AI Fabric Congestion Control: PFC, ECN and DCQCN

AI Fabric Congestion Control Lab: PFC, ECN and DCQCN

Simulate Protocol Behavior — Not Traditional Router Configuration

This is not a traditional Cisco or Juniper lab where you connect to routers and switches and configure protocols through the CLI.

Instead, these labs use network simulation tools to study AI networking technologies, algorithms, and protocol behavior.

As shown in the lab interface, you can change parameters, generate different traffic conditions, run simulations, and observe how those changes affect network performance.

What Can You Configure?

In this lab, you can experiment with parameters such as:

• Enable or disable DCQCN
• Tune ECN Kmin and Kmax thresholds
• Change the PFC PAUSE duration
• Modify the incast ratio
• Adjust the link speed
• Change the traffic duration
• Experiment with advanced DCQCN parameters

What Will You Learn?

The included workbook guides you through several practical experiments designed to demonstrate how PFC, ECN, and DCQCN behave under different congestion scenarios.

1. PFC Without DCQCN

Observe how PFC PAUSE can affect an unrelated victim flow, even when there is no packet loss.

2. DCQCN Enabled

See how ECN feedback and sender-side rate control can prevent congestion from growing to the point where PFC is triggered.

3. ECN Threshold Tuning

Change Kmin and Kmax values and observe their effects on:

• Queue behavior
• Goodput
• Fairness between flows
• Congestion response

4. DCQCN Recovery Tuning

Modify RATE_AI and RATE_HAI and analyze how they influence:

• Recovery speed
• Rate overshoot
• Oscillation
• Overall flow stability

Analyze the Results

Each experiment provides graphs and performance metrics so you can visually analyze the effect of your parameter changes.

You will be able to:

• Compare different congestion-control settings
• Understand how queue behavior changes
• Measure utilization and flow performance
• Evaluate fairness between competing flows
• Understand why different protocol configurations behave differently

Learning Outcome

By the end of this lab, you will understand not only what PFC, ECN, and DCQCN are, but also how they interact with each other, why tuning matters, and how different design choices affect congestion, utilization, queue behavior, and flow performance.