C2PF (Cloud Capacity Planning Framework)

Sizing the cloud by precedent — with no measure of accuracy

The industrial paradigm plans capacity from historical usage precedents — and never establishes how accurate those estimates are. For workloads without history, the method simply does not apply.

At the architectural level, decisions — which services to provision, at what reliability, under what performance envelope — rarely derive from explicit, structured objectives. Existing methods either presuppose a prior architecture (ATAM, CBAM) or produce generic outputs disconnected from cloud-specific constructs (ADD, well-architected frameworks). And at the assessment stage, everything depends on tacit knowledge: two architects facing the same problem arrive at different proposals — and that does not scale.

The risk lives at the start of the cycle

Cloud projects follow a familiar cycle: assessment, sizing, migration and operation/optimization. It is at the start — assessing and sizing — that the risk and cost of decisions concentrate, and that is exactly where the least rigor exists. Planning that only looks backward cannot justify its own accuracy nor handle the unprecedented.

The proposal is not another framework or another AI tool, but a scientifically grounded decision pipeline: turning declared objectives into architecture traceably, reproducibly and cloud-specifically — and, from there, externalizing specialist knowledge into platform-executable structures.

From objective to architecture, in four layers

C2PF is a formal classification model for cloud workloads via Service-Level (SLO), Performance-Level (PLO) and Reliability-Level (RLO) objectives, with abstract sizing classes. A decision-oriented process model closes the instantiation gap — how to derive an architectural decision from the declared values:

• ›Five principles: reproducibility, traceability, hierarchy-driven conflict resolution, domain specificity and conservative boundary resolution.
• ›Assessment Model (5 phases): externalizes assessment knowledge into reproducible, executable structures — each elicitation question maps to a C2PF capacity characteristic.
• ›Constrained LLM synthesis: the language model only translates a machine-readable assessment profile into a Statement of Work (SOW) — it does not invent sizing.

Capacity decisions as an engineering artifact

Make design-time capacity decisions traceable to declared objectives, reproducible and cloud-specific; externalize tacit assessment knowledge so it scales across practitioners; and open the path from intent to executable, validated architecture.

From thesis to commercial operation

C2PF began in the doctoral thesis (CESAR School, 2023) and evolved into two papers and a product in production:

• ›End-to-end example with a B2C payment service under PCI-DSS: all three objective types converge to the demand ceiling, yielding a C2PF-XL class, with compliance components added orthogonally.
• ›A challenge repository covering 6 industries, 30 categories and ~350 questions of assessment with pre-defined sizing characteristics.
• ›The product in use shows that scientific abstraction, properly engineered, dramatically reduces time-to-proposal.

Where the research goes next

• 01Empirical validation across five industrial scenarios — stress-testing boundaries, asymmetric profiles and the priority hierarchy against expert judgment.
• 02From intent to executable, validated architecture — turning the SOW (a transition artifact) into a formal, executable architectural decision.
• 03Independent replication of the SOW quality scores, to mitigate evaluator leniency bias.

Open fronts for collaboration

• ›Executable architecture — couple the C2PF plan to Infrastructure-as-Code (IaC) generation.
• ›Cost/FinOps optimization — link the sizing classes to cost models.
• ›Repository expansion — more verticals, categories and capacity characteristics.
• ›Validation instruments — benchmarks and protocols for design-time capacity planning.
• ›Domain extension — data and AI, beyond cloud, in the same capacity grammar.
• ›Sector applications — migration, data platforms and AI adoption in professional services.