Congestion Control Performance Research Articles

Mustang: Improving QoE for Real-Time Video in Cellular Networks by Masking Jitter

The advent of 5G and interactive live broadcasting has led to a growing trend of people preferring real-time interactive video services on mobile devices, particularly mobile phones. In this work, we measure the performance of Google congestion control (GCC) in cellular networks, which is the default congestion control algorithm for Web Real-Time Communication (WebRTC). Our measurements show that GCC sometimes makes bitrate decisions which are harmful to quality of experience (QoE) in cellular networks with high jitter. We further find that the frame delivery time (FDT) in the player can mitigate network jitter and maintain QoE. Moreover, the receiving rate is better to reflect the network congestion than RTT in cellular networks. Based on these measurements and findings, we propose Mustang, an algorithm designed to overcome the jitter in cellular networks. Mustang makes use of the FDT and receiving rate as feedback information to the sender. Then the sender adjusts its sending rate based on the information to guarantee QoE. We have implemented Mustang in WebRTC and evaluated it in both emulated and real cellular networks. The experimental results show that Mustang can improve WebRTC’s QoS and QoE performance. For QoS, Mustang increases the sending rate by 72.1% and has similar RTT and packet loss when compared with GCC, while it is about 30% better for QoE.

Congestion-Free Cluster Formation and Energy Efficient Path Selection in Wireless Sensor Networks using ButPCNN

Today, network congestion is a common occurrence that needs to be focused on and effectively addressed, particularly in Wireless Sensor Networks (WSN) for packed type networks. The main causes of congestion in WSN are a lack of channel capacity and energy waste. This study's major goal is to develop Energy Efficient Congestion Free Path Selection Protocol (ECFPSP) protocol, which aims to reduce network congestion. By selecting the most appropriate main cluster head (PCH) and secondary cluster head (SCH), the ECFPSP protocol is proposed to decrease end-to-end delay time and extend the network lifetime. The suggested protocol implements a routing protocol that provides security by avoiding hostile nodes and reducing data loss. It also routes the nodes. Hence, a Congestion-Free Cluster Formation is provided to increase the lifetime of the network by proposed ButPCNN approach. To decrease packet loss and conserve energy, this research also uses brand-new cluster-based WSNs. In comparison to other standard protocols, the simulation results reveal that ButPCNN has a reduced packet drop rate, which increases the ratio of packet distribution, network life, and residual energy. As a result, the suggested method enhances congestion control performance while using less energy and a recently developed strategy is suggested to successfully enhance network performance. The proposed ButPCNN gives 25 percent improvement to optimize traffic on overloaded node than the other traditional approaches.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

Deep Reinforcement Learning-Based Distributed Congestion Control in Cellular V2X Networks

Distributed congestion control (DCC) improves system performance by lowering channel congestion in vehicular environments with high vehicle density. The 3rd Generation Partnership Project standard defines the related metrics of channel busy ratio (CBR) and introduces possible rate and power control mechanisms to mitigate channel congestion in cellular vehicle-to-everything (C-V2X) sidelink. However, the DCC of C-V2X is not sufficiently specified to implement these controls. In this letter, we propose a novel DCC algorithm based on deep reinforcement learning (DRL) to improve congestion control performance in C-V2X sidelink. The proposed algorithm allows the DRL agent to observe a CBR state and select the packet transmission rate that can maximize the reward of packet delivery rate (PDR) while maintaining higher channel utilization. Simulation results show that the proposed algorithm provides performance gain in terms of PDR and sidelink throughput compared with the existing DCC method.

