Abstract
Several modelling approaches including CloudML emerged to specify the deployment of cloud-based applications and automate the provisioning of computational resources. While CloudML was introduced in the REMICS project, its development continued by ongoing projects, i.e., ARTIST, MODAClouds, and PaaSage. As the evolution of CloudML in the three projects aims for a different goal, a divergence between the current project-specific manifestations of CloudML can be identified. Moreover, as the projects consider different application scenarios, CloudML has been adapted to their needs. In this paper, we distil these needs and investigate how CloudML is currently manifested in the model-based ecosystems employed by the projects. We discuss the main challenges that need to be addressed to achieve a convergence of the current CloudML manifestations.
