Dependability Metrics : Research Workshop Proceedings

Justifying reliance in computer systems is based on some form of evidence about such systems. This in turn implies the existence of scientific techniques to derive such evidence from given systems or predict such evidence of systems. In a general sense, these techniques imply a form of measurement. The workshop Dependability Metrics'', which was held on November 10, 2008, at the University of Mannheim, dealt with all aspects of measuring dependability

Reconstruction of Software Component Architectures and Behaviour Models using Static and Dynamic Analysis

Model-based performance prediction systematically deals with the evaluation of software performance to avoid for example bottlenecks, estimate execution environment sizing, or identify scalability limitations for new usage scenarios. Such performance predictions require up-to-date software performance models. This book describes a new integrated reverse engineering approach for the reconstruction of parameterised software performance models (software component architecture and behaviour)

Automated extraction of palladio component models from running enterprise Java applications

Nowadays, software systems have to fulfill increasingly strin-gent requirements for performance and scalability. To ensure that a system meets its performance requirements during op-eration, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of perfor-mance models of Java EE applications, based on monitor-ing data collected during operation. We extract instances of the Palladio Component Model (PCM)- a performance meta-model targeted at component-based systems. We eval-uate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed. 1

The effects of oar-shaft stiffness and length on rowing biomechanics

This work investigates the effects of oar-shaft stiffness and length on rowing biomechanics. The mechanical properties of the oar-shafts were examined using an end-loaded cantilever system, and theoretical relations were proposed between the mechanics of the oar-shafts and rowing performance. On-water experiments were subsequently conducted and rowing biomechanics measured via the PowerLine Rowing Instrumentation System. The PowerLine system measures force and oar angle on the oarlock, as well as proper boat acceleration. The convergent validity and test-retest reliability of the PowerLine force measurements were determined prior to the on-water experiments. Thereafter, rowers were tested over a set distance using oar-shafts of different stiffness and length. There were slight differences in the biomechanics between rowing with the different oar configurations. However, the measured differences in the biomechanical parameters were on the same order of magnitude as the rower’s inter-stroke inconsistencies

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines

The relationship between search based software engineering and predictive modeling

Search Based Software Engineering (SBSE) is an approach to software engineering in which search based optimization algorithms are used to identify optimal or near optimal solutions and to yield insight. SBSE techniques can cater for multiple, possibly competing objectives and/or constraints and applications where the potential solution space is large and complex. This paper will provide a brief overview of SBSE, explaining some of the ways in which it has already been applied to construction of predictive models. There is a mutually beneficial relationship between predictive models and SBSE. The paper sets out eleven open problem areas for Search Based Predictive Modeling and describes how predictive models also have role to play in improving SBSE

Quantifying and Predicting the Influence of Execution Platform on Software Component Performance

The performance of software components depends on several factors, including the execution platform on which the software components run. To simplify cross-platform performance prediction in relocation and sizing scenarios, a novel approach is introduced in this thesis which separates the application performance profile from the platform performance profile. The approach is evaluated using transparent instrumentation of Java applications and with automated benchmarks for Java Virtual Machines