North
Client application layer
Workload behavior, request patterns, concurrency, retries, timeouts, queueing, and what the application experiences as latency or failure.
Systems Model
This is a conceptual visualization of how I look at infrastructure problems. It keeps the core data path visible while also showing the surrounding pressures that often change the answer: telemetry, workload shape, failure domains, and operational context.
Why this model
This is a visual way to show how I tend to look at infrastructure problems. The hard cases usually are not isolated to one layer, so I look for how the layers influence each other.
The triangle keeps the core data path visible: compute, storage, and network. The rings show the context that often changes the answer: telemetry, workload shape, failure domains, and operational reality.
It helps frame the investigation: where is the symptom seen, where might pressure originate, and what evidence is strong enough to act on?
This is also where structured issue-resolution training shows up in a practical way. The model is not a checklist, but it helps keep facts, assumptions, changes, impact, and next actions separate when the problem is messy.
South
Data path / storage layer
NAS, S3/object, metadata behavior, block/file semantics, NVMe/NVMe-oF, cache effects, persistence path, and backend pressure.
East
Network fabric
RDMA, RoCE, InfiniBand, Ethernet, MTU, PMTUD, congestion, retransmits, routing, fabric counters, and latency amplification.
West
Infrastructure context
Virtualization, Linux, Slurm / scheduler-aware behavior, Kubernetes, GPU nodes, host firmware, drivers, topology, telemetry, and operational constraints.
Infrastructure Behavior in Context
These are the recurring pressures I look for when a system is available, but performance, reliability, or customer impact says the operating picture is incomplete.
Hover, focus, or tap a vector to see how it affects the surrounding system. On touch screens, tapping a card or row keeps the relationship visible.
Performance
Throughput and latency are shaped by the full path: clients, queues, storage media, fabric behavior, workload timing, and operational limits.
Reliability
Redundancy helps only when the underlying failure domains are understood. Clean failover assumptions can hide path, hardware, topology, or operational risk.
Observability
Telemetry is useful when it explains behavior. Counters, traces, logs, and timing data have to be compared against what users and workloads actually experience.
Automation
Automation should reduce toil without becoming blind control-plane behavior. Scripts and workflows need guardrails, validation, and safe handoff.
Workload Behavior
Application patterns, burst behavior, request shape, and data access timing can expose constraints that static capacity views do not show.
Human Operations
Operational reality includes people, escalation paths, support handoffs, customer impact, and the difference between a system being up and being useful.
How the Pressures Affect Each Other
| Trigger Vector | Common Downstream Impacts | Engineering Rationale |
|---|---|---|
| Performance | Reliability, Observability | High throughput stresses queues, paths, storage media, and logging systems. Performance investigations often expose reliability or visibility gaps. |
| Reliability | Performance, Human Operations | High-availability layers can introduce topology complexity, latency trade-offs, and more operational surface area for teams to understand. |
| Observability | Automation, Workload Behavior | Telemetry can drive orchestration decisions, but only when the signals are meaningful and tied to workload behavior instead of isolated counters. |
| Automation | Human Operations, Reliability | Automation removes repeatable toil, but unsafe automation can amplify drift or act on incomplete assumptions. |
| Workload Behavior | Performance, Observability | Irregular request bursts, data access shape, and runtime timing can surface constraints that static infrastructure health checks miss. |
| Human Operations | Automation, Reliability | Operational outcomes depend on how clearly people can understand evidence, communicate risk, and act safely under pressure. |
Selected Relationship:
Select or hover over a vector to highlight its downstream impacts.
How this shows up in practice:
Investigations rarely stay inside one box. A performance symptom can become a reliability question, an observability gap can block automation, and workload behavior can make healthy infrastructure appear broken.