An optimization‐based congestion control for constrained application protocol

SummaryThe Constrained Application Protocol (CoAP) is a lightweight web transfer protocol designed based on the REST architecture standardized by the Internet Engineering Task Force (IETF) to meet and accommodate the requirements of the constrained Internet of Things (IoT) environments. Managing congestion control in a resource‐constrained lossy network with a high bit error rate is a significantly challenging task that needs to be addressed. The primary congestion control mechanism defined by CoAP specification leverages on basic binary exponential backoff and often fails to utilize the network dynamics to the best of its traffic conditions. As a result, CoCoA has been introduced for better IoT resource utilization. In addition, CoCoA retransmission timeout (RTO) for network dynamics is based on constant coefficient values. The resource‐constrained nature of IoT networks poses new design challenges for congestion control mechanisms. In this paper, we propose a new particle swarm optimization (PSO)‐based congestion control approach called psoCoCoA as a variation of CoCoA. The psoCoCoA applies random and optimal parameter‐driven simulation to optimize default CoAP parameters and update the fitness and velocity positions to adapt to the traffic conditions. This process is performed for different traffic scenarios by varying the retransmission and max‐age values by using the optimization‐based algorithm. We carried out extensive simulations to validate the congestion control performance for CoAP with Observe, CoCoA, and psoCoCoA with different network topologies. The results indicate that psoCoCA outperforms or very similar to CoCoA and achieves better performance compared to CoAP with Observe under different network scenarios.

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

Congestion control provides the endpoint the ability to probe available bandwidth and prevents the network falling into severe congestion. But loss based congestion control algorithms designed for ...

An Improved Algorithm to Enhance the Performance of FAST TCP Congestion Control for Personalized Healthcare Systems

The development of personalized medical systems should be supported by a fast and stable network system. The FAST TCP network system is the appropriate support system for this purpose. However, when the FAST TCP is deployed, the static mapping selection method for protocol parameters is unable to guarantee the small queuing delay and fast convergence of the network simultaneously. By conducting theoretical analysis and simulation experiments, the relationships among FAST TCP protocol slow start condition, control law gain parameters, and FAST TCP system convergence rate were examined. To ensure the stability of the FAST TCP system and to select the smallest protocol parameters, an improved method to effectively accelerate the convergence velocity of the FAST TCP system is proposed in this study. In this method, the number of packets for staying in the buffer for FAST TCP connections was taken as the criterion of the slow start, and the gain parameter of the control law was dynamically adjusted according to the local information of each FAST TCP connection. Using this improved method, the FAST TCP system can achieve a stable and small queuing delay, whilst the FAST TCP system could converge quickly to the equilibrium point simultaneously.

Low-Cost Datacenter Load Balancing With Multipath Transport and Top-of-Rack Switches

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this article, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack many-to-one short flows. We have implemented DCMPTCP in both the Linux kernel and ns-3 simulator. Our comprehensive testbed experiments and simulations show that DCMPTCP outperforms MPTCP in both 1 Gbps testbed, and 10 Gbps large-scale simulation network.

Improving the Congestion Control Performance for Mobile Networks in High-Speed Railway via Deep Reinforcement Learning

Due to the poor Transmission Control Protocol (TCP) performance in high-speed mobile scenarios, passengers have bad network experiences on High-Speed Railway (HSR). As a result, improving network performance for HSR scenarios has become urgent and widespread concerns. Some previous works quantitatively analyzed the TCP performance on HSR and proposed relevant solutions. Other works focused on the handover problem (the leading cause of poor network performance on HSR), and proposed a series of handover algorithms for HSR scenarios. However, the existing works are either limited to only measurement studies without algorithm implementation or lack of integration with real-world scenarios. In this paper, with a large amount of field measurement data in real HSR networks, we study the main reasons why traditional TCP performs poorly in HSR scenarios. To improve the TCP performance, we propose Hd-TCP, a customized Congestion Control (CC) algorithm designed to deal with frequent handover on HSR from the transport layer perspective. For the transmission characteristic on HSR, Hd-TCP can accurately evaluate the link condition and apply a Deep Reinforcement Learning (DRL) method to make a fine control. The simulation results show that Hd-TCP outperforms traditional CC algorithms in both throughput and latency by fully utilizing the transmission gap between handovers.

