Cloud computing allows the shared use of computational resources with its cost based on operational cycles. Given the technical and financial constraints of cloud computing projects, capacity planning processes help to identify the need for resources in a given workload in order for it to operate properly in the face of different contexts. The current industrial paradigm considers historical usage precedents for the capacity planning of cloud computing workloads. In addition, current practices of cloud capacity planning do not establish how accurate these plans are. This work aims to establish a functional paradigm for capacity planning of cloud computing workloads, which include: an architectural meta-model; a method for functional and architectural classification of workloads; a sizing equation and confidence index.

Graduated in System Analysis, Post-graduated in Management, Master’s Degree in Computer Science and PhD Candidate in Computer Science. Research Fields: Software Engineering, Software Reliability Engineering, Cloud Computing. Also teaches graduate and postgraduate courses, participating in the academic community, publishing articles, studies and research. Amazon Web Services Ambassador in Latin America and AWS Certified.