AresONE 1600GE AI Workload Validation

As AI clusters continue to scale, validating network performance under realistic workloads becomes increasingly important. This demonstration highlights how Keysight's AI Data Center Builder and AresONE 1600GE work together to emulate AI workload traffic, execute collective communication workloads, and analyze network performance through detailed analytics and reporting. The workflow begins within AI Data Center Builder, where workload configurations are defined, tests are executed, and performance metrics are analyzed. In the demonstrated test configuration, a collective communication workload is configured using an 8 GB data size, a 64 KB Remote Direct Memory Access (RDMA) message size, two queue pairs, and ten test iterations. The test environment includes sixteen hosts connected through an AresONE 1600GE configured with four 400GE fanout ports, providing a total of sixteen test ports for workload execution and analysis. The demonstration also highlights the platform's streamlined setup process. Within the port mapping interface, engineers can configure connections by providing the AresONE 1600GE IP address and verifying port assignments and speeds. Once the trial is started, AI Data Center Builder automatically connects to the test system, prepares the ports, and executes the configured workload. After test execution is complete, a comprehensive set of analytics and reporting tools is available for performance evaluation. The Summary view provides key metrics including collective completion time, ideal percentage, total execution time, and graphical visualizations that help engineers quickly assess workload performance. Additional analysis is available through the Details view, where minimum, maximum, and average timing statistics can be reviewed. The Iteration view provides visibility into collective start and end times for each iteration, along with flow completion time statistics that help engineers understand workload consistency across multiple runs. Histogram views further simplify analysis by displaying the distribution of completion times. The Chunk View provides cumulative distribution function (CDF) analysis for different chunk sizes, allowing engineers to evaluate transfer consistency and identify potential performance variations. The Flow Timeline view offers additional visibility into traffic behavior by showing how flows progress over time and helping confirm balanced workload execution without significant gaps or delays. Queue Pair views enable engineers to examine flow-level performance and latency measurements while filtering results between specific source and destination ranks. Associated Queue Pair Metrics provide summarized statistics for rapid analysis and troubleshooting. In addition, the Port Level view provides detailed statistics across all ports, enabling validation of performance across the entire test topology while supporting filtering based on specific metrics and parameters. Together, these analytics provide a comprehensive picture of workload behavior, network efficiency, and collective communication performance. By combining realistic AI workload emulation, collective communication testing, detailed analytics, and comprehensive reporting within a single workflow, AI Data Center Builder and AresONE 1600GE help engineers validate AI cluster network performance, identify bottlenecks, and optimize infrastructure before deployment. These capabilities help accelerate AI network validation and improve confidence in next-generation AI infrastructure designs.