Work place: Department of Computer Science and Engineering, Obafemi Awolowo University, IleāIfe, 220282, Nigeria
E-mail: gaderoun@oauife.edu.ng
Website: https://orcid.org/0000-0002-7992-514X
Research Interests:
Biography
Prof. Ganiyu Adesola Aderonmu is a Professor of Computer Science and Engineering with several years of research, teaching, leadership, and project funding experiences within and outside Nigeria. He has attracted several research grants, and he is currently the Center Leader of the African Center of Excellence, OAU Knowledge-driven ICT park at Obafemi Awolowo University, Ile-Ife, Nigeria, and spearheads the partnership program of the Digital Science and Technology Network (DSTN) project for the OAK-Park, Nigeria, and African Center of Excellence in Mathematics, Applications and Physical Sciences (ACE-SMIA), University of Abomey-Calavi, Benin Republic. He is a former Acting Head of the Department of Computer Science and Engineering (CSE); and former Director of the Information Technology and Communications Unit at OAU. He is a member of the Screening and Monitoring subcommittee of the Tertiary Education Trust Fund (TETFUND) research fund. He served as a member of curriculum development for the National Open University of Nigeria, and a member of COREN, CPN, and NUC accreditation teams, respectively, to various universities in Nigeria. He is a visiting research fellow at, University of Zululand, Republic of South Africa. He is the former National President of the Nigeria Computer Society.
By Jide Ebenezer Taiwo Akinsola Akinwale Olusolabomi Akinkunmi Ifeoluwa Michael Olaniyi John Edet Efiong Emmanuel Ajayi Olajubu Ganiyu Adesola Aderonmu
DOI: https://doi.org/10.5815/ijieeb.2026.01.01, Pub. Date: 8 Feb. 2026
Choosing the optimal software architecture for cloud-based systems is a critical and complex Multi-Criteria Decision Making (MCDM) problem, characterized by multiple, often conflicting, and interdependent criteria such as performance, cost, scalability, deployment speed, security, and maintainability. This research addresses this challenge by proposing and applying an integrated MCDM methodology that leverages Evidential Reasoning (ER) and Bayesian Inference (BI). The study's primary objective is to provide a robust and transparent framework for evaluating six common architecture styles: Monolithic, Microservices, Layered, Serverless, Event-Driven, and Service-Oriented Architecture (SOA). The methods employed involved a multi-stage process. First, criteria weights were derived using the Analytic Hierarchy Process (AHP) through expert pairwise comparisons. The techniques for handling uncertainty and dependencies were central. ER was utilized to aggregate subjective and objective assessments, representing them as belief distributions to explicitly account for imprecision and ignorance. Concurrently, BI was applied to model probabilistic interdependencies between criteria (Security influencing Performance, Performance influencing Scalability and Cost) within a Bayesian Network. The Intelligent Decision System (IDS) tool facilitated the operationalization of both ER aggregation and Bayesian inference. The results of the AHP weighting revealed the priorities: Performance (0.3930), Security (0.2355), Scalability (0.1420), Maintainability (0.1160), Deployment Speed (0.0568), and Cost (0.0568). The overall evaluation, integrating these weighted criteria with ER and BI, identified Monolithic architecture as the most suitable option, achieving a utility score of 0.81. This ranking was followed by Event-Driven (0.69), SOA (0.68), Serverless (0.68), Microservices (0.65), and Layered (0.47). A comprehensive sensitivity analysis was conducted to assess the robustness of this decision. Crucially, the analysis demonstrated that while the Monolithic architecture was initially optimal, significant shifts in criteria weights could alter the ranking. Specifically, when the weight of Security was substantially increased (to ~0.32) and Performance decreased (to ~0.25), the Serverless architecture emerged as the new top-ranked alternative (83% utility score), surpassing Monolithic (78%). This finding underscores the critical influence of strategic priorities on architecture selection. Future studies may also focus on developing data-driven, adaptive, and domain-specific decision frameworks to enhance the robustness, transparency, and real-world applicability of MCDM approaches for cloud-based software architecture selection.
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