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In 2016, Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR). The BBR congestion control algorithm measures the network bottleneck bandwidth and minimum delay in real-time to calculate the bandwidth delay product (BDP) and then adjusts the transmission rate to maximize throughput and minimize latency. However, relevant research reveals that BBR still has issues such as RTT unfairness, high packet loss rate, and deep buffer performance degradation. This article focuses on its most prominent RTT fairness issue as a starting point for optimization research. Using fluid models to describe the data transmission process in BBR congestion control, a fairness optimization strategy based on pacing gain is proposed. Triangular functions, inverse proportional functions, and gamma correction functions are analyzed and selected to construct the pacing gain model, forming three different adjustment functions for adaptive adjustment of the transmission rate. Simulation and real experiments show that the three optimization algorithms significantly improve the fairness and network transmission performance of the original BBR algorithm. In particular, the optimization algorithm that employs the gamma correction function as the gain model exhibits the best stability.

Google proposed the bottleneck bandwidth and round-trip propagation time (BBR), which is a new congestion control algorithm. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. However, some studies have shown that there are serious RTT fairness problems in the BBR algorithm. The flow with longer RTT will consume more bandwidth and the flows with shorter RTT will be severely squeezed or even starved to death. Moreover, these studies pointed out that even small RTT differences will lead to the throughput of BBR flows being unfair. In order to solve the problem of RTT fairness, an improved algorithm BBR-gamma correction (BBR-GC) is proposed. BBR-GC algorithm takes RTT as feedback information, and then uses the gamma correction function to fit the adaptive pacing gain. This approach can make different RTT flows compete for bandwidth more fairly, thus alleviating the RTT fairness issue. The simulation results of Network Simulator 3 (NS3) show that that BBR-GC algorithm cannot only ensure the channel utilization, but also alleviate the RTT fairness problem of BBR flow in different periods. Through the BBR-GC algorithm, RTT fairness is improved by 50% and the retransmission rate is reduced by more than 26%, compared with that of the original BBR in different buffer sizes.

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.

Ad hoc networks are characterized by flexible deployment and multi-hop communication, which has facilitated their growing prevalence in diverse applications. However, the TCP protocol exhibits substantial performance degradation in multi-hop ad hoc networks with dynamic topologies. To address this issue, this paper proposes TCP-RLA, a lightweight adaptive reinforcement learning-based TCP congestion control algorithm. It predicts network state variations and leverages a deep Q-network (DQN) with a rule-assisted discrete action space to adaptively tune the congestion window. This design boosts convergence speed and reduces computational complexity, making it well-suited for resource-constrained ad hoc nodes. Simulation results demonstrate that, compared with two reinforcement learning-based algorithms (GVegas and Orca), TCP-RLA achieves an average throughput improvement of 36.1% and 43.3%, an average round-trip time (RTT) reduction of 13.1% and 47.9%, and an average packet loss rate (PLR) reduction of 33.3% and 50%, respectively.

The rapid development of wireless network technology and the continuous evolution of network service demands have raised higher requirements for congestion control algorithms. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm based on the Transmission Control Protocol (TCP) protocol. While BBR offers lower latency and higher throughput compared to traditional congestion control algorithms, it still faces challenges. These include the periodic triggering of the ProbeRTT phase, which impairs data transmission efficiency, data over-injection caused by the congestion window (CWND) value-setting policy, and the difficulty of coordinating resource allocation across multiple concurrent flows. These limitations make BBR less effective in multi-stream competition scenarios in high-speed wireless networks. This paper analyzes the design limitations of the BBR algorithm from a theoretical perspective and proposes the Adaptive-BBR (Ad-BBR) algorithm. The Ad-BBR algorithm incorporates real-time RTT and link queue-state information, introduces a new RTprop determination mechanism, and implements a finer-grained, RTT-based adaptive transmission rate adjustment mechanism to reduce data over-injection and improve RTT fairness. Additionally, the ProbeRTT phase-triggering mechanism is updated to ensure more stable and smoother data transmission. In the NS3, 5G, and Wi-Fi simulation experiments, Ad-BBR outperformed all comparison algorithms by effectively mitigating data over-injection and minimizing unnecessary entries into the ProbeRTT phase. Compared to the BBRv1 algorithm, Ad-BBR achieved a 17% increase in throughput and a 30% improvement in RTT fairness, along with a 13% reduction in the retransmission rate and an approximate 20% decrease in latency.

An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. In a Wi-Fi network, a Wi-Fi client typically uses the Transmission Control Protocol (TCP) for its applications. Hence, it is essential to understand and quantify the TCP performance in such an environment. This work presents an emulator-based approach for investigating the TCP performance in Wi-Fi networks in a time- and cost-efficient manner. We introduce a new platform, which leverages the Mininet-WiFi emulator to construct various Wi-Fi networks for investigation while considering actual TCP implementations. The platform uniquely includes tools and scripts to assess TCP performance in the Wi-Fi networks quickly. First, to confirm the accuracy of our platform, we compare the emulated results to the results in a real Wi-Fi network, where the bufferbloat problem may occur. The two results are not only similar but also usable for finding the bufferbloat condition under different methods of TCP congestion control. Second, we conduct a similar evaluation in scenarios with the Wi-Fi link as a bottleneck and those with varying signal strengths. Third, we use the platform to compare the fairness performance of TCP congestion control algorithms in a Wi-Fi network with multiple clients. The results show the efficiency and convenience of our platform in recognizing TCP behaviors.

