Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/A3409A0B-407A-436F-B0DE-2E2D4C433BE8 | ||
| Year | 2025 | ||
| Type of Item | Diploma Work | ||
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| Bibliographic Citation | Ioannis Michalakis, "Argumentation-based decision support system for systems deployment: case study in the Ministry of Digital Governance", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2025 https://doi.org/10.26233/heallink.tuc.104026 | ||
| Appears in Collections |
The increasing complexity of software infrastructure management, particularly in public sector cloud deployment, necessitates intelligent decision-support mechanisms. Manual ways for decision making are very complex, results are inconsistent, and most of the times they waste resources. Automated solutions are crucial for optimizing key factors, including urgency, determining how quickly an application should be deployed along with infrastructure type, deployment location, resource allocation, and scalability. This diploma thesis introduces a new decision support system that uses argumentation to automate deployment choices for the Ministry of Digital Governance. The system uses different frameworks to balance competing needs, rules, and policies, which makes decisions both justified and easy to understand. The proposed system leverages Gorgias Cloud for reasoning, based on preferences, and Prolog to formally represent arguments. The system also uses Raison AI, which allows the creation of a complex decision policy, using a no-code symbolic interface to produce Prolog files. The backend was built using Spring Boot, a Java-based framework, to manage the entire process, creating both a decision strategy and YAML configuration files that help automate deployment. The proposed system enables users to define key parameters, such as infrastructure type (on-premise vs. cloud), scalability, resource requirements, location constraints, and urgency levels. Based on these inputs, the system evaluates trade-offs and generates optimised, explainable deployment strategies. A key contribution of this work is its ability to bridge the gap between technical decision-makers and policy stakeholders, providing a transparent and structured decision rationale. The results show, that using argumentation-based automation, can really improve the way government organizations adopt cloud technologies. In the future, the system could be expanded to integrate with DevOps pipelines, learn rules on its own, and even be used for things beyond just managing cloud infrastructures.
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