%0 Thesis %1 Kistowski2014 %A von Kistowski, Jóakim %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2014 %K Automated_model_learning Award Cloud Instrumentation_profiling_and_workload_characterization LIMBO Meta-models Supervised_by_Nikolas_Herbst Thesis_supervised_by_SE_member Tool descartes t_studentthesis %T Modeling Variations in Load Intensity Profiles %X Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9\% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.

@mastersthesis{Kistowski2014, abstract = {{Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9\% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.}}, added-at = {2020-04-08T16:48:09.000+0200}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {von Kistowski, J{\'o}akim}, biburl = {https://www.bibsonomy.org/bibtex/255d743dddaa73b0335fded78b178e29b/se-group}, interhash = {8cab6e846ec7d93aac1c0a627491ecad}, intrahash = {55d743dddaa73b0335fded78b178e29b}, keywords = {Automated_model_learning Award Cloud Instrumentation_profiling_and_workload_characterization LIMBO Meta-models Supervised_by_Nikolas_Herbst Thesis_supervised_by_SE_member Tool descartes t_studentthesis}, month = {March}, note = {<b>Gesellschaft zur F{\"o}rderung des Forschungstransfers (GFFT) Prize "Best Master Thesis"</b>}, school = {{Karlsruhe Institute of Technology (KIT)}}, timestamp = {2020-10-06T09:48:08.000+0200}, title = {{Modeling Variations in Load Intensity Profiles}}, type = {{Master Thesis}}, year = 2014 }