Congestion tracking control for multi-router TCP/AQM network based on integral backstepping

Active queue management (AQM) has been widely used for congestion avoidance in transmission control protocol (TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we propose a novel adaptive AQM(active queue management ) algorithm for TCP network to cope with the network delay and time-varying network parameters based on RBF-PID controller, the RBF(redial basis function) neural network is employed to automatically tune the controller's parameters according to link capacity, traffic load and transmitting time-delay, which makes the presented AQM algorithm perform well for a wide-range of network conditions. The simulation results have shown that the RBF-PID-based AQM is feasible and efficient, and yields superior performance with faster transient time and better adaptive ability compared to the proportional-integral (PI)-based AQM.

For the congestion control of the transmission control protocol (TCP) networks in presence of input saturation and the lumped uncertainty, an active queue management algorithm is proposed based on adaptive sliding mode control. One adaptive law is designed to estimate the lumped uncertainty such that the need for knowing the bound in advance is eliminated. The other adaptive law is proposed to ensure the system stability considering the TCP networks subjected to input saturation. Using these adaptive laws, an adaptive sliding mode controller is presented to guarantee asymptotic convergence and robustness against the lumped uncertainty. Numerical simulations are performed to show that the proposed scheme is effective in various network scenarios.

Review of Queue Management in Packet Switched Networks

The rapid evolution of computer networks, increase in the number of Internet users, and popularity of multimedia applications have exacerbated the congestion control problem. Congestion control is a key factor in ensuring network stability and robustness. When the underlying network and flow information are unknown, the transmission control protocol (TCP) must increase or reduce the size of the congestion window to adjust to the changes of traffic in the Internet Protocol (IP) network. However, it is possible that a software-defined approach can relieve the network congestion problem more efficiently. This approach has the characteristic of centralized control and can obtain a global topology for unified network management. In this paper, we propose a software-defined congestion control (SDCC) algorithm for an IP network. We consider the difference between TCP and the user datagram protocol (UDP) and propose a new method to judge node congestion. We initially apply the congestion control mechanism in the congested nodes and then optimize the link utilization to control network congestion.

As the Internet becomes increasingly heterogeneous, the issue of congestion control becomes ever more important. And the queue length and end-to-end (congestion) delays are some of the important things in term of congestion avoidance and control mechanisms. In this research we continued to study the performances of the New-Additive Increase Multiplicative Decrease (AIMD) algorithm as one of the core protocols for TCP congestion avoidance and control mechanism, we want now to evaluate the effect of using the New-AIMD algorithm to measure the queue length and end-to-end delays and we will use the NCTUns simulator to get the results after make the modification of the mechanism. And we will use the Drop tail mechanism as Active Queue Management (AQM) in the bottleneck router. After implementation of our new approach with different number of flows, we will measure the delay for two types of delays (queuing delay and end-to-end delay), we expect the delay will be less with using our mechanism comparing with the mechanism in the previous study. Now and after got this results as low delay for bottleneck link case, we know the New-AIMD mechanism work as well under the network condition in the experiments.

Stable Queue Management in communication networks

Congestion is an important issue which researchers focus on in the Transmission Control Protocol (TCP) network environment. To keep the stability of the whole network, congestion control algorithms have been extensively studied. Queue management method employed by the routers is one of the important issues in the congestion control study. Active Queue Management (AQM) has been proposed as a router-based mechanism for early detection of congestion inside the network. In this study, we are comparing AQM two popular queue management methods, Random Early Detection (RED) and droptail, in different aspects, such as throughput and fairness Index. The comparison results indicate RED performed slightly better with higher throughput and higher fairness Index than droptail. Simulation is done by using Network Simulator (NS2) and the graphs are drawn using X- graph.

This study is concerned with the design of active queue management (AQM) subjected to quantisation errors. First, the authors will show here that quantisation errors exist when AQM experiences transmission control protocol (TCP) during the congestion. Second, a TCP/AQM model that takes into account quantisation errors is proposed – the considered quantisers are dynamic and have a scale parameter. Based on this model, stabilisation will be analysed in which the controller and the scaling parameters are obtained using the Lyapunov–Krassovskii functional method. Finally, Matlab simulation results are displayed to show the effectiveness of the proposed method.

