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

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

“…Congestion Control (CC) [3] mechanisms for wired networks were developed in the 1980s and are still used today. There are predefined rules that govern how the Congestion Window (CW) [4] is reduced when packet reduction is identified, and how it is adjusted based on Round-Trip Time (RTT) measurements. Since its inception, the TCP protocol and its variations have been widely used.…”

Section: Fig 1 Types Of MLmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g., Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced at a rapid rate.

“…The retransmission-based effective TCP congestion control method for IoT was introduced in [30]. Bottle-neck bandwidth round trip propagation time improves the fairness of RTT for congestion control was introduced in [31]. The major problems employed in this research are,…”

Section: Problem Statementmentioning

confidence: 99%

Cross-Layer based TCP Performance Enhancement in IoT Networks

Parween¹,

Hussain²

2022

IJACSA

Transmission Control Protocol (TCP) used multiple paths for performing transmission of data simultaneously to improve its performance. However, previous TCP protocols in Internet of Things (IoT) networks experienced difficulty to transmit a greater number of subflows. To overcome the above issues, we introduced cross-layer framework to perform efficient packet scheduling and congestion control for increasing the performance of TCP in IoT networks. Initially, the proposed IoT network is constructed based on grid topology using Manhattan distance which improves the scalability and flexibility of the network. After network construction, packet scheduling is performed by considering numerous parameters such as bandwidth, delay, buffer rate, etc., using fitness based proportional fair (FPF) scheduling algorithm and selecting best subflow to reduce the transmission delay. The scheduled subflow is sent over an optimal path to improve the throughput and goodput. After packet scheduling, congestion control in TCP is performed using cooperative constraint approximation 3 + (CoCoA3 + -TCP) algorithm in which three stages are employed namely congestion detection, fast retransmission, and recovery. The congestion detection in TCP-IoT environment is performed by considering several parameters in which cat and mouse-based optimization (CMO) is utilized to adaptively estimate retransmission timeout (RTO) for reducing the delay and improving the convergence during retransmission. Fast retransmission and recovery are performed to improve the network performance by adjusting the congestion window size thereby avoiding congestion. The simulation of cross-layer approach is carried out using network simulator (NS-3.26) and the simulation results show that the proposed work outperforms high TCP performance in terms of throughput, goodput, packet loss, and transmission delay, jitter, and congestion window size.