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Abstract
Cloud computing has been a buzz word over the last decade - it offers great potential benefits for enterprises who migrate their computing systems from local data centers to a Cloud environment. One major obstacle to enterprise adoption of Cloud technologies has been the lack of visibility into migration effort and cost. Currently, there is very limited existing work in the literature. This thesis improves our understanding of this matter by identifying critical indicators of Cloud migration effort.
A taxonomy of migration tasks to the Cloud has been proposed, outlining possible migration tasks that any migration project to the Cloud may encounter. It enables Cloud practitioners to gain an understanding of the specific tasks involved and its implication on the amount of effort required. A methodology, called Cloud Migration Point (CMP), is presented for estimating the size of Cloud migration projects, by recasting a well-known software size estimation model, Function Point, into the context of Cloud migration. The CMP value implies how large the migration project is, and it can be used as an indicator for Cloud migration effort estimation. The process of calculating CMP also assists one in itemizing the migration tasks, and identifying the complexity of each task. This is useful for project planning and management. The empirical validation on the set of data points collected from our survey shows that, with some calibrations, the CMP metric is practically useful as a predictor for effort estimation under a defined set of assumptions. Besides size measurement, other factors also influence the migration effort. We propose a list of external cost factors, which do not affect how migration tasks are designed, but may affect how fast migration tasks can be done, such as development team's experience in software engineering, or experience with the Cloud.
Our overall contribution is to shed light into Cloud migration and the tasks involved, which enables Cloud practitioners to estimate the amount of effort required for the migration of legacy systems into the Cloud. This contributes towards the cost-benefit analysis of whether the benefits of the Cloud exceed the migration effort and other Cloud costs.