Congestion Control Scheme Research Articles

Comparative Analysis of Different Transport Layer Protocol Techniques Incognitive Network

Most of the networks employ TCP protocol for transmission control in transport mechanism. Although it offers numerous services such as reliability, end-to-end delivery, secure transmission of data, and so on to applications functioning across the World Wide Web, TCP needs to have effective congestion management techniques in order to handle traffic with a lot of data. Still, TCP have poor performance during data transmission in the network. The network community continues conducting research to develop a method that should provide a fair and effective transmission bandwidth distribution. Numerous congestion control strategies have been developed based on previous research in this area. This work discusses, identifies, compares, and analyses the behaviour of a few network congestion control strategies to determine their benefits and limitations. The widely known network simulator ns2 is employed for the simulation. The performance metrics for QTCP, TCP new Reno, TCP- Hybla, L-TCP and RL-TCP are throughput, average delay, PDR, packet loss, average jitter, latency and fairness are considered. The RL-TCP exhibits superior performance in multiple measures when it is compared to alternative TCP protocols, as indicated by simulation results. These metrics encompass throughput, average delay, packet delivery ratio (PDR), packet loss, jitter, latency, and fairness. Furthermore, several TCP protocols, such as L-TCP, TCP-Hybla, QTCP, and TCP-New Reno, have undergone evaluation, uncovering disparities in their individual performance attributes. Nevertheless, the RL-TCP regularly demonstrates superior performance in all aspects.

UFC3: UAV-Aided Fog Computing Based Congestion Control Strategy for Emergency Message Dissemination in 5G Internet of Vehicles

UFC3: UAV-Aided Fog Computing Based Congestion Control Strategy for Emergency Message Dissemination in 5G Internet of Vehicles

A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

A hybrid congestion control scheme for Named Data Networking via SDN

As the mainstream congestion control in Named Data Networking (NDN), the hybrid control mechanism integrates the advantages of consumer and router control mechanisms. However, The concurrent control by both consumers and routers may results in degraded data transmission performance. Therefore, we propose a novel hybrid congestion control scheme for NDN, called ndnSieve. By using the characteristics of Software Defined Network (SDN) that can achieve global network information acquisition as well as global resource scheduling, ndnSieve manages to invoke consumer and router control mechanisms based on different congestion states in the network respectively and achieves the synergy between two kinds of mechanisms. The results of experiment demonstrate that, compared with existing works, ndnSieve has the lowest average round-trip time, relatively high level of throughput and fairness, effectively improving the Quality of Service (QoS) of the network.

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

Through the RTT Lens : A Comparative study of CUBIC and BBR Congestion Control

As the demand for high-performance internet applications continues to surge, the efficiency of network congestion control algorithms becomes a critical factor in ensuring a seamless user experience. This research paper delves into the comparative analysis of two prominent congestion control mechanisms: BBR (Bottleneck Bandwidth and Round- trip propagation time) and Cubic[1]. Both algorithms play pivotal roles in regulating data flow within networks, but their approaches differ significantly. The comparison section highlights the adaptive behavior of each algorithm, emphasizing real-world implications for diverse network scenarios. The discussion interprets the findings, offering a nuanced understanding of the strengths and weaknesses of both BBR and Cubic, thereby contributing to the broader discourse on congestion control strategies.

CoopCon: Cooperative Hybrid Congestion Control Scheme for Named Data Networking

Congestion control is a key technology for guaranteeing quality-of-service (QoS) in Named Data Networking (NDN). Hybrid congestion control has gradually developed into the mainstream method in NDN congestion control, which capitalizes on the advantages of receiver adjusting rate and router diverting traffic to deal with congestion. However, it has to address how to effectively coordinate consumers and routers to prevent transport performance degradation caused by repeated and excessive control. In this paper, a hybrid congestion control scheme named CoopCon is proposed, which fully gives the cooperation between consumers and routers to control the congestion adaptively. Moreover, the optimal path is used resiliently by CoopCon to enhance the robustness of multipath forwarding and multicast data delivery in NDN. The proposed CoopCon is implemented in ndnSIM. And simulation results show that CoopCon consistently achieves higher total throughput than existing work. In particular, the total throughput of consumers deployed with CoopCon is 19.4% higher than that of consumers deployed with PCON in the BRITE-generated topology. Additionally, CoopCon also achieves the best fairness in a dumbbell topology, with a fairness index of even 0.96.

