Best Graduate Award (Absolventenpreis)
Simon Spinner from the Descartes Research Group (at SDQ) was awarded as the best graduate at the Faculty of Informatics at KIT for the year 2011/12.
Simon Spinner received the Faculty of Informatics’ Best Graduate Award for his MSc thesis with the title “Evaluating Approaches to Resource Demand Estimation” supervised by Fabian Brosig and Samuel Kounev, and overall, for his outstanding results in the “Master of Computer Science” Program at KIT. The MSc thesis presents an in-depth analysis and evaluation of existing approaches to resource demand estimation with respect to their behavior and performance in different application scenarios. The results of the thesis lay the foundation for extended research on this topic, which is continued by the Descartes Research Group as part of several collaborative projects with international partners from industry and academia including VMware, Inc. and Imperial College London. Simon Spinner has been member of the group since 2009 initially working as student assistant and then joining as a full-time researcher after completing his MSc thesis. His PhD topic is in the area of automatic model inference, which includes as part of it the automatic estimation of service resource demand.
Proactively managing the performance and resource efficiency 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 filtering. These approaches differ in their accuracy, their robustness and their applicability. For instance, there are notable differences 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 classification scheme for approaches to resource demand estimation. We implement a sub-set of these estimation approaches and evaluate them in a representative environment. We analyze the influence 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 field of resource demand estimation.