Representative problem domains

Large distributed storage environments where NAS, S3/object access, metadata activity, network fabric behavior, and customer workload patterns all influence perceived performance.

Correlate client symptoms with storage telemetry, fabric counters, protocol behavior, workload timing, and known topology or configuration constraints.

Identify the likely failure domain, collect the evidence path, explain the operational impact, and drive the investigation toward a validated conclusion.

Separate workload-side pressure from platform-side behavior, reduce ambiguity, and create a repeatable diagnostic path for engineering and customer-facing teams.

AI/HPC environments where GPU utilization, data access, storage latency, fabric behavior, and orchestration decisions combine into workload throughput.

Validate the data path across client, compute node, network, storage service, and telemetry layers while watching for starvation, queueing, topology bottlenecks, and bad assumptions.

Separate GPU, storage, fabric, scheduler, and workload-side constraints so the team can act on the real limiter instead of the loudest symptom.

Identify whether the limiting factor is workload shape, storage behavior, network path, host configuration, scheduler placement, or instrumentation.

Ambiguous platform incidents where several teams see partial symptoms but no single layer clearly owns the failure.

Build a timeline, define evidence boundaries, trace the request path, compare telemetry against observable behavior, and remove unsupported explanations.

Turn fragmented signals into a shared operating picture and keep the discussion anchored to evidence rather than ownership assumptions.

Convert scattered evidence into a practical operating picture, then document the mitigation, validation method, and longer-term engineering follow-up.

Constrained customer or lab environments where evidence collection must be fast, low-risk, portable, and understandable by more than one engineer.

Use Bash, Python, PowerShell, packet capture, syslog/Splunk-friendly output, and structured logs to collect host, network, storage, and workload context.

Make evidence collection repeatable, portable, and understandable so diagnostics can survive handoff, escalation, and later review.

Improve time-to-evidence, reduce repeated manual effort, and make diagnostic workflows easier to hand off, review, and reuse.