Congestion Control Protocol Research Articles

An Energy-Efficient Distributed Congestion Control Protocol for Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) generate a huge amount of multimedia data. Congestion is one of the most challenging open issues in WMSNs. Congestion causes low throughput, high packet loss and low energy efficiency. Congestion happens when the data carried by the network surpasses the available capacity. This article presents an energy-efficient distributed congestion control protocol (DCCP) to mitigate congestion and improve end-to-end delay. Compared to the other protocols, the DCCP protocol proposed in this article can alleviate congestion by intelligently selecting the best path. First, congestion is detected by using two congestion indicators. Second, each node aggregates the received data and builds a traffic congestion map. The traffic congestion map is used to calculate the best path. Therefore, the traffic is balanced on different routes, which reduces the end-to-end delay. Finally, a rate controller is designed to prevent congestion in the network by sending a congestion notification message to a source node. After receiving a congestion notification message, the source node immediately adjusts its transmission rate. Experimental results based on raspberry pi sensor nodes show that the proposed DCCP protocol significantly improves network performance and is superior to existing modern congestion control protocols.

A Survey on Congestion Control Protocols for CoAP

The Internet of things (IoT) comprises things interconnected through the internet with unique identities. Congestion management is one of the most challenging tasks in networks. The Constrained Application Protocol (CoAP) is a low-footprint protocol designed for IoT networks and has been defined by IETF. In IoT networks, CoAP nodes have limited network and battery resources. The CoAP standard has an exponential backoff congestion control mechanism. This backoff mechanism may not be adequate for all IoT applications. The characteristics of each IoT application would be different. Further, the events such as unnecessary retransmissions and packet collision caused due to links with high losses and packet transmission errors may lead to network congestion. Various congestion handling algorithms for CoAP have been defined to enrich the performance of IoT applications. Our paper presents a comprehensive survey on the evolution of the congestion control mechanism used in IoT networks. We have classified the protocols into RTO-based, queue-monitoring, and rate-based. We review congestion avoidance protocols for CoAP networks and discuss directions for future work.

Distributed Bandwidth Allocation Strategy for QoE Fairness of Multiple Video Streams in Bottleneck Links

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm’s QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme.

Performance analysis of congestion control protocols over 5G mmWave links

Millimeter wave technology is an important basic technology in 5G applications. It has higher bandwidth and lower delay. But high dynamics of the channel poses a major challenge to the design of congestion control protocols. Based on ns3, this paper conducts extensive experiments on the TCP congestion control protocols, and analyzes the transmission performance of different protocols over 5G millimeter wave links in detail. It will lay a foundation for further research on congestion control protocols suitable for 5G millimeter wave communications.

Analysis of Algorithms to Control the Congestion by Improve Energy Efficiency in WSN

Congestion is a major significant challenge in WSNs because it directly impacts energy efficiency and the network lifetime of sensor nodes in the network. This paper aims to analyze the different congestion control or avoidance routing protocols performances in WSNs with their drawbacks for identifying the upcoming scope of congestion-aware routing protocols in WSNs. This article proposed an adaptive queuing system with the WPDDRC called a proficient control (PRC) algorithm to tackle this issue. In this algorithm, two independent virtual queues are considered single physical length (lines), which accumulate the input packets from every child's node depending on the source's traffic significance and priority. A Proficient Rate Control (PRC) technique develops using traffic type priority and virtual queue conditions. Here, a PRC with fair bandwidth allocation (PRC-FBA) technique is compared and analyzed. It must handle the congestion due to the mix of RT and Non-RT (NRT) packets effectively in network.

GCCP - NS: Grid based Congestion Control protocol with N-Sinks in a Wireless Sensor Network

Wireless Sensor Networks (WSN) have been a current trend in the research field and has many issues when there are multiple mobile sinks. Data dissemination gets critical as their locations have to be repeatedly updated and results in huge consumption of the restricted battery supply in sensor nodes. In this paper, we propose GCCP – NS, a grid based congestion control protocol with N –sinks that solves the data dissemination problem leading to congestion. We construct a dual level grid structure to trail the locations of all the source nodes that reports the information to the mobile sinks by monitoring the network in a hierarchical manner. As an added advantage, it aids in data dissemination based on query flooding from the mobile sinks using quorum based method within each cell in the grid and avoids congestion in an effective manner. Simulation results show that our proposed protocol outperforms the other schemes in terms of packet delivery ratio, energy expenditure and throughput.

An Axiomatic Perspective on the Performance Effects of End-Host Path Selection

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection. In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestioncontrol dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the "axioms") such as efficiency, fairness, and loss avoidance. We analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantititative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.

