With the increasing importance of reliability in business and industrial IT systems, new techniques for architecture-based software reliability prediction are becoming an integral part of the development process. This dissertation thesis introduces a novel reliability modelling and prediction technique that considers the software architecture with its component structure, control and data flow, recovery mechanisms, its deployment to distributed hardware resources and the system�s usage profile.
Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. This dissertation proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predicting the failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduce the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component dependencies are combined into one model which employs Bayesian network theory to represent failure propagation. The combined model is continuously updated at runtime and makes predictions for individual components, as well as inferring their effects on other components and the whole system.
A new, quantitative architecture simulation approach to software design that circumvents costly testing cycles by modeling quality of service in early design states. Too often, software designers lack an understanding of the effect of design decisions on such quality attributes as performance and reliability. This necessitates costly trial-and-error testing cycles, delaying or complicating rollout. This book presents a new, quantitative architecture simulation approach to software design, which allows software engineers to model quality of service in early design stages. It presents the first simulator for software architectures, Palladio, and shows students and professionals how to model reusable, parametrized components and configured, deployed systems in order to analyze service attributes. The text details the key concepts of Palladio's domain-specific modeling language for software architecture quality and presents the corresponding development stage. It describes how quality information can be used to calibrate architecture models from which detailed simulation models are automatically derived for quality predictions. Readers will learn how to approach systematically questions about scalability, hardware resources, and efficiency. The text features a running example to illustrate tasks and methods as well as three case studies from industry. Each chapter ends with exercises, suggestions for further reading, and “takeaways” that summarize the key points of the chapter. The simulator can be downloaded from a companion website, which offers additional material. The book can be used in graduate courses on software architecture, quality engineering, or performance engineering. It will also be an essential resource for software architects and software engineers and for practitioners who want to apply Palladio in industrial settings.
This cumulative habilitation thesis, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.
Die modellbasierte Performancevorhersage ist ein bekanntes Konzept zur Gewährleistung der Softwarequalität. Derzeitige Ansätze basieren auf einem Modell mit einer Metrik, was zu ungenauen Vorhersagen für moderne Architekturen führt. In dieser Arbeit wird ein Multi-Strategie-Ansatz zur Erweiterung von Performancevorhersagemodellen zur Unterstützung von Multicore-Architekturen vorgestellt, in Palladio implementiert und dadurch die Genauigkeit der Vorhersage deutlich verbessert. - Model-based performance prediction is a well-known concept to ensure the quality of software. Current approaches are based on a single-metric model, which leads to inaccurate predictions for modern architectures. This thesis presents a multi-strategies approach to extend performance prediction models to support multicore architectures. We implemented the strategies into Palladio and significantly increased the performance prediction power.
This dissertation thesis presents an approach enabling the modelling and quality-of-service prediction of event-based systems at the architecture-level. Applying a two-step model refinement transformation, the approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques.
We present a novel performance modeling approach tailored to I/O performance prediction in virtualized environments. The main idea is to identify important performance-influencing factors and to develop storage-level I/O performance models. To increase the practical applicability of these models, we combine the low-level I/O performance models with high-level software architecture models. Our approach is validated in a variety of case studies in state-of-the-art, real-world environments.
