How to Test Network Scalability Under Load

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All-to-All AI Network Workloads

Engineers can evaluate network scalability for AI and high-performance computing workloads by recreating collective communication patterns in a controlled test environment. Start by building an all-to-all traffic topology that represents the intended compute fabric, then configure Remote Direct Memory Access over Converged Ethernet version 2 (RoCEv2) endpoints, workload characteristics, and the network controls that influence congestion behavior. Run the collective workload at defined offered loads and collect throughput, latency, packet-loss, and congestion-related measurements as traffic moves between endpoints. Engineers can repeat the same workload while changing one network or device parameter at a time, which keeps each comparison tied to a known configuration change.

As aggregate all-to-all traffic increases, engineers can monitor how latency, loss, and throughput change across the fabric and how congestion-control settings affect those results. They can vary workload size, endpoint count, offered load, topology, and device-under-test parameters while keeping the remaining conditions fixed. This approach creates repeatable AI network testing scenarios that show how the fabric behaves as collective communications place more pressure on shared links and queues. Engineers can use the resulting measurements to compare configurations, tune congestion-control behavior, and assess network scalability before deployment. Recording the topology, workload definition, offered load, timing windows, and control settings for every run also creates a consistent dataset for comparing revisions or infrastructure changes.

AI Network Scalability Test Solution

Create repeatable all-to-all collective communication workloads, increase offered load, and measure how the network responds as congestion develops across the fabric. Keysight’s IxNetwork uses RoCEv2 emulation to model AI and high-performance computing communication patterns and lets engineers configure workload behavior, endpoints, and network parameters while collecting throughput, latency, packet-loss, and congestion-related statistics. Engineers can establish a baseline, change a single device-under-test or congestion-control parameter, and rerun the same traffic sequence with the same topology, endpoint count, offered load, and measurement window. This controlled workflow helps teams compare how different settings affect collective communications as aggregate traffic increases. IxNetwork provides the repeatable traffic generation, RoCEv2 emulation, and measurement visibility needed to evaluate network scalability and congestion-control response without relying on uncontrolled production traffic.

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