How to Experiment with QUIC Congestion Control for Computer Network Assignments

Why Congestion Control Matters

The Internet is a shared medium. When multiple users send traffic through the same link, there is a risk that the network can become congested, leading to long delays, packet loss, or even congestion collapse where no traffic flows effectively. Congestion control algorithms are designed to prevent this by dynamically adjusting the rate at which data is transmitted.

Transport protocols like TCP have relied on congestion control for decades. Algorithms such as Tahoe, Reno, and Cubic have evolved to provide better fairness and throughput. However, TCP congestion control implementations typically live inside the operating system kernel, making them harder for students to experiment with directly. To modify parameters or test different schemes, one often has to recompile and reinstall the kernel—a time-consuming and impractical process for classroom learning.

This is where QUIC provides a fresh opportunity.

Why QUIC is a Game-Changer for Students

Unlike TCP, QUIC is not part of the kernel. Instead, it is implemented as a user-space library or application. This makes it easier to experiment with and modify.

For educators and students, this is a major advantage because:

1. Ease of Experimentation – QUIC implementations are often written in higher-level languages such as Python, Go, or Rust. Students can tweak congestion control logic directly in the source code without dealing with kernel complexities.
2. Cross-Platform Compatibility – Because QUIC runs in user space, it can be deployed on Windows, Linux, or macOS without requiring special kernel patches.
3. Support for Multiple Algorithms – Modern QUIC libraries allow experimentation with different congestion control schemes, including classic ones like NewReno and Cubic, and newer designs like BBR.
4. Integration with Simulation Tools – QUIC can be easily combined with network emulation environments, giving students the ability to replicate real-world conditions like high delay, packet loss, and bottleneck links.

This flexibility makes QUIC the perfect platform for hands-on labs in computer networking courses.

Setting Up the Experiment

To help students learn congestion control concepts, one practical setup is to create a controlled environment with multiple clients, servers, and a bottleneck link. By simulating congestion, students can directly observe how different algorithms respond.

A useful tool for this is container-based network emulation. Using lightweight Linux containers, students can create virtual routers, links, and end-hosts inside their own computers. This eliminates the need for specialized hardware or expensive lab equipment. The containers communicate just like real machines, and traffic can be shaped to simulate bandwidth constraints or latency.

Imagine a simple lab with:

• Two clients sending data
• Two servers receiving data
• A single bottleneck link connecting them

This topology is enough to demonstrate congestion collapse, fairness, and throughput distribution under different congestion control schemes.

First Experiment: Disabling Congestion Control

The first step in learning is to see what happens when there is no congestion control.

Students can disable QUIC’s congestion controller and allow senders to transmit at maximum speed. What follows is a clear demonstration of congestion collapse—packets fill the router buffer, queues overflow, and throughput drops drastically.

This experiment is powerful because it shows why congestion control is not optional. Without it, even simple networks can grind to a halt. Students quickly understand the importance of algorithms that regulate flow.

Exploring Classic Algorithms: NewReno and Cubic

Once students appreciate why congestion control exists, the next step is to explore how different algorithms manage traffic.

NewReno

NewReno is a refinement of the classic Reno algorithm. It uses the principle of additive increase, multiplicative decrease (AIMD) to adjust the congestion window.

• Additive Increase: The sender slowly increases its window size until packet loss occurs.
• Multiplicative Decrease: When a loss is detected, the sender cuts its window size, backing off to relieve congestion.

NewReno is predictable, relatively fair, and provides a good starting point for analysis. Students can measure throughput, latency, and fairness when multiple NewReno senders share the same link.

Cubic

Cubic, on the other hand, was designed for high-speed, long-distance networks. Instead of linear growth, Cubic uses a cubic function to adjust the congestion window. This allows it to grow more aggressively in networks with large bandwidth-delay products while still maintaining fairness.

By running experiments with NewReno and Cubic side by side, students see how different algorithms adapt to the same network conditions. In particular, they learn how round-trip time (RTT) influences performance. Cubic tends to be more RTT-fair compared to Reno-based algorithms, but its aggressiveness can also create tension when sharing links with more conservative flows.

