AI Fabric Multipathing, Incast and Collective Communication Lab

AI Fabric Multipathing, Incast and Collective Communication Lab

Simulate AI Fabric Transport 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, this lab uses network simulation to experiment with AI-fabric technologies, transport protocols, and their behavior under different traffic conditions.

As in our other simulation labs, you can change parameters, create different traffic patterns, run experiments, and directly observe how those changes affect network performance.

Technologies and Mechanisms

In this lab, you work with modern AI-fabric transport approaches including:

• NDP
• EQDS
• RoCE
• HPCC

You will also experiment with important fabric mechanisms such as:

• Packet spraying
• Entropy and path diversity
• Packet trimming
• ECN-aware path selection
• Large-scale incast traffic
• Collective communication patterns

What Will You Learn?

The included workbook guides you through several experiments designed to show how transport protocols and path-selection mechanisms behave inside an AI fabric.

1. Multipathing and Entropy

Change the available entropy and path-selection space and observe how path diversity affects:

• Flow completion time
• Tail latency
• Path utilization
• Traffic distribution across the fabric

2. Packet Trimming

Compare deep buffering with packet trimming and observe the trade-off between queueing delay and retransmissions.

This experiment helps demonstrate how reducing queue buildup can improve latency, while potentially increasing the need for packet recovery.

3. ECN-Aware Path Selection

Observe how congestion feedback can help traffic avoid congested paths instead of selecting paths without considering current network conditions.

Compare path utilization, congestion levels, and flow performance as the path-selection strategy changes.

4. Large-Scale Incast

Generate many-to-one traffic patterns where multiple senders simultaneously compete for the same receiver bottleneck.

Compare how different transport approaches behave under incast, including:

• EQDS
• RoCE
• HPCC

Analyze how each approach responds to congestion, queue buildup, contention, and receiver-side bottlenecks.

5. Collective Communication

Explore AI-style collective communication patterns and observe how transport behavior and network conditions affect distributed workloads.

This helps demonstrate why network behavior is especially important for synchronized and communication-intensive AI workloads.

Analyze the Results

Each experiment provides graphs and measurable results, allowing you to immediately observe how parameter changes affect the network.

You will be able to analyze:

• Path utilization
• Congestion behavior
• Flow completion time
• Tail latency
• Fairness between competing flows
• Queue buildup
• Retransmission behavior
• Transport efficiency under incast

Learning Outcome

By the end of this lab, you will understand how multipathing, packet spraying, congestion-aware path selection, packet trimming, incast, and different transport approaches interact inside an AI fabric.

You will also understand why throughput alone does not tell the whole story when evaluating AI network performance, and why metrics such as flow completion time, tail latency, fairness, congestion, and path utilization are equally important.