Energy efficient congestion control scheme based on Modified Harris Hawks Optimization for heavy traffic Wireless Sensor Networks

The performance of Wireless Sensor Networks (WSNs), a subset of Wireless Ad-hoc Networks, is significantly influenced by the application, lifetime, storage capacity, processing power, changes in topology, communication medium, and bandwidth. These restrictions call for a strong data transport control in WSNs that takes into account quality of service, energy efficiency, and congestion management. Wireless networks face a significant difficulty with congestion which impacts on the loss rate, channel quality, link utilization, the number of retransmissions, traffic flow, network lifetime, latency, energy, and throughput are all negatively impacted by congestion in WSNs. Since the routing problem has been shown to be NP-hard and it has been realized that a heuristic based method delivers better performance than their traditional counterparts, routing is one of the most popular methods for reducing the energy consumption of nodes and increasing throughput in WSNs. This research provides a Rate Aware Congestion Control (RACC), an effective congestion avoidance method that enhances network performance by applying Modified Harris Hawks Optimization (MHHO). Nodes are initially clustered using the DBSCAN clustering algorithm. When compared to existing approaches, the simulation outcomes of the developed technique indicate superior service, low delay, high energy, packet delivery ratio and increased living nodes.

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

A Prediction-Based Route Guidance Method Toward Intelligent and Green Transportation System

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with re-routing since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of re-routing. Against this backdrop, a Prediction-based Route Guidance Method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: perception about traffic congestion with three indexes (i.e., speed index, dense index, acceleration index); judgment about congestion type with four defined congestion types; prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); route planning about vehicles considering congestion duration and the extra time overhead of re-routing. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

A Weighted Optimal Scheduling Scheme for Congestion Control in Cloud Data Center Networks

Cloud services are experiencing a remarkable increase in the number of users and the resource required over the past few years. Thus, it has become a great challenge for the internet vendors to make a robust framework to serve the customers with low cost and delay. Congestion control is one of the essential topics of routing algorithms in cloud data center networks. In this paper, we propose a weighted optimal scheduling scheme WSPR for congestion control in cloud data center networks which prevents the congestion in advance with the global view so that it can make good use of vacant network resources. We choose BCube as our network model and modify the network topology to fit software-defined networks so as to have a full view of the topology. First, we design the SP graph which contains all shortest paths between a source server and a destination server. Second, we propose WSPR to allocate the most appropriate path to each flow for congestion control. We implement a system to simulate a data center, and evaluate our proposed scheme WSPR by comparing WSPR with other classical methods. The experimental results demonstrate that our proposed scheme WSPR has the best performance in terms of the maximum delay, average delay, and throughput among all compared methods.

WOA-Based Robust Congestion Control Scheme with Two Kinds of Propagation Latencies and External Disturbance in Software-Defined Wireless Networks

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme.

RaceCC: A rapidly converging explicit congestion control for datacenter networks

Congestion control (CC) in datacenter networks has three primary goals: high link utilization, low queuing delay, and rapid convergence to fairness. Most of the host-driven CC schemes perform well in achieving the first two goals, while it is difficult to converge to fairness rapidly. Switch-driven CC schemes can be a compelling alternative to achieve fairness since switches explicitly provide feedback to hosts. However, we found that existing switch-driven CC schemes converge slowly or sometimes could not guarantee low queues.Based on these observations, in this paper, we propose RaceCC, a RApidly Converging Explicit CC to achieve the three primary goals simultaneously. As a switch-driven CC scheme, RaceCC enables flows to reach a fair rate after the first RTT and guarantees high link utilization and low queue length. To converge rapidly, RaceCC adjusts flow rates through an intuitive MIMD method with additive-decrease for short queues and precise update for increase. Meanwhile, RaceCC can be implemented with several simple operations. We theoretically analyze the stability of RaceCC and evaluate its performance through micro-benchmark and large-scale simulations. The results show that RaceCC reduces the overall average and tail flow completion time (FCT) by 20% ∼ 57% and 15% ∼ 63%, compared to DCQCN, TIMELY, HPCC, PowerTCP, RCP and RoCC.

