A Graphical Approach to Modularization and Layering of Metamodels

Modularity is a key aspect in software engineering as it comes with several benefits like reusability, extensibility and maintainability. Although it is a well established concept, it has not received much attention when it comes to model-driven software development. Over time, metamodels tend to evolve and grow in complexity to encompass new aspects and features. If modularization steps are not taken and metamodels are extended intrusively, they can become difficult to maintain and to extend. With the increased complexity, the modularization can become even more challenging. In this work, we present a novel approach to assist the modeler in the task of modularization. Our approach addresses the problem from a graphical perspective. The proposed tool support displays a layered structure, where each layer has certain level of abstraction, and allows the modeler to organize metamodels inside the layers. In this layered structure, the metamodels should only depend on metamodels with the same or a higher abstraction level and should not take part in cyclical dependencies. The tool provides the modeler with full control over the modularization process and full knowledge about the relations between the metamodels, thus facilitating the modularization task greatly

Flexible Graphical Editors for Extensible Modular Meta Models

In model-driven software development, graphical editors can be used to create model instances more effciently and intuitively than with pure XML code. These graphical editors rely on models created on the basis of a meta-model. If such a meta-model is extended invasively not only its code has to be re-generated but also the graphical editor needs to be adapted. When developing multiple extensions, the meta-model as well as the corresponding graphical editor tend to get complex and error-prone. One way of coping with this complexity is to use modular meta-models and extending them noninvasively. However, having multiple meta-model fragments providing extended features is only half the job as equivalent graphical editors are needed as well. This master’s thesis therefore analyzes different types of extensions for meta-models as well as on graphical editor level. Next, a short analysis of extension mechanisms follows. These mechanisms are used for different realizations of extension types. Like the extension types, the mechanisms are also analyzed for both meta-models and for graphical editors. While the classiffcation of extensions resembles one part of this thesis’ concept, their mapping from meta-model level to graphical editor level marks the second part. This mapping is done in order to show possible impacts of a meta-model extension to its corresponding graphical editor. To validate this concept, the analyzed mappings are implemented exemplarily in two different frameworks. Furthermore, the two prototypes show the different possibilities each framework has to offer when it comes to their capabilities of extension. Therefore, this thesis can also be seen as guideline for extending a given graphical editor

Model-Driven Online Capacity Management for Component-Based Software Systems

Capacity management is a core activity when designing and operating distributed software systems. It comprises the provisioning of data center resources and the deployment of software components to these resources. The goal is to continuously provide adequate capacity, i.e., service level agreements should be satisfied while keeping investment and operating costs reasonably low. Traditional capacity management strategies are rather static and pessimistic: resources are provisioned for anticipated peak workload levels. Particularly, enterprise application systems are exposed to highly varying workloads, leading to unnecessarily high total cost of ownership due to poor resource usage efficiency caused by the aforementioned static capacity management approach. During the past years, technologies emerged that enable dynamic data center infrastructures, e. g., leveraged by cloud computing products. These technologies build the foundation for elastic online capacity management, i.e., adapting the provided capacity to workload demands based on a short-term horizon. Because manual online capacity management is not an option, automatic control approaches have been proposed. However, most of these approaches focus on coarse-grained adaptation actions and adaptation decisions are based on aggregated system-level measures. Architectural information about the controlled software system is rarely considered. This thesis introduces a model-driven online capacity management approach for distributed component-based software systems, called SLAstic. The core contributions of this approach are a) modeling languages to capture relevant architectural information about a controlled software system, b) an architecture-based online capacity management framework based on the common MAPE-K control loop architecture, c) model-driven techniques supporting the automation of the approach, d) architectural runtime reconfiguration operations for controlling a system’s capacity, e) as well as an integration of the Palladio Component Model. A qualitative and quantitative evaluation of the approach is performed by case studies, lab experiments, and simulation