# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## 5 Experimental Methodology, Results, and Discussion
### Experimental Testbed Setup
Moreover, flow fairness varies in the real world as network conditions dynamically change, affecting each flow unevenly over time. PCC's focus on fairness through learning mechanisms explains its lower unfairness compared to other competitive CCAs. Results in Sections 5.2-5.4 show the complexities of fairness in multi-CCA network traffic. While all flows initially experience a slow throughput rise due to network learning (latency, jitter, bandwidth), different CCAs exhibit varying behaviors due to their inherent design philosophies. These design principles lead to unfairness when non-identical CCAs compete. Additionally, factors like differing responsiveness to network changes (e.g., sudden congestion events) and varying interpretations of \"fairness\" by different algorithms can further exacerbate unfairness. Understanding these dynamics is crucial for optimizing network performance. One potential avenue for improvement could be the development of \"fairness-aware\" CCAs or \"switching\" for fairer CCAs that can dynamically adjust their behavior based on the presence of other algorithms, promoting a more cooperative approach to resource allocation. In Figure 5, CUBIC and BBR are competing, the throughput variation slightly improves over time. The influencing factor behind this is the adaptive nature of CCAs, which improves the estimation of available bandwidth. However, throughput imbalance remains unacceptably high in flows like in Figures 5. Not only do CCA flows such as CUBIC vs. BBR and PCC vs. BBR show disparities flattening out over time as CCAs adjust their data transfer aggressiveness, but distinct behaviors of CCAs with other CCA flows validate the hypothesis that no two CCA flows are perfectly fair to each other. Thus, intelligently assessing and switching available CCAs while studying changing network conditions would be very beneficial. Figure 4: Understanding throughput fairness (\(Th_{ratio}\)) by a comparison of throughput between two flows using the same CCA: (**a–c**). CUBIC flows showed higher fluctuations (**a**) compared to PCC flows (**b**), while BBR flows exhibited a significant throughput gap (**c**). Motivation to Choose the Scenarios  
The selected scenarios CUBIC vs. BBR-CUBIC, PCC vs. BBR-PCC, and BBR vs. PCC-BBR were chosen to highlight the dynamic interplay between different CCAs under varying network conditions. These scenarios were selected due to their representative nature in demonstrating how dynamically switching between CCAs can optimize network performance, fairness, and resource utilization. CUBIC and BBR were chosen for their distinct approaches to congestion control, with CUBIC being widely used and BBR offering a newer, bandwidth-centric methodology. PCC was included for its adaptive nature and ability to handle diverse network conditions. By focusing on these specific pairings and transitions, the paper aims to illustrate practical benefits and challenges of dynamic CCA switching, providing insights into improving network performance and fairness in real-world scenarios.