%0 Thesis %1 Spinner2011a %A Spinner, Simon %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2011 %K Automated_model_learning LibReDE Thesis_supervised_by_SE_member Performance Thesis_supervised_by_Fabian_Brosig descartes QPME_Bibliography Statistical_estimation_and_machine_learning se2_mastersthesis %T Evaluating Approaches to Resource Demand Estimation %X 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.

@mastersthesis{Spinner2011a, abstract = {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.}, added-at = {2020-04-08T16:47:46.000+0200}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Spinner, Simon}, biburl = {https://www.bibsonomy.org/bibtex/2e4944fd3a0e59513e4b72af74a53a025/se-group}, interhash = {27ad1779cccd2f5e502e2eca58ec2fc1}, intrahash = {e4944fd3a0e59513e4b72af74a53a025}, keywords = {Automated_model_learning LibReDE Thesis_supervised_by_SE_member Performance Thesis_supervised_by_Fabian_Brosig descartes QPME_Bibliography Statistical_estimation_and_machine_learning se2_mastersthesis}, month = {7}, note = {Best Graduate Award from the Faculty of Informatics}, school = {Karlsruhe Institute of Technology (KIT)}, timestamp = {2020-05-13T11:57:09.000+0200}, title = {{Evaluating Approaches to Resource Demand Estimation}}, year = 2011 }