Ralf Reussner

Bio: Ralf Reussner is an academic researcher from Karlsruhe Institute of Technology. The author has contributed to research in topics: Software system & Software. The author has an hindex of 35, co-authored 253 publications receiving 5771 citations. Previous affiliations of Ralf Reussner include Monash University, Caulfield campus & University of Kiel.

Elasticity in Cloud Computing: What It Is, and What It Is Not.

Abstract: Originating from the field of physics and economics, the term elasticity is nowadays heavily used in the context of cloud computing. In this context, elasticity is commonly understood as the ability of a system to automatically provision and deprovision computing resources on demand as workloads change. However, elasticity still lacks a precise definition as well as representative metrics coupled with a benchmarking methodology to enable comparability of systems. Existing definitions of elasticity are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related terms such as scalability and efficiency; the proposed measurement methodologies do not provide means to quantify elasticity without mixing it with efficiency or scalability aspects. In this short paper, we propose a precise definition of elasticity and analyze its core properties and requirements explicitly distinguishing from related terms such as scalability and efficiency. Furthermore, we present a set of appropriate elasticity metrics and sketch a new elasticity tailored benchmarking methodology addressing the special requirements on workload design and calibration.

Reliability prediction for component-based software architectures

Abstract: One of the motivations for specifying software architectures explicitly is the use of high level structural design information for improved control and prediction of software system quality attributes. In this paper, we present an approach for determining the reliability of component-based software architectures.Our method is based on rich architecture definition language (RADL) oriented towards modem industrial middleware platforms, such as Microsoft's. NET and Sun's EJB. Our methods involve parameterised contractual specifications based on state machines and thus permits efficient static analysis.We show how RADL allows software architects to predict component reliability through compositional analysis of usage profiles and of environment component reliability. We illustrate our approach with an e-commerce example and report about empirical measurements which confirm our analytical reliability prediction through monitoring in our reliability test-bed. Our evaluation confirms that prediction accuracy for software components necessitates modelling the behaviour of binary components and the dependency of provided services on required components. Fortunately, our measurements also show that an abstract protocol view of that behaviour is sufficient to predict reliability with high accuracy. The reliability of a component most strongly depends on its environment. Therefore, we advocate a reliability model parameterized by required component reliability in a deployment context.

Model-Based performance prediction with the palladio component model

Abstract: One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independent from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influence factors like the hardware platform or the usage profile into account. In our approach, we use the Palladio Component Model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. In this paper, we present our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy can be sufficient to support the evaluation of architectural design decisions.

Handbook of software reliability engineering

Abstract: Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

Intelligent Transportation Systems

Abstract: The increasing need for mobility has brought about significant changes in transportation infrastructures. Inefficiencies cause enormous losses of time, decrease in the level of safety for both vehicles and pedestrians, high pollution, degradation of quality of life, and huge waste of nonrenewable fossil energy.The scope of this article is to introduce novel functionality for providing knowledge to vehicles, thus jointly managing traffic and safety. This will be achieved through the design of the proposed functionality, which, at a high level, will comprise (1) sensor networks formed by vehicles of a certain vicinity that exchange traffic-related information, (2) cognitive management functionality placed inside the vehicles for inferring knowledge and experience, and (3) cognitive management functionality in the overall transportation infrastructure. The goal of the aforementioned three main components shall be to issue directives to the drivers and the overall transportation infrastructure valuable in context handling.

Interpolation and SAT-based model checking

Abstract: We consider a fully SAT-based method of unbounded symbolic model checking based on computing Craig interpolants. In benchmark studies using a set of large industrial circuit verification instances, this method is greatly more efficient than BDD-based symbolic model checking, and compares favorably to some recent SAT-based model checking methods on positive instances.