AbstractCloud-native architecture has become an essential part of the cloud computing paradigm with the capacity for improved horizontal and vertical scalability, automation, usability, and multi- tenancy. Cloud-native architecture continues to be used and adopted by enterprises across the globe. There are, however, slow adoption rates in lower-middle-income countries, different researchers have discussed several factors responsible for the slow adoption. One of the significant factors is the need for more knowledge about the architecture and the decision to determine its suitability for the enterprise. The decision support system in which the enterprise stakeholder can depend on to assist in making the needed and correct decision are unavailable. This research produced a step-by-step decision support framework for achieving a stress-free cloud-native architecture adoption.
To understand the enterprise's needs concerning adopting cloud-native architecture, this study used a combination of the Theory of Planned Behaviour (TPB) and the Technology Adoption Model (TAM) to identify the intention to adopt cloud-native architecture. Questionnaire is used in collecting data and then analyse using both the descriptive and multivariate statistical analysis technique. The findings from this analysis showed that the complexity of the architecture, lack of technical knowledge and attitude towards new ideas and technology, significantly affect the enterprise's view of porting or migrating into the cloud-native architecture. The output of the data analysis was then used to improve the decision-support framework by adding significant factors to the framework as a task that the enterprise needs to consider before, during and after porting into cloud-native architecture. Likewise, the framework made a provision for cloud deployment models as decisions or tasks to be considered by the enterprise because it is an essential part of cloud-native adoption that can is scarce in many clouds computing decision-supporting systems.
The decision-support framework was evaluated and validated through expert review and expert system. Finally, a novel holistic decision-supporting system which could help users or enterprises make informed decisions about porting into cloud-native architecture less reconfiguration of the data or application was introduced and developed. Also, an expert system was develop from the framework, which was already validated via the expert review, the framework combination, and the expert system supporting the organisation in decision-making.
|Date of Award||4 Oct 2023|
|Supervisor||Christina Johanna Fitch (Supervisor) & Olumuyiwa Matthew (Supervisor)|