Enter the Modern Era: BBRv1 and BBRv2

Beyond NewReno and Cubic, QUIC allows exploration of BBR (Bottleneck Bandwidth and Round-trip propagation time). Unlike traditional loss-based algorithms, BBR estimates the actual available bandwidth and RTT of the network path, then adjusts its sending rate accordingly.

• BBRv1 introduced the concept of pacing traffic at the estimated bottleneck rate.
• BBRv2 refined this with better fairness mechanisms and improved responsiveness to congestion.

Students experimenting with BBR quickly notice how different it feels compared to NewReno or Cubic. Instead of waiting for packet loss, BBR proactively manages sending rates based on measurements. This makes it particularly effective in networks with large buffers or variable delays.

Fairness and Queue Management: FQ-CoDel

The final step in congestion control labs is to study active queue management (AQM) schemes. Even with good congestion control, some flows can behave unfairly, especially when certain applications don’t cooperate.

One well-known solution is FQ-CoDel (Fair Queuing with Controlled Delay).

It works by:

• Separating traffic into different queues
• Scheduling packets fairly across flows
• Dropping packets early to prevent long queues

In lab experiments, enabling FQ-CoDel shows how fairness can be restored even when unresponsive or aggressive flows exist. Students learn that congestion control isn’t just about end-hosts—it also involves cooperation from routers.

Visualizing the Results

Data alone isn’t enough—students need to see how congestion control behaves.

For example:

• A graph showing the throughput of two NewReno senders sharing a bottleneck link reveals how they converge toward fairness.
• A heat map of throughput over time shows whether one flow dominates or if resources are shared equally.

These visualizations make abstract concepts like fairness and collapse tangible. Students can observe the trade-offs between algorithms and understand why no single scheme is perfect.

Lessons for Students

Through these QUIC-based experiments, students gain several important insights:

1. Congestion control is essential – Without it, networks collapse under heavy load.
2. Different algorithms suit different networks – NewReno, Cubic, and BBR each shine in different conditions.
3. Fairness is a key challenge – Not all algorithms share bandwidth equally, and mechanisms like FQ-CoDel are needed to balance flows.
4. Practical experience is invaluable – Reading about algorithms in textbooks is helpful, but seeing them in action cements understanding.
5. User-space protocols like QUIC democratize experimentation – Students no longer need complex kernel modifications to explore advanced transport behaviors.

Why This Matters for Computer Network Assignments

At computernetworkassignmenthelp.com, we know that many students struggle with congestion control when working on assignments. The topic is mathematically rich, conceptually deep, and practically challenging. By leveraging QUIC-based labs, we make these concepts easier to grasp.

When working on assignments, students often face tasks like:

• Explaining how different congestion control schemes behave under varying network conditions.
• Designing experiments to measure fairness and throughput.
• Comparing traditional TCP-based algorithms with modern QUIC approaches.
• Writing reports that include performance graphs and analysis.

Our team specializes in guiding students through these challenges, providing not just theoretical explanations but also practical approaches to implement, test, and analyze congestion control.

Conclusion

Experimenting with congestion control is one of the best ways for students to understand the transport layer of computer networks. While TCP has long been the backbone of Internet congestion control, its kernel-based implementations make experimentation difficult. QUIC changes the game by bringing transport into user space, allowing students to modify, test, and visualize congestion control schemes with relative ease.

By exploring scenarios with no control, then gradually adding NewReno, Cubic, BBR, and finally queue management techniques like FQ-CoDel, students build a complete picture of how Internet traffic is managed. These experiments highlight not only the technical aspects of congestion control but also the importance of fairness and efficiency in real-world networking.

At computernetworkassignmenthelp.com, we encourage students to combine theory with practice. By engaging in QUIC-based labs, learners move beyond textbooks and gain hands-on skills that will serve them in assignments, projects, and professional careers.