Solution] ALCC: Migrating Congestion Control To The Application Layer In Cellular Networks

TCP is known to perform poorly in cellular network environ- ments. Yet, most mobile applications are explicitly built on the conventional TCP stack or implicitly leverage TCP tun- nels to various cellular middleboxes, including performance- enhancing proxies, application-specific edge proxies, VPN proxies and NAT boxes. Despite significant advances in the design of new congestion control (CC) protocols for cellular networks, deploying these protocols without bypassing the underlying TCP tunnels has remained a challenging propo- sition. This paper proposes the design of a new Application Layer Congestion Control (ALCC) framework that allows any new CC protocol to be implemented easily at the application layer, within or above an application-layer protocol that sits atop a legacy TCP stack. It drives it to deliver approximately the same as the native performance. The ALCC socket sits on top of a traditional TCP socket. Still, it can leverage the large congestion windows opened by TCP connections to carefully execute an application-level CC within the window bounds of the underlying TCP connection. This paper demonstrates how ALCC can be applied to three well-known cellular CC pro- tocols: Verus, Copa, and Sprout. For these protocols, ALCC can achieve comparable throughput and delay characteristics (within 3-10%) as the native protocols at the application layer across different networks and traffic conditions. ALCC al- lows a server-side implementation of these protocols with no client modifications and with zero bytes overhead. The ALCC framework can be easily integrated with off-the-shelf applications such as file transfers and video streaming.

Improved Priority-based Congestion Control Protocol for Multi-Access Edge Computing (MAEC) Using IoT-based Wearable Devices for Neurological Diseases Diagnosis

Stroke is one of the fatal diseases that affect the brain and causes death within 3 to 10 hours. However, most of the deaths caused by a stroke can be avoided with the identification of the nature of stroke and react to it in a timely manner by intelligent health systems. Internet of Things (IoT) has become an important aspect in medical industry for monitoring of stroke related data/information using various wearable devices. Moreover, Multiple-Access Edge Computing (MAEC) is playing a major role for processing, analysing, and storing of data which leads to several researchers to compete in improving the mechanism of congestion control. In this paper, an improved Priority-based Congestion Control Protocol (iPCCP) is proposed for obtaining increased throughput, decreasing delay, effective resource utilization, and longer network lifetime by optimal energy consumption among IoT based sensor nodes. The proposed method categorizes the data-traffic into emergency and normal data. The packet delivery rate is considered for the normal data-traffic and retains the size of the buffer to improve the throughput and avoids the packet drops due to congestion. The energy consumption and network traffic load is reduced using the data aggregation and filtration technique. For emergency situations, priority-based routing scheme is used to have greater throughput and lesser delay. The performance of the proposed technique outperforms in term of traffic load, lifespan, energy consumption, and network throughput and simulation results are compared with other existing methods to show the improvement of the proposed work.

Exploring the Measurement Lab Open Dataset for Internet Performance Evaluation: The German Internet Landscape

The Measurement Lab (MLab) provides a large and open collection of Internet performance measurements. We make use of it to look at the state of the German Internet by a structured analysis, in which we carve out expressive results from the dataset to identify busy hours and days, the impact of server locations and congestion control protocols, and compare Internet service providers. Moreover, we examine the impact of the COVID-19 lockdown in Germany. We observe that only parts of the Internet show a performance degradation at the beginning of the lockdown and that a large impact in performance depends on the network the servers are located in. Furthermore, the evolution of congestion control algorithms is reflected by performance improvements. For our analysis, we focus on the busy hours. From the end-user perspective, this time is of most interest to identify if the network can support challenging services such as video streaming or cloud gaming at these intervals.

Forecasting TCP's Rate to Speed up Slow Start

Selection of the optimal transmission rate in packet-switched best-effort networks is challenging. Typically, senders do not have any information about the end-to-end path and should not congest the connection but at once fully utilize it. The accomplishment of these goals lead to congestion control protocols such as TCP Reno, TCP Cubic, or TCP BBR that adapt the sending rate according to extensive measurements of the path characteristics by monitoring packets and related acknowledgments. To improve and speed up this adaptation, we propose and evaluate a machine learning approach for the prediction of sending rates from measurements of metrics provided by the TCP stack. For the prediction a neural network is trained and evaluated. The prediction is implemented in the TCP stack to speed up TCP slow start. For a customizable and performant implementation the extended Berkeley packet filter is used to extract relevant data from the kernel space TCP stack, to forward the monitoring data to a user space data rate prediction, and to feed the prediction result back to the stack. Results from a online experiment show improvement in flow completion time of up to 30%.

