Migrating existing enterprise software to cloud platforms involves the comparison of competing cloud deployment options (CDOs). A CDO comprises a combination of a specific cloud environment, deployment architecture, and runtime reconfiguration rules for dynamic resource scaling. Our simulator CDOSim can evaluate CDOs, e.g., regarding response times and costs. However, the design space to be searched for well-suited solutions is extremely huge. In this paper, we approach this optimization problem with the novel genetic algorithm CDOXplorer. It uses techniques of the search-based software engineering field and CDOSim to assess the fitness of CDOs. An experimental evaluation that employs, among others, the cloud environments Amazon EC2 and Microsoft Windows Azure, shows that CDOXplorer can find solutions that surpass those of other state-of-the-art techniques by up to 60%. Our experiment code and data and an implementation of CDOXplorer are available as open source software.

SUMMARY The migration of software systems to IaaS (infrastructure as a service)‐ or PaaS (platform as a service)‐based cloud environments enables SaaS providers to benefit from the cloud's merits, such as smoothly scaling up and down existing applications. Our approach, CloudMIG, aims at supporting SaaS providers to perform those migrations. Here, validating the specific constraints that are imposed by a cloud environment constitutes an important early‐phase activity. For example, the access to the file system, number of files, or calls to specific methods may be restricted by cloud providers. Those constraints have to be considered when evaluating the suitability of competing cloud environment candidates. In this paper, we describe CloudMIG's corresponding parts: a generic cloud environment model that incorporates these constraints and appropriate violation detection mechanisms. A software system's conformance can be examined with the assistance of constraint validators. They operate on extracted Knowledge Discovery Meta‐Model‐based system models and can, among others, apply metrics formulated with the Software Metrics Meta‐Model through our metrics execution engine. Additional constraint validators can be plugged into the validation process as needed. In this context, we implemented a prototype and modeled the PaaS environment Google App Engine for Java. We report on a quantitative evaluation regarding the detected constraint violations of five open‐source systems. Copyright © 2012 John Wiley & Sons, Ltd.