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Model-Driven Engineering (MDE) introduced the notion of metamodeling as the main means for defining modeling languages. As a well organized engineering discipline, MDE should also have its theory clearly defined in terms of the relationships between key MDE concepts. Following the spirit of MDE, where models are first class citizens, even the MDE theory can be defined by models, or so called megamodels. In this paper, we use Favre's megamodel that was already used for defining linguistic metamodeling. Starting from the premise that this megamodel can also be used for defining other MDE concepts, we use it to specify the notion of ontological metamodeling. Here, we show that in order for this megamodel to be able to fully capture all the concepts of ontological metamodeling, some refinements should be applied to its definition. We also show how these new changes are in the same direction with the work of Kühne in defining linguistic and ontological metamodels.

Hybrid cloud deployment can be an attractive option for companies wanting to deploy software services on scalable public clouds, while still assuming local control over sensitive data resources. A hybrid deployment, despite providing better control, is difficult to design since code must be partitioned and distributed efficiently between public and private premises. This paper describes our research into automated partitioning of software services for hybrid clouds. We have identified two specific shortfalls of existing partitioning research which are important to a hybrid cloud setting: (i) inflexibility in placement of software function execution between public/private hosts and (ii) no support for making explicit tradeoffs between monetary cost and performance. We propose a new software profiling and partitioning framework (called MANTICORE) which addresses these problems. Experiments on an open-source Web application show that the new approach ensures better performance without increasing costs.

Since the introduction in the early nineties, feature models receive a great deal of attention in industry and academia. Industrial success stories in applying feature models for modeling software product lines, and using them for configuring software-intensive systems motivate academia to discover ways to integrate different feature dependencies into the feature model, and automate verified feature configuration. In this paper we demonstrate how ontologies and Semantic Web technologies facilitate seamless integration of required external services and deployment platform capabilities into the feature model. Furthermore, we also contribute with an algorithm for automating staged configuration using Semantic Web reasoners to discover unfeasible features of the feature model.