A price-aware congestion control protocol for cloud services

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

Rate aware congestion control mechanism for wireless sensor networks

The sensor nodes in wireless sensor networks (WSNs). inherit low processing, shorter range and low power features with the miniature size to offer wireless transmission. Therefore, in order to fulfil the long-time data sensing need, several Quality of Service (QoS) parameters such as: lesser delay, higher throughput and packet delivery ratio (PDR) with minimum overhead must to be improved. In recent years extensive research efforts have been made to ameliorate these parameters to achieve optimum QoS. However, the rate adaptation and congestion control in WSNs are still least explored areas. The traffic congestion in WSNs is the main reason that results in higher delay and low throughput. In this paper, a new rate aware congestion control (RACC) mechanism has been proposed which defines three levels of congestion based on which the data rate, throughput, overhead and the delay. RACC at the transport layer, improves congestion by source rate regulation at the specific hotspot areas. Further, RACC has been applied to different modulation schemes like: 16 QAM (Quadrature Amplitude Modulation), BPSK (Binary Phase Shift Keying) and QPSK (Quadrature Phase Shift Keying) to test the optimum modulation scheme for the proposed approach. The testing is done to ensure the that the data can be sent to the longer distant sensors using appropriate modulation technique suitable for the congestion model (RACC). The simulation outcomes in NS2 tool confirms the improvement of RACC over existing techniques (Delay-aware congestion control protocol (DACC) and Joint energy replenishment and load balancing (J-ERLB)) in WSNs. The overall improvement for RACC over existing techniques follows an improvement percentage 17% for throughput parameter, for packet delivery ratio, it is 8.35%, while for normalized routing overhead it shows 0.56% and for MAC Overhead, average end to end delay, and average remaining energy it shows 0.64%, 2.04% and 59.28% improvement respectively.

Delay-Based Network Utility Maximization Modelling for Congestion Control in Named Data Networking

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

Efficient video transmission—a critical review of various protocols and strategies

ABSTRACT With the advent of 3D video technology and ultra-high-definition (UHD) displays, traditional video coding methods and network/transport protocols seem to fail. This necessitates optimizations at multiple levels and layers: codec-level, system-level, and different layers in the TCP/IP suite. We critically analyze various protocols at different layers in the TCP/IP suite and compare them for various applications. Moreover, the importance of devising intelligent behavior-based strategies at the server side is highlighted. Furthermore, roles and advantages of cross-layer optimization in video transmission are given. The importance of datagram congestion control protocol (DCCP) in an efficient video transmission in the coming years is unfolded. We demonstrate the importance of devising good strategies at the server side in saving the bandwidth. To the best of the knowledge of the authors, this is the first paper that elaborates on DCCP and its significance for the future. In this article, besides analyzing various protocols and discussing optimizations, etc., the value-added suggestions on how to make improvements at various levels are presented. Thus, this work is a one-stop source of information for researchers working on this topic.

An axiomatic perspective on the performance effects of end-host path selection

In various contexts of networking research, end-host path selection has recently regained momentum as a design principle. While such path selection has the potential to increase performance and security of networks, there is a prominent concern that it could also lead to network instability (i.e., flow-volume oscillation) if paths are selected in a greedy, load-adaptive fashion. However, the extent and the impact vectors of instability caused by path selection are rarely concretized or quantified, which is essential to discuss the merits and drawbacks of end-host path selection.In this work, we investigate the effect of end-host path selection on various metrics of networks both qualitatively and quantitatively. To achieve general and fundamental insights, we leverage the recently introduced axiomatic perspective on congestion control and adapt it to accommodate joint algorithms for path selection and congestion control, i.e., multi-path congestion-control protocols. Using this approach, we identify equilibria of the multi-path congestion-control dynamics and analytically characterize these equilibria with respect to important metrics of interest in networks (the “axioms”) such as efficiency, fairness, and loss avoidance. Moreover, we analyze how these axiomatic ratings for a general network change compared to a scenario without path selection, thereby obtaining an interpretable and quantitative formalization of the performance impact of end-host path-selection. Finally, we show that there is a fundamental trade-off in multi-path congestion-control protocol design between efficiency, stability, and loss avoidance on one side and fairness and responsiveness on the other side.

