Understanding QUIC Congestion Control through Hands-On Experiments

Unlike TCP, which is integrated into the operating system, QUIC runs at the application layer. This means that developers and students can work with QUIC implementations like any other software library. Various QUIC implementations are available in different programming languages, offering flexibility and accessibility for a wide range of learners.

For students who are already familiar with Python programming, using a Python-based QUIC implementation simplifies the learning curve significantly. By leveraging Python’s readability and ecosystem, students can focus on understanding networking behavior rather than dealing with complex kernel development tasks.

To bring this concept to life, a series of congestion control lab experiments were developed using aioquic, a Python QUIC implementation that supports many modern QUIC features. By integrating aioquic with Kathara, an open-source network emulation platform based on Linux containers running inside Docker, students can create controlled, repeatable networking environments on their own computers—whether they use Windows, Linux, or macOS.

Kathara enables students to build realistic network topologies with multiple nodes, routers, and links, emulating the behavior of actual networks. It’s lightweight, portable, and well-suited for academic experiments in computer networking courses.

The Experimental Setup: A Simple Yet Powerful Topology

The congestion control experiments begin with a simple network topology consisting of two clients, two servers, and a bottleneck link between them. This straightforward setup provides the perfect foundation for exploring how congestion control algorithms behave under different network conditions.

In the first stage of the experiment, the QUIC implementation is modified to disable congestion control entirely. This allows students to observe what happens when multiple senders transmit data at high rates without any control over their sending windows. Predictably, this leads to a congestion collapse—a situation where packets are dropped excessively, queues overflow, and overall network throughput drops dramatically.

By seeing this failure mode firsthand, students gain a deep, intuitive understanding of why congestion control is critical for maintaining network stability and fairness.

Exploring Different Congestion Control Algorithms

Once the importance of congestion control is established, students move on to experimenting with several widely used congestion control schemes.

The lab environment supports algorithms such as:

• NewReno – A classic loss-based congestion control algorithm that has been the default in many transport implementations for years.
• Cubic – A more modern congestion control algorithm optimized for high-speed networks, known for its cubic growth function.
• BBRv1 and BBRv2 – Algorithms that focus on achieving high throughput and low latency by modeling the network path’s bandwidth and round-trip time, rather than relying solely on packet loss as a signal.

By switching between these algorithms, students can compare their behaviors under identical network conditions.

For example, they can explore:

• How different algorithms respond to changes in round-trip time (RTT).
• How they share bandwidth when multiple flows using different algorithms compete for the same bottleneck link.
• How quickly each algorithm recovers from congestion events.
• The fairness of bandwidth allocation between multiple flows.

Such experiments give students a comprehensive view of how congestion control affects performance and user experience in real networks.

Visualizing the Results

An important part of learning from network experiments is visualization. Raw numbers and log files can be difficult to interpret, especially when dealing with multiple flows and rapidly changing network conditions. To address this, the lab setup includes a set of Python scripts that generate clear, insightful visualizations of the experimental data.

For example, one graph illustrates the evolution of throughput for two NewReno senders sharing a bottleneck link over time. Students can see how the congestion windows grow, how the flows adjust to network conditions, and how they stabilize after initial competition. This visual reinforces the theoretical principles taught in class, showing how the congestion avoidance and loss recovery mechanisms work in practice.

Another visualization uses a heat map to depict the throughputs of two flows over time. In this view, students can observe how the two flows share the bottleneck fairly, with throughput values distributed in a balanced way. This helps students understand not only the dynamics of congestion control but also the concept of fairness—a key design goal in many transport protocols.

Adding Fair Queuing with FQ-Codel

The experiments culminate in exploring FQ-Codel (Fair Queuing Controlled Delay), a modern queue management technique designed to reduce latency and improve fairness, particularly in the presence of unresponsive or misbehaving flows. By enabling FQ-Codel in the bottleneck router, students can observe how the queueing discipline influences the performance of different congestion control algorithms.

FQ-Codel ensures that no single flow can dominate the bottleneck link, even if it doesn’t implement congestion control properly. This is crucial in real-world networks, where not all traffic behaves cooperatively. Students can see how FQ-Codel improves latency, reduces queue buildup, and maintains fairness among all flows.

From Theory to Practice

The beauty of this experimental setup lies in its ability to bridge the gap between theory and practice. Students typically learn about congestion control algorithms in the abstract: they study AIMD (Additive Increase Multiplicative Decrease), loss-based versus delay-based algorithms, and fairness models. However, seeing these algorithms in action, interacting with real network queues and competing flows, brings the concepts to life.

For example, when students disable congestion control entirely and watch throughput collapse despite high sending rates, they grasp the fundamental reason why congestion control exists. When they switch between NewReno and Cubic and see different throughput patterns, they understand how algorithm design choices impact performance. When they enable FQ-Codel, they see how queue management techniques can improve fairness and user experience even without modifying the end-host protocols.

Advantages of Using QUIC for Student Labs

There are several reasons why using QUIC for congestion control labs is a game-changer in computer networking education:

1. User-space implementation – Students can modify, extend, and experiment with the QUIC stack without kernel programming.
2. Cross-platform compatibility – With tools like Kathara, experiments run on Windows, Linux, and macOS, making the labs accessible to a wide range of students.
3. Python-based – Using a Python implementation lowers the barrier to entry for students who already have basic programming skills.
4. Modern protocols – QUIC is not just a research toy; it’s a widely deployed protocol used in HTTP/3. By learning QUIC, students gain skills that are highly relevant to today’s Internet.
5. Rich experimentation space – QUIC’s flexible design allows students to explore cutting-edge congestion control algorithms like BBR, which are increasingly important in modern networks.

Integrating These Labs into Academic Learning

Incorporating hands-on QUIC congestion control experiments into coursework or assignments significantly enhances the learning experience.

At computernetworkassignmenthelp.com, we often assist students with:

• Setting up the lab environment, including Kathara and aioquic.
• Configuring different congestion control algorithms and explaining their theoretical underpinnings.
• Designing experiments to compare performance under varying network conditions, such as changing RTTs, bandwidths, or queue sizes.
• Analyzing and visualizing results using Python scripts and graphs.
• Interpreting findings in the context of real-world networking principles.

Whether students are working on individual assignments, lab projects, or research work, these experiments provide practical insights that are difficult to achieve through theory alone.

Conclusion

Congestion control remains one of the most critical mechanisms in ensuring the stability, efficiency, and fairness of the Internet. By leveraging QUIC’s user-space implementation and combining it with container-based network emulation tools, students can now experiment with congestion control algorithms directly, gaining a much deeper understanding of how they work in practice.

This hands-on approach not only strengthens students’ conceptual understanding but also prepares them for real-world networking challenges, where protocols like QUIC and algorithms like BBR are increasingly shaping Internet traffic.

At computernetworkassignmenthelp.com, our team is dedicated to helping students master these advanced concepts through practical guidance and expert support. If you’re working on congestion control assignments or want to design your own QUIC-based experiments, our experts can assist you at every step—from setup to analysis.