Transmission Control Protocol (TCP) used multipath for achievement of data instantaneously to improve its performance.Anyhow, the preceding TCP protocol in the Internet of Things (IoT) network experienced a struggle to transmit a superior number of subflows.The Bottleneck Bandwidth and Round-trip propagation time (BBR) generate a network path model by assessing the available bottleneck bandwidth and the minimal Round-Trip Time (RTT) to achieve the desired delivery rate, consequently reducing the latency.However, some research studies have indicated the presence of significant fairness issues related to RTT in the BBR algorithm.To overcome this issue, we proposed a New Enhanced Congestion Control (CC) mechanism referred to as Bottleneck Bandwidth and Round-trip propagation time (BBR-NEnh) which is cross-layer-based efficient congestion control in a 5G IoT heterogeneous network.The proposed work encloses three sequential processes including grid-based network construction, improved two-factor clustering, and Hybrid TCP congestion control.Initially, we perform network construction by grid-based to improve the information transfer rate and increase connectivity among devices.After that, IoT devices are clustered based on two factors congestion and buffer space using the Improved K-Medoids algorithm (IK-Med) by the edge server.Further, to enhance the efficiency of TCP, the proposed research adopts a BBR-NEnh by optimizing the congestion control parameters using the Adaptive Tunicate Swarm Optimization (ATSO) algorithm.Finally, the dynamic handover and adjustment of windows are performed based on the Fast Adaptation Technique (FAdT) which can send the data quickly without congestion.The implementation of the proposed research is carried out using NS-3.26 and the performance of the proposed BBR-NEnh model is evaluated using a variety of performance metrics, including goodput, delay, packet loss, queue length, transmission rate, and throughput.The performance of the proposed work is compared with IMPRTT (Improved RTT) and CW-IoT (Congestion Window Algorithm for the Internet of Things).The average throughput, goodput, and packet loss of the proposed work for 1500 packets size are 4, 0.9, and 10.3, and the average delay, transmission rate, and queue length over 150 seconds are 0.004, 2500, and 36.1, demonstrating that the proposed work outperforms earlier work like IMPRTT and CW-IoT.

TCP (Transmission Control Protocol) is the most used transport protocol for wired and wireless networks. It provides many services (reliability, end to end delivery ...) to the applications running over the Internet, but to be able to manage traffics with a huge quantity of data, TCP must have robust congestion control mechanisms. Many researchers have agreed that despite the existence of some congestion control algorithms, TCP still suffers from disappointing performances for short and long flows. For that, researches are still launched by the network community in order to have the suitable mechanism ensuring fair and efficient bandwidth allocation. The works already carried out in this subject has elaborated several congestion control mechanisms. In this paper, we discuss, identify, analyze and compare the behavior of some congestion control mechanisms under congested wireless mech networks, in order to identify their advantages and their respective limits. For the simulation, we used the well known network simulator ns2. Simulation results show that TCP Tahoe, TCP Reno, TCP New Reno, Sack are loss-based, they are beneficial for latency-sensitive flows, while TCP Vegas which is delay-based, it is recommended for applications that don't endure the loss of information but suffers from fairness problems when sharing a bottleneck with competitive flows.

Classical Transmission Control Protocol (TCP) designs have never considered the identity of the competing transport protocol as useful information to TCP sources in congestion control mechanisms. When competing against a TCP flow on a bottleneck link, a User Datagram Protocol (UDP) flow can unfairly occupy the entire link bandwidth and suffocate all TCP flows on the link. If it were possible for a TCP source to know the type of transport protocol that deprives it of link access, perhaps it would be better for the TCP source to react in a way which prevents total starvation. In this paper, we use coefficient of variation and power spectral density of throughput traces to identify the presence of UDP transport protocols that compete against TCP flows on bottleneck links. Our results show clear traits that differentiate the presence of competing UDP flows from TCP flows independent of round-trip times variations. Signatures that we identified include an increase in coefficient of variation whenever a competing UDP flow joins the bottleneck link for the first time, noisy spectral density representation of a TCP flow when competing against a UDP flow in the bottleneck link, and a dominant frequency with outstanding power in the presence of TCP competition only. In addition, the results show that signatures for congestion caused by competing UDP flows are different from signatures due to congestion caused by competing TCP flows regardless of their round-trip times. The results in this paper present the first steps towards development of more ’intelligent’ congestion control algorithms with added capability of knowing the identity of aggressor protocols against TCP, and subsequently using this additional information for rate control.

