents a method to model end-to-end TCP congestion control problem using classification techniques. The network status parameters as the input and the type of network status as the output are defined through the analysis of some existing congestion control algorithms. NewReno, CUBIC, and Compound are used as feedback to produce the training data of the XGBoost classifier. The experimental results show that the classifier effectively shapes the strategies of three outstanding congestion control algorithms and almost achieves the same throughput, delay, and fairness. The proposed method makes the congestion control algorithm able to learn from data produced by network.

Optimal Joint Power and Rate Adaptation for Awareness and Congestion Control in Vehicular Networks

To support inter-vehicular applications, vehicles broadcast beacons with information about their state. Congestion may occur when the load on the channel due to beaconing can prevent the transmission of other types of messages. In this paper, we propose a distributed algorithm for optimal joint adaptation of transmit power and beaconing rate for congestion and awareness control. Our approach is based on a network utility formulation of the congestion control problem, which allows us to induce a desired fairness notion and set different priorities for vehicles. We formulate the general problem but, since it is not convex, we assume Rayleigh fading and derive an algorithm, called PRAIOS, that solves the optimization problem in a decentralized way with convergence guarantees. Our results, validated with realistic simulations in both static and dynamic scenarios and compared with other proposals, show that PRAIOS quickly converges to close to optimal allocations, while keeping the maximum beaconing load at the desired level. They also show that it can control the load when the fading is not Rayleigh. Applications can dynamically set their requirements as constraints, that are enforced by the algorithm while complying with the maximum load, which allows seamless integration of operational requirements into the control framework.

A Novel Congestion Controller With Prescribed Settling Time for TCP/AQM Network System

This paper studies the congestion control problem with prescribed settling time of nonlinear TCP/AQM network system. With the continuous improvement of users' requirements for network quality of service, it is a challenging task to quickly recover from the network congestion state to the normal network state. In the framework of backstepping, this paper studies how to realize congestion control according to the network state. The basic idea is to introduce a new nonautonomous differential equation to compensate the shortcomings of the existing arbitrary convergence time controller, and integrate it into the design of congestion controller to realize free-will arbitrary time congestion control. In order to reduce the packet loss rate and avoid reducing network efficiency caused by a large number of packet retransmissions, a finite-time performance function is introduced in the design process. The results show that using the proposed congestion mechanism, the congestion control problem with prescribed settling time can be solved by a nonpiecewise continuous arbitrary convergence time control law. The arbitrary time convergence of the system state is proved theoretically. Finally, the effectiveness of the proposed controller is verified by simulations and compared with related work.

Two-Facet Scalable Cooperative Optimization of Multi-Agent Systems in the Networked Environment

Cooperatively optimizing a vast number of agents over a large-scale network faces unprecedented scalability challenges. The scalability of existing optimization algorithms is limited by either the agent population size or the network dimension. As a radical improvement, this article, for the first time, constructs a two-facet scalable distributed optimization framework. This novel framework distributes the computing load among agents (scalability w.r.t. population size) and enables each agent to only consider partial network constraints in its primal variable updates (scalability w.r.t. network dimension). We first develop a systemic network dimension reduction technique to virtually cluster the agents and lower the dimension of network-induced constraints and then constitute a novel shrunken primal-multi-dual subgradient (SPMDS) algorithm based on the reduced-dimension network for strongly coupled convex optimization problems. Optimality and convergence of the proposed distributed optimization framework are rigorously proved. The SPMDS-based optimization framework is free of agent-to-agent or cluster-to-cluster communication. Besides, the proposed method can achieve significant floating-point operations (FLOPs) reduction compared with full-dimension cases. The efficiency and efficacy of the proposed approaches are demonstrated, in comparison with benchmark methods, through simulations of electric vehicle charging control problems and traffic congestion control problems.

