The cluster was burning. Not with fire, but with waste—CPU cycles idling, memory capped out, network pipes choking while storage sat untouched. Every engineer in the room knew the problem. No one had the numbers.
Infrastructure resource profiles are the map to that terrain. They define what each workload actually needs in CPU, memory, network throughput, and storage. Without them, capacity planning becomes guesswork, scaling is a gamble, and costs spiral far beyond budget. With them, decisions turn from reactive to precise.
An open source model for infrastructure resource profiles changes the game. Closed systems hide their logic, making it hard to adapt, audit, or integrate. Open source makes the model transparent, extensible, and verifiable. You can inspect the code, apply your own metrics, and adjust how resources are profiled across environments. This enables teams to align allocations tightly to application demand.
A solid model starts by collecting fine-grained metrics: CPU cores requested versus used over time, memory consumption peaks and medians, sustained I/O rates, and real network utilization patterns. Then it applies statistical methods to define optimal requests and limits. The best open source implementations support multiple workloads, Kubernetes-native workloads, and bare-metal or VM-based systems alike.