Improving performance of backpressured packet networks by integrating with an end-to-end congestion control algorithm

End-to-end rate-based congestion control with random loss: convergence and stability

Researches have shown that self similar traffic has essential effects on the network parameters such as loss rate, length of the queue in the routers and throughput. Since classical congestion control algorithms work in short time scales and do not consider self similarity and long time dependency phenomenon of data, development of congestion control algorithms which be able to distinguish and manage of such traffic have been very important to researchers. Multiple time-scale congestion control is a method which has high performance reliance co-efficiency. In this research congestion control in large time scales has been implemented in fuzzy form. Simulation results show that fuzzy congestion controller will have better performance in comparison with a non-fuzzy two time-scale controller, if fuzzy rules are selected correctly.

An INT-based TCP window modulator for congestion control in data center networks

Channel congestion is one of the most critical issues in IEEE 802.11p-based vehicular communications as it leads to the unreliability of safety applications. As a countermeasure, many Decentralized Congestion Control (DCC) algorithms have been proposed. One of the most prominent DCC algorithms is the message-rate based LIMERIC. Recently, algorithms have also been proposed to support higher vehicle density (better scalability). One of such algorithms is the combined message-rate and data-rate based congestion control algorithm (MD-DCC). MD-DCC can support around 2.7 times higher vehicular density than LIMERIC. However, if LIMERIC has been deployed, can MD-DCC be introduced and coexist well with LIMERIC. The objective of this paper is to investigate how MD-DCC coexists with LIMERIC. Given a scenario where vehicles may use either LIMERIC or MD-DCC, we study the impact of coexistence on channel load, fairness and reliability of vehicles at different densities. Simulation studies show that there is no significant degradation of reliability both for LIMERIC and MD-DCC at different densities. On the contrary, coexistence can improve the reliability of LIMERIC vehicles. Furthermore, MD-DCC can support vehicles at large densities even when it coexists with LIMERIC retaining its scalability. However, fair allocation of resources is not guaranteed when LIMERIC and MD-DCC coexist.

In the past few decades, many Transmission Control Protocol (TCP) congestion control algorithms have been investigated to meet the growing network demands and to enhance the performance of TCP in lossy or high-bandwidth-delay-product (high-BDP) networks. However, it is still challenging to implement a dynamic congestion control algorithm for wide range of diverse mobile users, network conditions and applications. This paper explores avenues for enhancement of TCP congestion control algorithm for next generation mobile networks by dynamically learning the available bandwidth and deriving the Congestion Control Factor N. N is used to Adaptive Increase/Adaptive Decrease (AIAD) the Congestion Window (CWND) dynamically instead of using the traditional approach that is Additive Increase/ Multiplicative Decrease (AIMD) paradigm. Once there is congestion, our proposed algorithm will not allow the CWND to decrease multiplicatively or steeply. After dropping to a certain level (lower than legacy), we try to take the CWND to previous state adaptively with the help of calculated bandwidth (based on learning). This in turn helps to efficiently control the CWND for better network utilization especially in case of lossy and high-BDP conditions. As soon as it reaches the original state, it remains stable for longer time as compared to legacy until packet loss or time out. We demonstrate the effectiveness of our algorithm with the help of live air experiments (performed in Samsung R&D India, Bangalore) and NS3 based simulation experiments. Through our experiments, we show that our algorithm outperforms the legacy congestion control algorithms (like CUBIC, RENO, TCPW) and the existing CLTCP algorithm in terms of goodput, intra algorithm fairness and inter algorithm fairness maintaining the scalability and friendliness.

