HPE Networking Data Center AI Data Center Operations in Action

As AI clusters grow in size and importance, seemingly minor network issues can cause costly job slowdowns or failures and become increasingly difficult to diagnose through conventional operational methods. Through customer examples and demonstrations, this session shows how HPE Networking Data Center Director and Mist Data Center Assurance combine intent-based automation and agentic AI to simplify deployments and reduce operational complexity across AI fabrics. At Networking Field Day 41, Sridhar Narahari, product manager at HPE, led the session.

Narahari began with a demo of HPE’s AI assistant troubleshooting a job reported as running slowly. The assistant drew on switch telemetry from Apstra Data Center Director, an agent running on the GPU NICs, and job data from schedulers such as Slurm. Within seconds, it traced the bottleneck to the GPU NIC, citing congestion notifications as evidence and confirming that the switches showed no congestion. Narahari attributed the approach to the Mist AI platform HPE is extending into the data center, a context graph that keeps responses grounded, specialized models, and live diagnostics that can run CLI commands. For deployments, he cited Oak Ridge National Laboratory, which built a cluster of more than 4,000 GPUs and 8,000 links in four to five weeks despite having no prior experience with the platform. Operators define intent, such as GPU counts, link speeds, and three-stage or rail-optimized designs. The system then generates what can be hundreds of thousands of configuration lines, validates them in a digital twin before commit, and continuously checks cabling against the design using LLDP data.

Narahari argued that at scale, job failures are a question of when rather than if. He cited Meta data showing a 10-GPU job fails on average after about 48 days, while a job of roughly 1,000 GPUs fails in about eight hours, with optics and links accounting for a growing share of those failures. HPE’s predictive analytics flags optics likely to fail, reporting confidence, time to failure, and impact. It also explains the root cause in plain language, such as declining transmit power on a single lane, and suggests actions such as cleaning a dirty transceiver. Asked about Juniper TAC integration, Narahari said the assistant can open tickets once the operator approves. A colleague added that the model has been trained on past TAC tickets and documentation and can run self-diagnostics on the switches.