Congestion control performance investigation of ModQUIC protocol using JioFi network: A case study

Congestion control performance investigation of ModQUIC protocol using JioFi network: A case study

V2V System Congestion Control Validation and Performance

Major international automakers have considered the deployment of the 5.9 GHz dedicated short-range communications (DSRC) on their vehicle fleets for wireless connectivity. DSRC-enabled vehicle-to-vehicle (V2V) communication through broadcast of basic safety messages enables safety applications for crash warning and avoidance. However, in dense traffic conditions as the V2V deployment scales up, the resultant channel load increases and leads to channel congestion and may adversely affect the performance of the safety applications. The Society of Automotive Engineers J2945/1 standard that builds atop Institute of Electrical and Electronics Engineers (IEEE) 802.11p and IEEE 1609 standards provides the minimum performance requirements for V2V safety communications. Specifically, it provides a congestion control protocol for transmission rate and power adaptations to achieve robust performance in dense vehicular networks. The primary contribution of this paper is that using a congestion generation testbed that emulates channel congestion including a large number of remote vehicles, we can validate and test any V2V equipped vehicle for compliance with the J2945/1 standard. Our paper also demonstrates that under heavy congestion, even with 600 ms of inter-transmit time, a moving vehicle can be tracked to a lane-level accuracy.

Hybrid Fuzzy Congestion Controllers for Computer Networks Tuned by Modified Particle Swarm Optimization

One of the most debated issues nowadays is the quality of computer network service. The best internet service must provide a fast processing of the traffic. Each router has a queue of packets that provides a buffer space, where the packets wait for processing. Transmission Control Protocol (TCP) is a packets congestion control theory. Active Queue Management (AQM) is a mechanisms proposed to employ at gateways to improve the performance of TCP congestion control. AQM mechanisms aim to provide high link utilization with low loss rate and low queuing delay while reacting to load changes quickly. Random Early Detection (RED) is an extensively studied AQM algorithm that can detect congestion by dropping packets randomly with certain probability that serves as the function of the average queue size. In this work, hybrids Fuzzy Logic Controllers (FLC) are proposed to measure the router queue size directly by use them as a congestion controllers. A multiple hybrid fuzzy controllers are proposed, where (Proportional Integral Derivative controller (PID) -like FLC-Particle Swarm Optimization (PSO) Based, Proportional Derivative (PD)-like FLC with conventional I-PSO Based and PID tuned by Fuzzy Logic-PSO Based), which is provided to regulate the queue length, round trip time and packet loss. The Particle Swarmed Optimization (PSO) algorithm is used for tuning the gains of hybrid fuzzy logic controller which helps in reducing the error of the queue size. This is achieved through minimizing the rise time, peak time, settling time and overshoot of the AQM response. The empirical results revealed a high-performance improvement regarding the proposed method in comparison to previous works of other researchers.

Performance of TCP Congestion Control in UAV Networks of Various Radio Propagation Models

Unmanned aerial vehicles (UAVs) have recently become popular for both recreational and commercial use and UAV networks have thus started to attract the attention of researchers in area of the computer communication and networking. One important topic in UAV networks is congestion control because congestion causes packet losses and delays which result in the waste of all types of network resources such as bandwidth and power. Although there are studies on the performance of TCP congestion control in wireless networks, they focus on terrestrial networks of two dimensions in general. In this paper we study the performance of TCP congestion control in three dimensional UAV networks. In particular, we investigate how TCP congestion control performs in such type of network using various radio propagation models. Our data on the average flow throughput, packet delay, and packet loss rate in UAV networks show that TCP congestion control improves the network performance of UAV networks in general, but it faces challenges when the link losses become severe. Our study thus shows that investigation on new congestion control schemes is stilled needed for the emerging UAV networks.

CoCoA+: An advanced congestion control mechanism for CoAP

The Constrained Application Protocol (CoAP) has been designed by the Internet Engineering Task Force (IETF) for Internet of Things (IoT) devices. Due to the limited radio channel capacities and hardware resources of such devices, congestion can be a serious problem. CoAP addresses this important issue with a basic congestion control mechanism. CoCoA, an Internet-Draft proposal, introduced alternative congestion control mechanisms for CoAP. Yet, there has been limited evaluation of these congestion control mechanisms in the literature. In this paper, we assess the methods applied in CoCoA in detail and propose improvements to address the shortcomings observed in the congestion control mechanisms. We carry out simulations to compare the congestion control performance for default CoAP, CoCoA, and our new proposal, CoCoA+, in a variety of network topologies and use cases. The results show that CoCoA+ outperforms default CoAP and achieves better results than CoCoA in the majority of considered cases.