Multiple bottleneck topology TCP/AQM switching network congestion control with input saturation and prescribed performance

This paper studies the congestion control problem of multiple bottleneck networks. First, this paper regards the routing nodes of the network as multi-agents, and considers its own dynamic process. For the first time, the time-varying characteristics of the number of sessions in the network transmission process and the mutual influence between nodes are considered into the model, and a queue utilization rate switching model is established to describe the dynamics of a single agent. Then, considering the boundedness of the control input, combined with the prescribed performance technique, two congestion controllers are designed by using the backstepping method according to whether the controller depends on the number of sessions. The designed congestion controllers improve the robustness of the TCP/AQM network system in the case of frequent and time-varying sessions in multiple bottleneck networks. In addition, the designed controllers can stabilize different routing nodes of multiple bottleneck networks under different queue lengths, thereby improving the utilization efficiency of the network. Finally, the effectiveness and superiority of the designed controllers are verified by simulation.

Congestion-Aware Rideshare Dispatch for Shared Autonomous Electric Vehicle Fleets

The problem of traffic congestion caused by the fast-growing travel demands has been getting serious in urban areas. Meanwhile, the future of urban mobility has been foreseen as being electric, shared, and autonomous. Accordingly, the routing and charging strategies for fleets of shared autonomous electric vehicles (SAEVs) need to be carefully addressed to cope with the characteristics of the rideshare service operation of the SAEV fleets. In the literature, much work has been done to develop various traffic control strategies for alleviating the problem in urban traffic congestion. However, little research has proposed effective solutions that integrate the route of charging strategies for SAEV fleets with the urban traffic congestion problem. In this regard, this work presents an integrated framework that tackles the route and charging of SAEV fleets as well as the urban traffic congestion prevention issues. Notably, our contribution in this work not only proposes a joint solution for the problems of the urban traffic congestion control and rideshare dispatch of SAEV fleets, but also fills the gap of the routing and charging strategies for mixed privately owned EVs (PEV) and SAEV fleets in the literature. A general optimization framework is formulated, and effective heuristics are proposed to tackle the above-mentioned problems in this work. The feasibility and effectiveness of the proposed algorithms were evaluated through four different scenarios in the simulation. After applying the proposed algorithms, the traffic volumes of the oversaturated main arterial road were diverted to other less busy road sections, and the traveling times of EV passengers were decreased by 28% during peak periods. The simulation results reveal that the proposed algorithms not only provide a practical solution to prevent the problem in urban traffic congestion during rush hours, but also shorten the travel times of EV passengers effectively.

Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model

Cloud model and sensor network are the research hotspots in recent years. This paper proposes a congestion and rate control strategy for wireless sensor networks based on cloud model. It adjusts the input rate of nodes based on cloud model through node congestion detection. Aiming at the problem of network congestion control, two congestion adjustment algorithms based on red are improved. Congestion threshold and congestion degree are used as the basis of packet transmission rate adjustment to realize network support plug control. In this paper, the congestion control strategy NP starts to alleviate congestion at about 40 s and controls the packet loss rate at about 116 packets/s, which is 45.3% lower than the multipath congestion control strategy and is more stable. However, when β = 0 , the packet loss rate of the double congestion threshold algorithm is 20% lower than that of the single congestion threshold algorithm. The reason is that the double congestion threshold algorithm allows a stronger source rate control mechanism earlier than the single congestion threshold algorithm a large number of packets are lost in a point, but this also makes the network performance relatively low.

Observer‐based finite‐time congestion control for TCP/AQM network system with time‐varying delays and UDP flows

AbstractIn this paper, the problem of observer‐based finite‐time congestion control for transmission control protocol (TCP)/active queue management (AQM) network systems with time‐varying delay and nonresponsive flows such as UDP is studied. The congestion control problem of TCP/AQM network system with time delay and external disturbances observer‐based finite‐time control is solved for the first time, and a theorem proved by the Lyapunov function of TCP/AQM network systems finite‐time bounded is given to ensure that the closed‐loop system is finite‐time bounded. Finally, the control gain and observer gain are obtained by linear matrix inequality (LMI) technique. Simulation results show the effectiveness and superiority of the proposed method.

