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

Abstract

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.

Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times

“…The problem of BBR unfairness and lossiness has been widely studied in the literature [8][9][10][11]15]. Soon after releasing the first version, it became clear that BBR was responsible for excessive packet loss, especially in shallow buffers, and high queuing variation in large buffers.…”

“…Additionally, BBRv2 actively monitors packet losses or ECN rates, thus preventing excessive loss ratios. The second iteration solved the problem of high loss and retransmission ratios; unfortunately, it could not provide fair bandwidth sharing in the presence of other BBR flows with different RTTs and loss-based TCP flows [14,15].…”

Toward Fair and Efficient Congestion Control: Machine Learning Aided Congestion Control (MLACC)

“…They are better suited for high-speed and dynamic networks, like wireless networks, since they are not influenced by random packet loss. However, accurately measuring the transmission delay still poses a significant challenge [14] along with the other problems mentioned in Section 1 regarding fairness, link overutilization, and significant packet loss [16].…”

When BBR beats FEC coded CUBIC flows over SATCOM

“…BBRv1 was introduced by Cardwell et al [22]. In 2019, the same authors introduced BBRv2 [23], [24], a fairer version of BBRv1 [25]. BBR assesses maximum available bandwidth and the minimum measured RTT to determine the congestion window size.…”