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Home/ Blog/ Empirical comparison of TCP BBR congestion control algorithms for Google proxy website

Empirical comparison of TCP BBR congestion control algorithms for Google proxy website

PYPROXY PYPROXY · May 28, 2025

The TCP BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm, developed by Google, is designed to enhance the performance of network connections. It aims to optimize the bandwidth utilization and reduce latency, providing a more efficient way to manage network traffic. In this article, we will conduct a practical comparison of the TCP BBR congestion control algorithm as implemented on Google proxy websites, measuring its effectiveness in real-world scenarios. The comparison will include performance metrics such as throughput, latency, and packet loss, offering valuable insights into how BBR performs under different network conditions. The results will help network engineers, developers, and businesses make informed decisions about adopting this algorithm for their systems.

Introduction to Congestion Control Algorithms

Network congestion control is an essential aspect of modern internet communications. The role of congestion control algorithms is to prevent network congestion and optimize the flow of data between devices. Traditional congestion control algorithms, such as TCP Reno and TCP Cubic, primarily focus on packet loss as the key signal for congestion. These algorithms slow down the data transmission rate when packet loss occurs, which may not be the most efficient approach, especially in environments where packet loss is not a reliable indicator of congestion.

In contrast, Google's TCP BBR algorithm takes a different approach by focusing on bottleneck bandwidth and round-trip propagation time as key factors for adjusting the transmission rate. By continuously estimating the available bandwidth and round-trip time, BBR aims to maintain the optimal flow of data, improving both throughput and latency.

TCP BBR Algorithm: A Closer Look

TCP BBR was introduced by Google as a solution to the inefficiencies found in traditional congestion control algorithms. Unlike algorithms that rely on packet loss to infer congestion, BBR dynamically adjusts the sending rate based on real-time measurements of available bandwidth and network round-trip time. This proactive approach allows BBR to optimize network utilization and minimize delays.

The core mechanism behind TCP BBR involves three key components:

1. Bandwidth Estimation: BBR constantly estimates the available bottleneck bandwidth of the network path, which is the maximum rate at which data can be sent without causing congestion.

2. Round-trip Time Estimation: The algorithm also tracks the round-trip time (RTT), which is the time taken for a packet to travel from the sender to the receiver and back. A lower RTT typically indicates a more efficient network path.

3. Sending Rate Adjustment: Based on the bandwidth and RTT estimates, BBR adjusts the sending rate to optimize throughput and reduce latency, avoiding congestion and packet loss.

Practical Comparison: TCP BBR vs. Traditional Algorithms

In this section, we will compare the performance of TCP BBR with traditional congestion control algorithms such as TCP Reno and TCP Cubic. The comparison will focus on several key performance indicators, including throughput, latency, and packet loss.

1. Throughput: Throughput refers to the amount of data successfully transmitted over a network in a given period. Traditional algorithms like TCP Reno and TCP Cubic may suffer from underutilizing the available bandwidth due to their reliance on packet loss as the congestion signal. In contrast, TCP BBR continuously estimates the available bandwidth, allowing it to better match the transmission rate to the network's capacity. As a result, BBR typically achieves higher throughput, especially in high-bandwidth, low-latency environments.

2. Latency: Latency is the time it takes for a packet to travel from the sender to the receiver. High latency can negatively affect user experience, especially in applications that require real-time communication, such as video conferencing and online gaming. While traditional algorithms tend to increase latency in response to packet loss, TCP BBR focuses on minimizing latency by adjusting the sending rate based on real-time measurements. This approach results in lower latency, even in networks with variable conditions.

3. Packet Loss: Packet loss occurs when data packets are dropped during transmission, usually due to congestion in the network. Traditional congestion control algorithms rely on packet loss to signal congestion and reduce the sending rate. However, this can lead to unnecessary reductions in throughput. TCP BBR, on the other hand, is designed to avoid packet loss by proactively adjusting the sending rate before congestion occurs. This results in lower packet loss rates compared to traditional algorithms.

Real-World Performance: Case Studies

To illustrate the effectiveness of TCP BBR in real-world scenarios, we will explore several case studies comparing its performance to traditional congestion control algorithms.

1. High-Bandwidth Network: In high-bandwidth networks with low latency, such as fiber-optic connections, TCP BBR outperforms traditional algorithms by achieving higher throughput and lower latency. In these environments, the traditional algorithms often underutilize the available bandwidth, resulting in lower performance. BBR, on the other hand, adjusts the sending rate to match the network's capacity, maximizing throughput while minimizing latency.

2. Congested Networks: In networks with high congestion and packet loss, traditional algorithms like TCP Reno may struggle to maintain high throughput due to their reliance on packet loss as a congestion signal. TCP BBR, however, avoids packet loss by proactively adjusting the sending rate, maintaining a stable connection even in congested conditions.

3. Mobile Networks: Mobile networks, with their variable bandwidth and latency, present a unique challenge for congestion control algorithms. TCP BBR has shown promise in these environments by adjusting the sending rate based on real-time bandwidth and RTT estimates. This enables BBR to maintain a more stable connection and reduce latency, providing a better user experience for mobile users.

Conclusion: The Future of Congestion Control

The TCP BBR algorithm represents a significant advancement in congestion control, offering improved performance in terms of throughput, latency, and packet loss compared to traditional algorithms. Its proactive approach to adjusting the sending rate based on real-time bandwidth and round-trip time measurements allows it to optimize network utilization and minimize delays, even in challenging network conditions.

As internet traffic continues to grow and network conditions become more complex, algorithms like TCP BBR will play an increasingly important role in ensuring efficient and reliable data transmission. For businesses, developers, and network engineers, understanding and adopting the BBR algorithm can lead to significant improvements in network performance and user experience.

In conclusion, the practical comparison of Google’s TCP BBR algorithm reveals its clear advantages over traditional congestion control methods, particularly in high-bandwidth, low-latency, and congested network environments. By embracing BBR, organizations can ensure that their networks are equipped to handle the increasing demands of modern internet traffic.

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