BBR Congestion Control Research Articles

Optimization of BBR Congestion Control Algorithm Based on Pacing Gain Model.

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.

Optimizing Data Transport Efficiency in Datacenters through Traffic Shaping of BBR Congestion Control

Compared to commonly used loss-based congestion control algorithms predominantly used in Transmission Control Protocol (TCP) implementations, congestion-based congestion control called BBR has shown much better performance in resource-abundant modern communication links. However, for a high influx of TCP sessions on the bottleneck switch, clusters in High-Performance Compute (HPC) nodes and data centers face resource constraints because of the immense workload during orchestration and relocation of workflows across the resource pool. This article discusses how to resolve this problem, commonly known as TCP incast, through efficient queue management of the bottleneck link and adding a shaper function in the standard BBR algorithm. We analyzed TCP incast issue for two efficient versions of congestion control i.e., BBR and CUBIC (named after the cubic function used instead of linear function), in a highly overloaded convergent switch of the cluster. It is noticeable that the queuing delay and buffer build-up are two essential parameters in causing TCP Incast. Hence, we used the M/G/1/B queuing model when multiple TCP sessions generate the network traffic and different buffer build-up scenarios are analyzed in the bottleneck node of HPC clusters. Based on the findings of our queuing analysis, we propose an incast recovery BBR algorithm that introduces additional controls like Incast shaping to deal with queue build-up during TCP incast. The effects of these modifications in BBR implementation are studied in terms of performance parameters like flow completion time, throughput, RTT variations, and fairness to other competing flows are significant compared with standard BBR and CUBIC implementations.

BBR-With Enhanced Fairness (BBR-EFRA): A new enhanced RTT fairness for BBR congestion control algorithm

Towards the end of 2016, the Google research team proposed and developed a new state-of-the-art TCP congestion control algorithm called Bottleneck Bandwidth and Round-trip propagation time (BBR). When deployed on various Google internal servers, BBR attained higher throughput and low latency performances on modern-day and sophisticated networks than traditional congestion control algorithms like Cubic, Reno, or Vegas. Unlike conventional congestion control algorithms, BBR controls data transmission by maximizing delivery rate and minimizing the round-trip time (RTT), therefore maximizing bandwidth utilization and improving throughput and latency delay performances. However, some experiments have reported persistent queue formation and massive packet retransmissions rate in the bottleneck link, which happens to be the main cause of unfairness between different RTT flows. BBR prefers and favors long RTT over short RTT flows; therefore, long RTT flows are allocated more bandwidth when competing with short RTT flows in the bottleneck link. It was also noted that even the minor difference between the two competing flows could be a source of throughput imbalance and unfairness. The dominance of long RTT flows is the central origin of the high queuing delay, packet loss and retransmission rates, and even severe BBR vulnerability that malicious users can exploit to obtain a larger share of bandwidth simply by increasing the RTT (delay). Therefore, we propose a BBR-With Enhanced Fairness (BBR-EFRA) to mitigate this major concern challenge. Our proposed algorithm adaptively controls the congestion window (CWND) by adjusting the bandwidth delay product (BDP) values of each BBR flow during data transmission based on buffer queue status computation. Our proposed approach guarantees different RTT flows to compete for the available bottleneck bandwidth more equally, hence improving the BBR fairness issue. We evaluated our algorithm on the NS3 simulating environment. BBR-EFRA shows improved RTT fairness by more than 16%, reduced retransmission rate by more than 20%, and queuing delay by more than 18%, and finally increased Jain’s fairness index by more than 1.3 times compared with other recently published BBR variants like an adaptive congestion window of BBR(BBR-ACW). Therefore, with BBR-EFRA, short RTT flows can fairly compete for the available bandwidth even with higher RTT differences under various network scenarios.

A QoS-Based Fairness-Aware BBR Congestion Control Algorithm Using QUIC

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.

Bbr-With Enhanced Fairness (Bbr-Efra): A New Enhanced Rtt Fairness for Bbr Congestion Control Algorithm

Bbr-With Enhanced Fairness (Bbr-Efra): A New Enhanced Rtt Fairness for Bbr Congestion Control Algorithm

Comparison of TCP SIAD and TCP BBR Congestion Control in Simulated 5G Networks

Abstract 5G cellular networks have introduced a completely novel air interface called New Radio (NR). This technology delivers numerous benefits compared to previous generations, including significantly higher peak data rates. However, due to the propagation properties of the frequencies used in NR, the volatility of the available downlink capacity also increases. In this paper, we study two TCP congestion control algorithms which are designed to be able to quickly utilize sudden increases in available capacity. We present an implementation of TCP SIAD in the ns-3 open source network simulator and compare its performance with TCP BBR using the mmWave module of the simulator.

