Cloud readiness assessment framework and recommendation system

Cloud computing is one of the fastest growing technologies. For developing countries like Ethiopia which has a growing ICT, cloud computing is an attractive choice to adopt. However, the adoption of such a technology should be planned ahead of time taking into consideration the various factors that make adoption successful. The main objective of this research is to propose a cloud readiness assessment framework and an expert system that assesses cloud readiness and recommend which cloud deployment and service model to adopt. The research is grounded by well-studied technological innovation adoption theories: Technology Organization Environment framework (TOE), Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM). Based on these theoretical foundations, a new cloud readiness framework is proposed. A survey is designed based on the framework; using this survey an initial dataset is generated and expanded using synthetic data generator. The expert system relies on predictive modeling for assessing cloud readiness. So, using Weka machine learning platform, J48 decision tree algorithm is experimented using various settings, to train and obtain acceptable model accuracy. Training is performed on the original dataset and synthetically generated dataset. The best obtained model accuracy is 75% with the original dataset.