TCP congestion control adjusts the sending rate in order to protect Internet from the continuous traffic and ensure fair coexistence among multiple flows. Especially, loss-based congestion control algorithms were mainly used, which worked relatively well for past Internet with low bandwidth and small bottleneck buffer size. However, the modern Internet uses considerably more sophisticated network equipment and advanced transmission technologies, and loss-based congestion control can cause performance degradation due to excessive queueing delay and packet loss. Therefore, Google introduced a new congestion control in 2016, Bottleneck Bandwidth Round-trip propagation time (BBR). In contrast with traditional congestion control, BBR tries to operate at the Kleinrock’s optimal operating point, where delivery rate is maximized and latency is minimized. However, when BBR and loss-based congestion control algorithms coexist on the same bottleneck link, most of bottleneck bandwidth is occupied by flows that use a particular algorithm, and excessive packet retransmission can occur. Therefore, this paper proposes a BBR congestion window scaling (BBR-CWS) scheme to improve BBR’s inter-protocol fairness with a loss-based congestion control algorithm. Through Mininet experiment results, we confirmed that fairness between BBR-CWS and CUBIC improved up to 73% and has the value of 0.9 or higher in most bottleneck buffer environments. Moreover, the number of packet retransmissions was reduced by up to 96%, compared to the original BBR.

The traffic within data centers exhibits bursts and unpredictable patterns. This rapid growth in network traffic has two consequences: it surpasses the inherent capacity of the network’s link bandwidth and creates an imbalanced network load. Consequently, persistent overload situations eventually result in network congestion. The Software Defined Network (SDN) technology is employed in data centers as a network architecture to enhance performance. This paper introduces an adaptive congestion control strategy, named DA-DCTCP, for SDN-based Data Centers. It incorporates Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) to establish congestion awareness and an ECN marking model. To mitigate incorrect congestion caused by abrupt flows, an appropriate ECN marking is selected based on the queue length and its growth slope, and the congestion window (CWND) is adjusted by calculating RTT. Simultaneously, the marking threshold for queue length is continuously adapted using the current queue length of the switch as a parameter to accommodate changes in data centers. The evaluation conducted through Mininet simulations demonstrates that DA-DCTCP yields advantages in terms of throughput, flow completion time (FCT), latency, and resistance against packet loss. These benefits contribute to reducing data center congestion, enhancing the stability of data transmission, and improving throughput.

New versions of HTTP protocols have been developed to overcome many of the limitations of the original HTTP/1.1 protocol and its underlying transport mechanism over TCP. In this paper, we investigated the performance of modern Internet protocols such as HTTP/2 over TCP and HTTP/3 over QUIC in high-latency satellite links. The goal was to uncover the interaction of the new features of HTTP such as parallel streams and optimized security handshake with modern congestion control algorithms such as CUBIC and BBR over high-latency links. An experimental satellite network emulation testbed was developed for the evaluation. The study analyzed several user-level web performance metrics such as average page load time, First Contentful Paint and Largest Contentful Paint. The results indicate an overhead problem with HTTP/3 that becomes more significant when using a loss-based congestion control algorithm such as CUBIC which is widely used on the Internet. Also, the results highlight the significance of the web page structure and how objects are distributed in it. Among the various Internet protocols evaluated, the results show that HTTP/3 over QUIC will perform better by an average of 35% than HTTP/2 over TCP in satellites links specifically with a more aggressive congestion algorithm such as BBR. This can be attributed to the non-blocking stream multiplexing feature of QUIC and the reduced TLS handshake of HTTP/3.

On a connected car, the performance of Internet access will significantly affect the user experience. For electric cars that use vehicle-to-grid (V2G) communication to interact with the Internet during charging, the charge cable quality poses a challenge to the V2G communication. Specifically, the performance of Transmission Control Protocol (TCP), the transport protocol that most Internet applications use, may suffer due to the high noise and consequent errors that the charge cable presents. Currently, TCP NewReno is the TCP implementation that ISO 15118 standard stipulates for the V2G communication. However, its congestion control algorithm has been designed for the general Internet environment where congestion, not link errors, account for most of packet losses. Indeed, we confirm that the throughput of TCP NewReno rapidly degrades as the error rate increases on the charge cable. Specifically, we show that other TCP variants such as TCP Illinois far exceeds TCP NewReno in both lossy and non-lossy link environments. Finally, we propose how to configure TCP NewReno parameters to make it achieve the throughput comparable to other TCP variants on V2G communication environments, regardless of the link quality presented by the charging cable.