Delay-Aware BBR Congestion Control Algorithm for RTT Fairness Improvement

Kim, Geon-Hwan; Cho, You Ze

doi:10.1109/access.2019.2962213

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…A variant of BBR, called Modest-BBR, modifies BBR, reducing its aggressiveness and increasing fairness with Cubic while still maintaining similar performance to the original BBR [10]. Another variant of BBR called, instead, DA-BBR, focuses on the RTT-fairness achieving fair throughput between short-RTT flows and long-RTT flows where RTT is five times higher [16]. DA-BBR works on top of the BBQ algorithm, which has been the first attempt to address the problem of RTT fairness of the original BBR [15].…”

Section: Related Workmentioning

confidence: 99%

“…The core feature of BBR is to be model-based, and it behaves following a state-machine composed by four states: Sturtup, Drain, ProbeBW and ProbeRTT. The first two states belong to the initial part of a connection, then BBR moves to a steady-state phase composed by the last two states [16]. The input for the state machine are the last the amount of data that can be enqueued, limited by the congestion window size and, in particular, by the TSQ policy, and then the data is delivered to the lower layers pacing it at the current pacing rate.…”

Section: Stackmentioning

confidence: 99%

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BBRp: Improving TCP BBR Performance Over WLAN

2020

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This paper shows the inefficiency of TCP BBR in exploiting the Wi-Fi bandwidth. This limitation of BBR has been observed with both IEEE 802.11n and IEEE 802.11ac, where the mechanism of frame aggregation is used to boost the throughput of data transmission. In the last years, many TCP variants have been introduced to limit the bufferbloat phenomena and bound the latency through a reduction of the queue backlog injection rate. However, this mechanism impacts on the Wi-Fi frame aggregation logic, impeding TCP congestion controls to reach the full throughput potential of a Wi-Fi interface. While this problem can be solved with TCP Cubic by allowing the sender node to enqueue more packets, for TCP BBR the fix is not the same, as it has a customized pacing algorithm. With this contribution we propose BBRp, a new BBR version that allows for fine-tuning the congestion control pace, achieving between four and six times more throughput over IEEE 802.11n and IEEE 802.11ac channels, at the cost of an increased latency that is however always less than the latency obtainable with loss-based TCP congestion controls.

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Section: Related Workmentioning

confidence: 99%

Section: Stackmentioning

confidence: 99%

BBRp: Improving TCP BBR Performance Over WLAN

2020

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“…Several papers [38]- [42], [45] deal with problems that can occur when comparing between CUBIC and BBR, and studies to solve them have been introduced [43], [44], [46]- [49]. Furthermore, several studies introduced the causes and solutions to the RTT fairness problem in BBR where high RTT flows occupy more bandwidth [50]- [55]. Moreover, efforts have been made to apply BBR, which is based on the model, not the loss, to multipath TCP [56]- [58].…”

Section: B Related Workmentioning

confidence: 99%

Understanding of BBRv2: Evaluation and Comparison With BBRv1 Congestion Control Algorithm

et al. 2021

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Google proposed a bottleneck bandwidth round-trip propagation time (BBR) for TCP to replace the loss-based congestion control algorithms, such as Reno and CUBIC. Unlike the loss-based algorithms, BBR models a network path from source to destination and dynamically controls the sending rate using control parameters, such as pacing rate, congestion window, and quantum to achieve high throughput and low latency. However, many studies have reported performance issues in BBR operation, such as excessive packet loss in shallow buffers, the unfairness among different RTT flows, the unfairness with loss-based algorithms, and so on. Google is developing a new version of BBR, BBRv2, to resolve these performance issues. In this study, we evaluate and compare two versions of BBR on a Mininet emulator and a physical testbed, focusing on whether the BBRv2 alpha can alleviate the performance issues of BBRv1 and whether other novel issues will arise in BBRv2. The experiment results show that BBRv2 improves the unfairness and aggressiveness in small buffer less than 1 BDP. Moreover, multiple BBRv2 flows not only show better fairness in bandwidth sharing, but also reduce the amount of packet retransmissions. However, we observed that the challenging issues such as RTT unfairness, coexistence with loss-based algorithms, and synchronization between BBRv2 flows still exist. This study explores BBRv2's current behavior in various network scenarios and compares the performance of BBRv2 with the BBRv1 congestion control algorithm. INDEX TERMSTCP congestion control, BBRv1, BBRv2, fairness, retransmission. Ph.D. degrees in electronics engineering from Kyungpook National University, in 2015 and 2019, respectively. He is currently working as a Postdoctoral Research Fellow with the Telecommunication and Network Laboratory, Kyungpook National University. His current research interests include multipath TCP, TCP congestion control algorithms, and UAVs path planning and network constraints.

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“…Because BBR is still in the development stage, fairness between BBR algorithm and other loss-based CCAs [ 26 , 27 , 28 , 29 ] and RTT fairness [ 12 , 30 , 31 , 32 , 33 , 34 ] need to be improved. In the above analysis, one of the most serious problems of BBR is the RTT fairness problem in the protocol.…”

Section: Introductionmentioning

confidence: 99%

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

Pan

Tan

et al. 2021

Sensors

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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.

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Delay-Aware BBR Congestion Control Algorithm for RTT Fairness Improvement

Cited by 31 publications

References 16 publications

BBRp: Improving TCP BBR Performance Over WLAN

BBRp: Improving TCP BBR Performance Over WLAN

Understanding of BBRv2: Evaluation and Comparison With BBRv1 Congestion Control Algorithm

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

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