Next generation IT infrastructures are highly driven by virtualization technology. The latter enables flexible and efficient resource sharing allowing to improve system agility and reduce costs for IT services. Due to the sharing of resources and the increasing requirements of modern applications on I/O processing, the performance of storage systems is becoming a crucial factor. In particular, when migrating or consolidating different applications the impact on their performance behavior is often an open question. Performance modeling approaches help to answer such questions, a prerequisite, however, is to find an appropriate workload characterization that is both easy to obtain from applications as well as sufficient to capture the important characteristics of the application. In this paper, we present an automated workload characterization approach that extracts a workload model to represent the main aspects of I/O-intensive applications using relevant workload parameters, e.g., request size, read-write ratio, in virtualized environments. Once extracted, workload models can be used to emulate the workload performance behavior in real-world scenarios like migration and consolidation scenarios. We demonstrate our approach in the context of two case studies of representative system environments. We present an in-depth evaluation of our workload characterization approach showing its effectiveness in workload migration and consolidation scenarios. We use an IBM System z equipped with an IBM DS8700 and a Sun Fire system as state-of-the-art virtualized environments. Overall, the evaluation of our workload characterization approach shows promising results to capture the relevant factors of I/O-intensive applications.

Quantitative prediction of non-functional properties, such as performance, reliability, and costs, of software architectures supports systematic software engineering. Even though there usually is a rough idea on bounds for quality of service, the exact required values may be unclear and subject to trade-offs. Designing architectures that exhibit such good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. Automated approaches search the design space with multi-objective metaheuristics such as evolutionary algorithms. However, as quality prediction for a single architecture is computationally expensive, these approaches are time consuming. In this work, we enhance an automated improvement approach to take into account bounds for quality of service in order to focus the search on interesting regions of the objective space, while still allowing trade-offs after the search. We compare two different constraint handling techniques to consider the bounds. To validate our approach, we applied both techniques to an architecture model of a component-based business information system. We compared both techniques to an unbounded search in 4 scenarios. Every scenario was examined with 10 optimization runs, each investigating around 1600 architectural candidates. The results indicate that the integration of quality of service bounds during the optimization process can improve the quality of the solutions found, however, the effect depends on the scenario, i.e. the problem and the quality requirements. The best results were achieved for costs requirements: The approach was able to decrease the time needed to find good solutions in the interesting regions of the objective space by 25% on average.