English

The employment of the existing protocols such as Transmission Control Protocol (TCP) cannot achieve the requirements of high-speed networks such as long term evolution advanced (LTE-A) due to the large-bandwidth and low-latency links used in this network. LTE-A network is the continuation of 3GPP-LTE (3GPP: 3rd Generation Partnership Project) and it is targeted to advanced development of the requirements of LTE in terms of throughput and coverage. Then, LTE-A is not new as a radio access technology, but it is an evolution of LTE to enhance the performance. Therefore, it is necessary to enhance the TCP variants such as improving the congestion control performance over LTE-A to make these variants fulfills the needs of the huge data which transferred over the secured links. This article offered comparative evaluation and estimation of the queue size performed by different TCP source variants over traffic model of LTE-A system using the Network Simulator 2 (NS-2).The queue size measurement proved that TCP Vegas performed better than TCP Tahoe and TCP Reno that are used in the simulation scenario.   Keywords: Long Term Evolution Advanced (LTE-A), queue size, Transmission Control Protocol (TCP), Network Simulator 2 (NS-2).

CONGESTION CONTROL BASED ON SLIDING MODE CONTROL AND SCHEDULING WITH PRIORITIZED QUEUING FOR WIRELESS NETWORKS

The application of sliding mode control in wireless network is used for considering the joint congestion control and scheduling. Incorporating dual decomposition enables the joint congestion control and scheduling problem to be considered separately. The communication among these two sub problems is given by the lagrangian price value. Based on the utility optimization of network the congestion control performance for queues can be improved using sliding mode based congestion controller. Buffers on network devices are managed with various queuing techniques. Properly managed queues can minimize dropped packets and network congestion, as well as improve network performance. Thus, the queuing structure of CBWFQ (Class Based Weighted Fair Queuing) and strict Priority is combined so that the packets are classified into different classes and priorities are given to these classes, where the higher priority are given to delay sensitive packets and are scheduled effectively.

Systematic mapping study on the congestion control problem in TCP/IP

Traffic congestion of the Internet is one of the major communication problems lived by millions of users. Many works have been devoted to improve the internet congestion control performance [1]. In this paper, we will apply a Systematic Mapping Study to the congestion control problem in TCP/IP, in order to build a classification scheme and measure the field interest. The analysis of results focuses on frequencies of publications for categories within the scheme. Thereby, the coverage of the research field can be determined. Different filters of the scheme can also be combined to answer more specific research Questions.

A QoS-Oriented Congestion Control Mechanism for Satellite Networks

The sharply increasing amount of data, which are transferred by the satellite network, requires the satellite network to provide quality-of-service (QoS). However, the upsurge in the data flow leads to the network congestion, impeding its ability to offer QoS. Congestion control mechanisms, deployed in the ground networks, have been thoroughly studied. But those deployed in the satellite networks have not been studied yet. As satellite networks are now important supplements to the ground backbone networks, this paper carefully analyzes the current challenges of developing the QoS-oriented congestion control mechanism for satellite networks. On this basis, a consequent mechanism QMCC (QoS-oriented mechanism of congestion control for satellite networks) is described in detail. Under that QMCC, the source nodes utilize an equation to compute the sending rate for each data flow; meanwhile, the intermediate nodes keep detecting real-time package-loss rates for timely adjustments. Simulation results indicate QMCC can provide superior congestion control performance, and raise network throughputs without reducing the QoS effects.

APPLICATION OF NEWTON RAPHSON ALGORITHM FOR OPTIMIZING TRANSMISSION CONTROL PROTOCOL PERFORMANCE

Wireless networks are growing rapidly. TCP is the most widely-used protocol on Internet and so optimizing TCP performance is very important for fast efficient data transfer. The different existing TCP variants and solutions they have not been analyzed together to identify the bottlenecks in wireless networks. TCP has a major problem in its congestion control algorithm which does not allow the flow to achieve the full available bandwidth on fast long-distance links. This problem has been studied in this study using a new high speed congestion control TCP protocol based on the Newton Raphson algorithm This study further analyses involving six TCP performance evaluation constraints namely, TCP full bandwidth utilization, throughput, packet loss rate, fairness in sharing bandwidth, friendliness in short-RTT and long-RTT and these constraints are used to evaluate the proposed Newton Raphson Congestion Control (NRC-TCP) performance. This study shows that the proposed algorithm performs better compared with the other methods of application.