Topics of interest for research and development at undergraduate, master's and doctoral levels.
Software Engineering
Summary. Investigates how traditional patterns (MVC, Singleton, Factory) are adapted for cloud-native architectures, including serverless design patterns and microservices communication patterns.
Objective. Identify the most effective patterns in high-scalability scenarios, proposing a best-practices guide.
Summary. Explores how Scrum and Kanban can be harmonized with DevOps practices, analyzing impacts on continuous value delivery.
Objective. Propose a hybrid framework that optimizes workflow in multidisciplinary teams.
Distributed Systems
Summary. Analyzes challenges, strategies and migration patterns, including automated tooling and strangler-fig techniques.
Objective. Develop a taxonomy of patterns and tools to ease migration from monoliths to microservices.
Summary. Explores metrics such as SLIs and SLOs, correlating them with resilience and performance in production.
Objective. Propose an evaluation model to measure and improve observability and resilience in critical systems.
Summary. Investigates practices such as Chaos Engineering to prevent failures and ensure high availability.
Objective. Create practical guidelines for SRE teams to implement reliability as a technical discipline.
Cloud Computing
Summary. Addresses techniques, methods and models to plan resource capacity for cloud workloads.
Objective. Evolve the C2PF (Cloud Capacity Planning Framework) model for cloud workloads.
Summary. Techniques and tools to forecast capacity demand, optimizing cost and ensuring performance.
Objective. Develop a predictive, Machine-Learning-based model for hybrid-cloud environments.
Summary. Explores native patterns — multi-tenancy, horizontal scalability, billing patterns — in SaaS solutions.
Objective. Create a best-practices guide for SaaS architecture focused on scalability and cost.
Summary. Analyzes models such as CMMI adapted to the cloud, proposing a structure to measure maturity.
Objective. Propose a maturity framework from initial use to an advanced optimization stage.
Data & Artificial Intelligence
Summary. Investigates Data Lakes as the link between legacy systems, distributed applications and analytics platforms — patterns, ingestion, governance, scalability and performance.
Objective. Propose and validate a Data Lake architectural model oriented to enterprise integration.
Summary. Explores integrity, bias, synthetic data and provenance as an interdependent system for trustworthy AI.
Objective. Define and evaluate the Data-Centric Trust Pipeline as a data-governance model.
CARLOS DIEGO C. P.