Rethinking the rate estimation of BBR congestion control

Wang

et al. 2023

Preprint

Google scientists published Bottleneck Bandwidth and Round-trip propagation time (BBR), a novel TCP congestion control algorithm variant. Unlike traditional congestion control algorithms, BBR adjusts its transmission rates by periodically measuring the network traffic based on round-trip propagation time (RTprop) and available bottleneck bandwidth (Btlbw). This results in BBR achieving higher delivery rates and minimizing the latency on our modern-day network infrastructure. However, some recent experimental evaluations and analyses have shown severe RTT (round-trip time) fairness concerns in the TCP-BBR algorithm. When two competing flows with different RTTs coexist on a shared bottleneck link, BBR tends to prefer and favor longer RTT flows, therefore consuming more bandwidth share than short RTT counterparts, which can result in short RTT flows being starved even to death. This preference nature of the BBR in favor of long RTT flows results in an acute throughput imbalance and an extreme BBR and network vulnerability that malevolent individuals can easily exploit by increasing the delay (RTTs) in their flows to create unwarranted long RTTs on their TCP flows to unfairly obtain and enjoy the lion's share of the available bandwidth than the rest of users on the network. The BBR-With Adaptive Pacing Gain Rates (BBR-APG) algorithm is proposed in this study as an alternative solution to the TCP-BBR's RTT fairness problem. Our proposed approach adaptively adjusts the pacing gain rates of each TCP flow based on the bottleneck inflight queue status during the bandwidth estimation phase instead of the default fixed pacing gain rates as provided by the original version of the BBR algorithm, which only works perfectly when only a single flow is involved. With the BBR-APG algorithm, different RTT flows can fairly compete for the available bottleneck bandwidth, thus enhancing the RTT fairness issue that has been compromised in the original version of the BBR algorithm. Through our extensive simulation analysis on NS3, we identified that our proposed methodology improves RTT fairness by more than 18\%, a reduction of more than 24\% and 20\% on packets retransmission rates and queuing delay, respectively, compared to other recently published BBR variants like an adaptive congestion window of BBR (BBR-ACW) on various network conditions.

Section: Related Workmentioning

confidence: 99%

BBR-With Adaptive Pacing Gain Rates (BBR-APG): An improved TCP-BBR Congestion Control Algorithm on RTT Fairness Design Problem

Wang

et al. 2023

Preprint

“…The related experimental results showed that BBR can significantly improve the throughput of TCP connection, compared with traditional CUBIC [8]. Some scholars have made some modifications to the control parameters in BBR to obtain better performance [9][10][11] and apply it to different network fields [12][13][14]. Moreover, Google is actively upgrading BBR v2 by modifying some parts of the BBR algorithm [15,16].…”

Section: Introductionmentioning

confidence: 99%

Improved RTT Fairness of BBR Congestion Control Algorithm Based on Adaptive Congestion Window

Pan

Tan

et al. 2021

Electronics

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.

“…Literature [ 12 , 13 , 14 ] has evaluated the behavior of BBR and proposed some problems: unfairness in sharing bottleneck between BBR and Reno/CUBIC, the RTT fairness problem of BBR, massive packets retransmission and so on. Aware of the problem of BBR algorithm, some scholars have modified the regulation factors of BBR to improve the defects of BBR itself [ 15 , 16 , 17 ]. On the other hand, BBR is applied to different network domains to achieve a better performance [ 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Improvement of RTT Fairness Problem in BBR Congestion Control Algorithm by Gamma Correction

Pan

Tan

et al. 2021

Sensors

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.