With an increasing number of cloud computing offerings in the market, migrating an existing computational infrastructure to the cloud requires comparison of different offers in order to find the most suitable configuration. Cloud providers offer many configuration options, such as location, purchasing mode, redundancy, and extra storage. Often, the information about such options is not well organised. This leads to large and unstructured configuration spaces, and turns the comparison into a tedious, error prone search problem for the customers. In this work we focus on supporting customer decision making for selecting the most suitable cloud configuration in terms of infrastructural requirements and cost. We achieve this by means of variability modelling and analysis techniques. Firstly, we structure the configuration space of an IaaS using feature models, usually employed for the modelling of variability intensive systems, and present the case study of the Amazon EC2. Secondly, we assist the configuration search process. Feature models enable the use of different analysis operations that, among others, automate the search of optimal configurations. Results of our analysis show how our approach, with a negligible analysis time, outperforms commercial approaches in terms of expressiveness and accuracy. (C) 2015 Elsevier B.V. All rights reserved.