With the rapid development of computer network, today’s network structure has become very complex. The promotion of Wi-Fi and the widespread use of mobile terminals such as cell phone, laptop and smart watch, have made wireless access become the main way of Internet surfing. Conventional congestion control algorithms are based on packet loss to perceive network congestion like NewReno, CUBIC. On Wi-Fi scenarios, last-mile delivery is wireless, and it may cause random loss. In this case, these congestion control algorithms based on packet loss perception can not distinguish the network congestion correctly and maybe reduce the congestion window when congestion doesn’t occur. To solve this problem, we propose optimization of these congestion control algorithms, which can be intelligently aware of packet loss and improve TCP transmission performance. We evaluated TCP transmission performance on wired network and wireless network. The results show that the performance of our method is similar to that of the conventional congestion control algorithms in wired environment, while the bandwidth utilization is significantly improved on wireless network. In addition, our work achieves great competition fairness using logical analysis.KeywordsTCPCongestion control algorithmNewRenoCUBICWireless network

Congestion control (CC) is an indispensable component of transport protocols to prevent congestion collapse as it distributes the available bandwidth among all competing flows, ideally in a fair manner. It thus has a large impact on performance and there exists a constantly evolving set of CC algorithms, each addressing different performance needs. While the algorithms are commonly tested regarding the problems underlying their implementation, the interaction with existing algorithms is often not considered. Additionally considering the fact that content providers (CPs) such as content distribution networks (CDNs) are known to tune TCP stacks for performance gains, the large assortment of algorithms opens the door for custom parametrization and potentially unfair bandwidth sharing. In this paper, we thus empirically investigate if current Internet traffic generated by CPs still adheres to the conventional understanding of fairness. For this, we compare fairness properties of testbed hosts to actual traffic of six major CPs subject to different queue sizes and queueing disciplines in a home-user setting. Additionally, we investigate how mice and elephant flows from the different CPs interact. We find that some employed CC algorithms lead to significantly asymmetric bandwidth shares and very poor flow completion times for mice flows. Fortunately, AQMs such as FQ_CoDel are able to alleviate such unfairness.

To alleviate the lower performance of Transmission Control Protocol (TCP) congestion control over complex network, especially the high latency and packet loss scenario, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm. In contrast with other TCP congestion control algorithms, BBR adjusted transfer data by maximizing delivery rate and minimizing delay. However, some evaluation experiments have shown that the persistent queues formation and retransmissions in the bottleneck can lead to serious fairness issues between BBR flows with different round-trip times (RTTs). They pointed out that small RTT differences cause unfairness in the throughput of BBR flows and flows with longer RTT can obtain higher bandwidth when competing with the shorter RTT flows. In order to solve this fairness problem, an adaptive congestion window of BBR is proposed, which adjusts the congestion window gain of each BBR flow in network load. The proposed algorithms alleviate the RTT fairness issue by controlling the upper limit of congestion window according to the delivery rate and queue status. In the Network Simulator 3 (NS3) simulation experiment, it shows that the adaptive congestion window of BBR (BBR-ACW) congestion control algorithm improves the fairness by more than 50% and reduces the queuing delay by 54%, compared with that of the original BBR in different buffer sizes.

Congestion control is a fundamental technology to balance the traffic load and the network. The Internet Engineering Task Force (IETF) Quick UDP Internet Connection (QUIC) protocol has flexible congestion control and at the same time possesses the advantages of high efficiency, low latency, and easy deployment at the application layer. Bottleneck bandwidth and round‐trip propagation time (BBR) is an optional congestion control algorithm adopted by QUIC. BBR can significantly increase throughput and reduce latency, in particular over long‐haul paths. However, BBR results in high packet loss in low bandwidth and low fairness in multi‐stream scenarios. In this article, we propose the enhanced BBR congestion control (eBCC) algorithm, which improves the BBR algorithm in two aspects: (1) 10.87% higher throughput and 74.58% lower packet loss rate in the low‐bandwidth scenario and (2) 8.39% higher fairness in the multi‐stream scenario. This improvement makes eBCC very suitable for IoT communications to provide better QoS services.