Intelligent TCP Congestion Control Scheme in Internet of Deep Space Things Communication

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.

Efficient congestion control scheme based on caching strategy in NDN

As a pioneering network architecture, Named Data Networking (NDN) leverages the content-centric model and connectionless transmission mode to enhance network capacity. Despite the emergence of numerous congestion control protocols aimed at improving NDN transmission efficiency, most of these approaches implicitly assume that each interest packet elicits one single data packet, which significantly restricts network performance. To address this issue, we introduce C3NDN, a Congestion Control scheme that leverages caching strategy in NDN. C3NDN formulates a One-Interest-Multiple-Data model to improve network efficiency, and employs a probabilistic caching strategy to cache popular content at important nodes and optimize the in-network caching characteristic of NDN. Furthermore, C3NDN incorporates a congestion control algorithm based on the One-Interest-Multiple-Data model, which considers the bandwidth and delay information of the transmission path. Additionally, C3NDN implements a marking method that takes full advantage of node cache and reduces transmission time. Extensive experiments have been conducted to show the performance of C3NDN, and the results demonstrate that C3NDN can reduce the transmission time by up to 41.68% when compared with the other congestion control schemes.

Stochastic Digital-Twin Service Demand With Edge Response: An Incentive-Based Congestion Control Approach

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

A congestion control scheme based on path recovery for smart grid communication

In order to solve the problem that the original path forwarding traffic caused by congestion that cannot be recovered in time in Smart Grid Communication, a congestion control scheme RFCC (Router Forwarding Congestion Control) based on path recovery is proposed, which not only supports the receiving end combined with router nodes to relieve congestion, it can also solve the problem of low transmission performance caused by not being able to restore the original path in time after the traffic is transferred. Active queue management technology is used to detect congestion and notify downflow nodes (users and routers) in time, which trigger downflow users to adjust the request sending window and intermediate routers to transfer packets. In addition, the original path traffic is restored in time through the path recovery policy. Firstly, the path traffic is recovered according to the congestion mark carried by the subsequent content packets. Secondly, the transmission frequency of the probe packets is calculated according to the information such as the packet queue length of routers and the available bandwidth of the congested path, and the probe packets are periodically sent to the congested path, and the traffic is sent to the congested path in time. The alternate path is transferred back to the original path. This paper implements the scheme in NS-3, and extensive experiments show that the proposed scheme outperforms the comparison schemes, which not only higher and more stable throughput can be obtained, but also shorter and more stable transmission delay can be obtained. In addition, the optimal path transmission volume and link utilization are improved.

Rationale and study design of the GOREISAN for heart failure (GOREISAN-HF) trial: A randomized clinical trial

BackgroundThe decongestion strategy using loop diuretics is essential for improving signs and symptoms of heart failure (HF). However, chronic use of loop diuretics in HF has been linked to worsening renal function and adverse clinical outcomes in a dose-dependent manner. Goreisan, a traditional Japanese herbal medicine, has a long history of use in Japan for regulating body fluid homeostasis and has been recognized as causing less adverse outcomes such as dehydration in contrast to loop diuretics in clinical practice. Therefore, we designed the GOREISAN-HF trial to evaluate the long-term effects of a new decongestion strategy adding Goreisan to usual care in patients with HF and volume overload. MethodsThe GOREISAN-HF trial is an investigator-initiated, multicenter, pragmatic, randomized, comparative effectiveness trial in which we will enroll 2,192 patients hospitalized for HF at 68 hospitals in Japan. All study participants will be randomly assigned to either a decongestion strategy that adds Goreisan at a dose of 7.5 g daily on top of usual care or usual care alone. Investigators have the flexibility to change the existing diuretic regimen in both groups. The primary end point is the improvement rate of cardiac edema at 12-month follow-up, and the co-primary end point is a composite of all-cause death or hospitalization up to the end of the planned follow-up period. Secondary end points include longitudinal changes in patient-reported outcomes, loop diuretics dose, and renal function. ConclusionsThe GOREISAN-HF is the first large-scale randomized pragmatic trial to assess the efficacy and safety of a new congestion control strategy adding Goreisan to usual care in patients with HF and volume overload. Registration Number: NCT04691700