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

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.

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

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.

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

ModQUIC protocol performance verification with CUBIC and BBR congestion control mechanisms

ModQUIC protocol is developed to compete for TCP's dominance. In this work, ModQUIC protocol performance is evaluated using the BBR protocol and suggested a new decrease factor (β) for CUBIC protoc...

Rethinking the rate estimation of BBR congestion control

The acknowledgment (ACK) compression makes the available bandwidth estimation in bottleneck bandwidth and round-trip propagation time (BBR) inaccurate which results in the high queuing delay under low packet loss rate environments. The additional queuing delay affects the user experience of interactive applications like Skype. To solve the above-mentioned issue, this Letter presents a novel method, named calibrating bandwidth estimation (CBE), to calibrate the bandwidth estimation. The CBE can detect the implausible ACK rates accurately and the extensive simulation prove the effectiveness of CBE.

Delay-Aware BBR Congestion Control Algorithm for RTT Fairness Improvement

In late 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm to achieve high bandwidth and low latency for Internet traffic. Unlike loss-based congestion control algorithms, BBR works without filling the bottleneck buffer. Consequently, BBR can reduce packet loss and minimize end-to-end packet delay, which has attracted the attention of many researchers in recent years. However, some studies have reported the creation of persistent queues that cause unintended problems, resulting in a serious fairness issue between TCP BBR flows with different round-trip times (RTTs). Although existing congestion control algorithms also exhibit fairness issue between different RTT flows, BBR has a more serious problem in that the imbalance is considerable even with small RTT difference between the two flows, and the long RTT flow uses most of the bandwidth. The preponderance of long RTT flows is a serious problem because a particular user may cause imbalance by maliciously increasing the delay. Therefore, we propose a Delay-Aware BBR (DA-BBR) congestion control algorithm to mitigate the RTT fairness issue between BBR flows. In a network emulation experiment using the Mininet, the DA-BBR increased the fairness index by 1.6 times that of the original BBR, and the retransmission was greatly reduced. DA-BBR flow with short RTT demonstrated fair throughput even in competition with DA-BBR flows where RTT is five times higher.

BBR 혼잡제어 알고리즘 표준화 및 연구 동향 분석

현재 인터넷상에서 이용되는 웹 응용들의 90% 이상의 트래픽이 전송계층 프로토콜로 TCP를 사용하고 있다. 구글은 2016년 말 웹 응용들의 네트워크 속도 향상을 위한 새로운 혼잡제어 알고리즘으로 BBR (Bottleneck Bandwidth and Round-trip propagation time)을 제안하였다. BBR은 손실기반 혼잡제어 알고리즘과는 달리 병목링크의 버퍼를 가득 채우지 않으며 동작하기 때문에 패킷 손실을 줄이고 전송지연을 최소화할 수 있어 최근 많은 연구자들의 관심을 끌고 있다. 본 고에서는 BBR 알고리즘에 대한 동작 특성과 최신 표준화 동향을 살펴보고 연구개발 동향 및 이에 대한 현재의 연구 이슈를 고찰한다.

Congestion Control and Packet Scheduling for Multipath Real Time Video Streaming

Real time video traffic has stringent delay requirement and is bandwidth demanding when streaming over the internet. Single path transmission may provide unsatisfactory picture quality to users due to packet loss, jitter, and low bandwidth. Multipath transmission is another option to achieve better service by aggregating bandwidth and exploiting path diversity. As utility maximization theory shows, to achieve better satisfaction is to provide higher throughput and lower frame delivery delay for real time video traffic. The congestion control is responsible to probe maximum available bandwidth and packet scheduling is to take advantages of path diversity, which are two key research points in multipath context and are also the main concern in this paper. Given the BBR congestion control algorithm causes high-packet loss rate and large transmission delay when BBR flows competing for bandwidth resource, a delay response BBR (Delay-BBR) algorithm is proposed for real time video transmission. The main idea is that the sender will actively reduce the rate to let intermediate routers drain the excess occupied buffer once round trip delay signal exceeds a certain threshold. The results show that the delay-BBR can achieve lower transmission delay and lower packet loss rate when compared with QUIC-BBR and WebRTC-BBR. The rate stable feature makes delay-BBR appropriate for real time video transmission. Moreover, a packet scheduling algorithm works in coordination with the rate control algorithm achieves lower frame delivery delay further compared with the benchmark algorithms.

Space Environment Data Transfer System Based on BBR Congestion Control Algorithm

Space Environment Data Transfer System Based on BBR Congestion Control Algorithm