Congestion control enables network nodes to adopt certain approaches to avoid network congestion as much as possible, and respond when network congestion occurs. BBR is a transmission rate-based congestion control algorithm proposed by Google for Chromium QUIC and Linux kernel deployment. The new transmission protocol QUIC proposed by Google has a better congestion control mechanism than TCP. Based on the existing BBR congestion control algorithm, this paper proposes the IBCCA congestion control algorithm and evaluates the performance of the IBCCA, CUBIC, and BBR algorithms under the QUIC protocol in wireless LAN. When the network traffic burst flow is small or the burst duration is short, IBCCA achieves higher network goodput and lower transmission round-trip time. In the experiment of multi-stream transmission, the IBCCA algorithm achieves higher fairness while the good-put and RTT are at the same level. The BBR algorithm achieves a smaller RTT, and the goodput of the IBCCA algorithm is not much different from the CUBIC algorithm.

In 2016, Google proposed the bottleneck bandwidth round-trip propagation time (BBR) as a new congestion control algorithm for improving network speed. As BBR has different behavioral characteristics from loss-based congestion control, it must ensure the coexistence between congestion control algorithms. However, throughput imbalance has been reported in a competition between BBR and loss-based congestion control. In this paper, we propose an opponent congestion control identification model of BBR (OI-BBR) with machine learning to improving inter-protocol fairness. In the emulation experiments, we confirmed that OI-BBR based on the proposed model improved the inter-protocol fairness by operating differently according to the opponent congestion control algorithms.

Space-ground integrated networks (SGINs) have been recently regarded as a promising way to provide resilient, dependable as well as efficient data transmission in the high-speed railway (HSR) scenario. Applying multipath transmission control protocol (MPTCP) to SGIN can realize data transmission simultaneously via terrestrial and satellite networks. However, since the existing congestion control (CC) mechanisms of MPTCP fail to distinguish between adverse influences (such as packet loss and/or round-trip time increase) caused by congestion and those caused by handovers, it suffers severe performance degradation in the HSR scenario where handover frequently occurs. In this article, we first present the SGIN oriented HSR (SGIN-HSR) with MPTCP. Then leveraging cross-layer information (i.e., reference signal received power), we design a novel cross-layer aided MPTCP CC mechanism targeted at SGIN-HSR based on deep reinforcement learning, which is referred to as HSR-CC, to alleviate performance degradation problems induced by handover. The experimental results show that HSR-CC significantly enhances the goodput and outperforms state-of-the-art MPTCP CC algorithms in SGIN-HSR environments where handover frequently occurs.

ABSTRACTAlthough delay‐based congestion control protocols such as FAST Transmission Control Protocol (TCP) promise to deliver better performance than traditional TCP Reno, they have not yet been widely incorporated to the Internet. Several factors have contributed to their lack of deployment. Probably, the main contributing factor is that they are not able to compete fairly against loss‐based congestion control protocols. In fact, the transmission rate in equilibrium of delay‐based approaches is always less than their fair share when they share the network with traditional TCP Reno derivatives that employ packet losses as their congestion signal. There are also other performance impairments caused by the sensitivity to errors in the measurement of the congestion signal (queuing delay) that reduce the efficiency and the intra‐protocol fairness of the algorithms. In this paper, we report, analyse and discuss some recent proposals in the literature to improve the dynamic behaviour of delay‐based congestion control algorithms, FAST in particular. The coexistence of sources reacting differently to congestion, the identification of congestion appearance in the reverse path and thepersistent congestionproblem are the issues specifically addressed. Copyright © 2011 John Wiley & Sons, Ltd.

A single flow stream could be divided into several sub-flows to be sent across multiple paths. This method obtains obvious advantages against traditional TCP as it maintains higher reliability and makes bettaer use of the network resources. Congestion control algorithm is an essential part to be considered. This paper first reviews the existing multipath TCP congestion control algorithms and then analyzes and formulates the goals and problems need to be achieved and solved. A delay-based congestion control algorithm named Weighted Vegas (wVegas) is provided. Finally, two possible modifications are demonstrated including adjusting the congestion control window according to how far the path is from congestion and redefining the behavior when loss occurs.

Chapter 5 - Congestion Control in High-Speed Networks