Evaluating approaches to resource demand estimation

Proactively managing the performance and resource e ciency of running software systems requires techniques to predict system performance and resource consumption. Typically, performance predictions are based on performance models that capture the performance-relevant aspects of the considered software system. Building performance models involves the estimation of resource demands, i.e., estimating the time a unit of work spends obtaining service from a resource. A number of approaches to estimating the resource demands of a system already exist, e.g., based on regression analysis or stochastic ltering. These approaches di er in their accuracy, their robustness and their applicability. For instance, there are notable di erences in the amount and type of measurement data that is required as input. However, to the best of our knowledge, a comprehensive evaluation and comparison of these approaches in a representative context does not exist. In this thesis, we give an overview of the state-of-the-art in resource demand estimation and develop a classi cation scheme for approaches to resource demand estimation. We implement a subset of these estimation approaches and evaluate them in a representative environment. We analyze the in uence of various factors of the environment on the estimation accuracy, considering the impact of current technologies, such as multi-core processors and virtualization. Our work improves the comparability of existing estimation approaches and facilitates the selection of an approach in a given application scenario. Additionally, it shows possible directions for future research in the eld of resource demand estimation.