What are the best practices for tuning TCP congestion control algorithms?

2 Configure the parameters

The second step is to configure the parameters of the chosen algorithm. Each algorithm has some tunable parameters that affect its behavior and performance. For example, Reno has parameters such as initial window size, slow start threshold, and congestion avoidance factor. Cubic has parameters such as cubic factor, beta factor, and maximum window size. BBR has parameters such as pacing gain, congestion window gain, and probe RTT mode. PCC has parameters such as utility function, exploration factor, and learning rate. Therefore, you should understand the meaning and impact of each parameter, and adjust them according to your objectives and conditions. For instance, you may want to increase the initial window size to improve the startup performance, or decrease the beta factor to reduce the packet loss rate.

3 Monitor the metrics

The third step is to monitor the metrics of the algorithm. You need to measure and analyze the performance and behavior of the algorithm in real time or offline. Some of the common metrics are throughput, latency, packet loss, fairness, and stability. You can use tools such as iperf, ping, traceroute, tcpdump, and wireshark to collect and visualize these metrics. For example, you can use iperf to measure the throughput and latency of a TCP flow, or use tcpdump and wireshark to capture and inspect the TCP packets and headers. Therefore, you should monitor the metrics of the algorithm, and identify any problems or anomalies that may occur.

4 Evaluate the results

The fourth step is to evaluate the results of the algorithm. You need to compare and contrast the performance and behavior of the algorithm with other algorithms or baselines. You can use criteria such as efficiency, fairness, robustness, and security to evaluate the results. For example, you can use metrics such as Jain's index, coefficient of variation, or Gini coefficient to assess the fairness of the algorithm, or use metrics such as throughput ratio, latency ratio, or loss ratio to assess the efficiency of the algorithm. Therefore, you should evaluate the results of the algorithm, and determine if they meet your expectations and standards.

5 Update the algorithm

The fifth step is to update the algorithm. You need to modify or improve the algorithm based on the feedback and findings from the previous steps. You can use methods such as optimization, adaptation, or learning to update the algorithm. For example, you can use gradient descent, convex optimization, or Lagrangian relaxation to optimize the parameters of the algorithm, or use feedback control, reinforcement learning, or neural networks to adapt or learn the behavior of the algorithm. Therefore, you should update the algorithm, and enhance its performance and behavior.

6 Test the algorithm

The sixth and final step is to test the algorithm. You need to verify and validate the algorithm in different scenarios and conditions. You can use methods such as simulation, emulation, or experimentation to test the algorithm. For example, you can use tools such as ns-3, mininet, or testbeds to simulate, emulate, or experiment with the algorithm in various network topologies, traffic patterns, and congestion levels. Therefore, you should test the algorithm, and ensure its reliability and security.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?