An New Efficient Cluster Based Detection Mechanisms for Distributed Denial of Services (DDoS) Attacks

Cluster aggregation of statistical anomaly detection is a mechanism for defending against denial of service attack (dos) and distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestioncontrol problem; because most of the congestion is occurred in the malicious hosts not follow the normal endto- end congestion control. Upstream routers are also notified to drop such packets in order that the router’s resources are used to route legitimate traffic hence term cluster aggregation. If the victim suspects that the cluster aggregations are solved by most of the clients, it increases the complexity of the cluster aggregation. This aggregation solving technique allows the traversal of the attack traffic throughout the intermediate routers before reaching the destination. In this proposal, the aggregation solving mechanism is cluster aggregation to the core routers rather than having at the victim. The router based cluster aggregation mechanism checks the host system whether it is legitimate or not by providing a aggregation to be solved by the suspected host.

Robust control of congestion in computer networks: An adaptive fractional-order approach

In this study, the congestion problem of computer networks is solved by introducing an adaptive fractional-order controller. The controller is designed to enhance the efficiency of nonlinear transmission control protocol/active queue management (TCP/AQM) in these networks. External disturbances can perturb the data transmission. Moreover, factors such as undetermined link capacity can affect the congestion control problem. Hence, the proposed controller should guarantee robustness against disturbances and uncertainties. Besides, small elapsed time to reach the desired queue length as well as convergence of tracking error to zero are essential in queue management. These objectives are fulfilled by designing a fractional-order controller. High tracking capability and robustness make the controller an effective method for TCP/AQM networks. Furthermore, asymptotic stability of the network is precisely proven based on the fractional-order Lyapunov lemma. A variety of simulations are performed to examine capability of the proposed method confronting disturbances and uncertainties while providing a fast and stable response with zero tracking error. The results are also compared with the results of two recently developed control approaches for congestion problem, using performance indices, which confirm the efficiency and superiority of the introduced controller.

Research on SDN Congestion Control Based on Reinforcement Learning

Aiming at the congestion control problem of SDN, this paper designs a network congestion control scheme based on Q-learning algorithm of reinforcement Learning. According to the usage of link bandwidth in the network, the algorithm adjusts the transmission rate of the source data stream continuously, and finally obtains the optimal data stream allocation scheme. Experimental simulation evaluates the performance of the algorithm, the proposed control algorithm based on reinforcement learning congestion, can effectively improve the link utilization, reduce network congestion. The experimental results show that the congestion control algorithm proposed in this paper can allocate the optimal rate for each data stream, reduce network congestion, and has better performance than the traditional SDN algorithm.

Research on the Traffic Flow Control of Urban Occasional Congestion Based on Catastrophe Theory

Aimed at the problem of occasional congestion control, the cusp catastrophe theory is used to establish the catastrophe model of the urban road system under occasional congestion, finding breakpoints and analyzing stability after urban road system catastrophes by constructing the energetic function; based on the catastrophe characteristics of the urban road system, the feasibility method of congestion control is discussed. The results show that the control method of traffic flow based on catastrophe characteristics of the urban road system can effectively improve the efficiency of the road system in theory. Finally, the applicability of the control method based on catastrophe characteristics is analyzed by examples under different occasional congestion situations.

Global asymptotic consensus of multi-agent internet congestion control system.

This paper considers the Internet congestion control and consensus problems of a multi-agent system composed of a congested node, some aggregation nodes and their following nodes. First, a novel multi-agent Internet congestion control system with both discrete and distributed delays is proposed. Second, the stability and Hopf bifurcation of the congested node are discussed. Subsequently, a simple error controller designed for aggregation nodes, some necessary and sufficient conditions have been established to solve the bifurcation consensus problem between aggregating nodes and the congested node. Bifurcation consensus of aggregation nodes and their following nodes are also obtained through consensus protocols which based on a novel predictive controller. So far, the bifurcation asymptotically consensus of multi-agent Internet congestion control system is achieved. It is worth noting that conditions for global stability of system can be obtained by selecting appropriate control parameters, and conclusions of global asymptotic stability consensus is given to draw conditions for avoiding network congestion in the whole system. Finally, numerical examples illustrated the correctness and validity of the main results.