Queue delay estimation and its application to TCP Vegas

In deep space missions, large-scale deep space information and continuous sensory data are generated, processed, and exchanged over Internet of deep space things (IoDST). Ensuring reliable communication in IoDST requires resolution of challenges engendered by the unique characteristics of IoDST, including long propagation delays, link outages, high error rates, and asymmetric bandwidths. Thus, transmission control protocol (TCP) layer functionalities are crucial for ensuring reliable IoDST communications. However, existing TCP congestion control (CC) protocols present poor performance in IoDST communications, owing to the dependence of traditional window-based CC approaches on pre-configured rules to adjust transmission rate against the aforementioned unique characteristics. In this paper, we propose an intelligent CC scheme called optimistic actor–critic-based TCP congestion control (OAC-TCPCC) to solve the problems of the challenging link conditions in IoDST. OAC-TCPCC dynamically determines optimal congestion window for data transmission over IoDST communication links to maximize the TCP throughput performance and minimize file transfer time. Simulation results reveal that OAC-TCPCC improves the TCP throughput performance by up to 27%, 79%, 92%, 477%, 643%, and 766%; and reduces file transfer time by up to 16%, 37%, 50%, 48%, 58%, and 79%, compared to DRL-CC, TP-Planet, FAST TCP, TCP Peach, TCP BBR, and TCP CUBIC, respectively.

Many smart healthcare centers are deploying long distance, high bandwidth networks in their computer network infrastructure and operation. Transmission control protocol (TCP) is responsible for reliable and secure communication of data in these medial infrastructure networks. TCP is reliable and secure due to its congestion control mechanism, which is responsible for detecting and reacting to the congestion in the network. Many TCP congestion control mechanisms have been developed previously for different operating systems. TCP CUBIC, TCP Compound, and TCP Fusion are the default congestion control mechanism in Linux, Microsoft Windows, and Sun Solaris operating systems, respectively. The earliest congestion control mechanism Standard TCP acts as the trademark congestion control mechanism. The exponential growth of congestion window ( $cwnd$ ) in slow start phase of the TCP CUBIC causes burst losses of packets, and TCP flows did not share available link bandwidth fairly. The prime aim of this paper is to enhance the performance of TCP CUBIC for long distance, high bandwidth secured networks to achieve better performance in medical infrastructure, concerning packet loss rate, protocol fairness, and convergence time. In this paper, congestion control module for slow start is proposed, which reduces the effect of the exponential growth of $cwnd$ by designing the new limits of $cwnd$ size in slow start phase, which in turn decreases the packet loss rate in healthcare networks. NS-2 is used to simulate the experiments of enhanced TCP CUBIC and state-of-the-art congestion control mechanisms. Results show that the performance of enhanced TCP CUBIC outperforms by 18% as compared with the state-of-the-art congestion control mechanisms.

An Active Queue Management (AQM) is a proactive scheme that controls the network congestion by avoiding the congestion before it happened. When implementing AQM in wireless networks several contemporary issues must be considered, such as interference, collisions, multipath-fading, propagation distance, shadowing effects and route failure, and whether the wireless networks is WLAN or other type. Therefore, the needs for AQM algorithm that can perform in WLAN network as good and efficient as in wired network become so crucial. This paper proposes a new AQM algorithm called Hybrid Fair AQM (HF-AQM) that can achieve better fairness and higher utilization in WLAN environment by hybridizing queue delay and input rate to measure network congestion. The simulation results show that HF-AQM manages to gain a higher fairness level in comparison to several types of AQM schemes with higher link utilization due to the adaptation and hybridization that been used in its congestion indicator, control function and feedback function.