3CP: Coordinated Congestion Control Protocol for Named-Data Networking

Congestion control has a vital role in the prosperity of any network. Thus, designing an effective mechanism for congestion control of the NDN is an active research area. In NDN, congestion control and interest forwarding mechanisms are usually considered an integrated plane for the network traffic management. In this paper, we propose to decouple the forwarding and congestion control planes. We offer a novel architecture for the NDN router in which congestion control and forwarding modules cooperate for interest flow management and congestion control. Based on this framework, we introduce a new explicit feedback-based congestion control protocol (3CP) for managing the consumers’ sending rate in an NDN environment that multi-source and multi-path content delivery is intrinsically supported. 3CP employs a new per-packet feedback computation to inform consumers from the available resource of paths toward repositories. Also, it provides feedback to the forwarding mechanism of a router for adjusting the sending rate of interest packets to each interface. By interest flow management, 3CP controls the traffic of data packets in the network. As a significant achievement, 3CP prevents multi-path flows from acquiring more network resources and could manage fair resource allocation to the flows in each path, whereas the consumers obtain the same throughput. The packet-level simulation was conducted by an NDN simulator, and the results confirmed that 3CP outperforms the existing multi-source/multi-path congestion control mechanisms in NDN.

Balanced Multipath Transport Protocol for Mitigating MPTCP Incast in Data Center Networks

Recent data centers provide dense inter-connectivity between each pair of servers through multiple paths. These data centers offer high aggregate bandwidth and robustness by using multiple paths simultaneously. Multipath TCP (MPTCP) protocol is developed for improving throughput, fairly sharing network link capacity and providing robustness during path failure by utilizing multiple paths over multi-homed data center networks. Running MPTCP protocol for latency-sensitive rack-local short flows with many-to-one communication pattern at the access layer of multi-homed data center networks creates MPTCP incast problem. In this paper, Balanced Multipath TCP (BMPTCP) protocol is proposed to mitigate MPTCP incast problem in multi-homed data center networks. BMPTCP is a window-based congestion control protocol that prevents constant growth of each worker’s subflow congestion window size. BMPTCP computes identical congestion window size for all concurrent subflows by considering bottleneck Top of Rack (ToR) switch buffer size and increasing count of concurrently transmitting workers. This helps BMPTCP to avoid timeout events due to full window loss at ToR switch. Based on current congestion situation at ToR switches, BMPTCP adjust transmission rates of each worker’s subflow so that total amount of data transmitted by all concurrent subflows does not overflow bottleneck ToR switch buffer. Simulation results show that BMPTCP effectively alleviates MPTCP incast. It improves goodput, reduces flow completion time as compared to existing MPTCP and EW-MPTCP protocols.

LearningCC: An online learning approach for congestion control

AbstractIt is noticed that traditional loss based algorithms are not moving data very well in today's Internet. To get bottleneck bandwidth fully utilized, these algorithms will keep increasing the congestion window and maintain a standing queue of packets in routers, which cause large end to end latency. Such long latency is quite annoying to these ever growing delay sensitive applications. Recently, much effort has been devoted by researchers from both academia and industry to design better congestion control protocols and make a better Internet. In this article, we present an online learning based approach named LearningCC for congestion control. Instead of adjusting the congestion window with fixed rule as these traditional algorithms do, there are several options for an endpoint to choose. Each option is mapped as an arm of a bandit machine. The exploration and exploitation scheme is used to guide the selection on congestion window update rule. Through trial and error, an endpoint improves its performance by choosing action with better reward. Experiments are conducted on ns3 platform to verify the effectiveness of LearningCC. Results indicate it achieves lower transmission delay than loss based algorithms. Especially, we found LearningCC makes significant throughput improvement in link suffering from random loss.

DLM technique for QoS improvement in MANETS

Wireless networks are used extensively in communication technologies. From Tesla to date researchers, many are working on wireless data transmission techniques and technologies. Despite the immense progressions over the past decade, there are precise hurdles that the industry endures to face. Bandwidth restrictions, latency problems, and device compatibility issues prevent the viewer from meeting seamless data transmissions. Many devices used for communication are used with the wireless interface and are proficient in transmitting data efficiently to the communication range. The growth in data communication requirements increases the network traffic and results in a network bottleneck. There are more challenges for Mobile Ad hoc Networks (MANETs) due to the additional overhead of resource constraints. Congestion leads to depletion of the node's energy, deterioration of network performance, and increased network latency and packet loss. As a result, energy-efficient and reliable state-of-the-art congestion control protocols must be designed to detect, notify and control congestion effectively. To minimize the packet loss in MANETs using Transmission Control Protocol (TCP), we proposed a data loss minimization technique (DLMT). Results show that enhanced DLMT outperforms by 18% compared to state-of-the-art proven congestion control mechanisms. DLMT improves the Quality of Service (QoS) constraints and improves performance by reducing the delay in better throughput, which can be seen by analyzing experimental results. The proposed coordination process's scalability and robustness are shown in good agreement with simulation results and analytic results for the stochastic model.