%0 Conference Paper %1 KiHeKo2014-SOSP-LIMBO %A von Kistowski, Jóakim %A Herbst, Nikolas Roman %A Kounev, Samuel %B Proceedings of the Symposium on Software Performance 2014 %D 2014 %I University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology %K LIMBO Award Metrics_and_benchmarking_methodologies Performance Automated_model_learning Resource_management Instrumentation_profiling_and_workload_characterization Meta-models t_poster myown Tool descartes %P 131--140 %T Using and Extending LIMBO for the Descriptive Modeling of Arrival Behaviors %X LIMBO is a tool for the creation of load profiles with variable intensity over time both from scratch and from existing data. Primarily, LIMBO's intended use is the description of load arrival behaviours in open workloads. Specifically, LIMBO can be employed for the creation of custom request or user arrival time-stamps or for the re-parameterization of existing traces. LIMBO bases on the Descartes Load Intensity Model (DLIM) for the formalized description of its load intensity profiles. The DLIM formalism can be understood as a structure for piece-wise defined and combined mathematical functions. We outline DLIM and its elements and demonstrate its integration within LIMBO. LIMBO is capable of generating request or user arrival time stamps from DLIM instances. In a next step, these generated time-stamps can be used for both open workload based benchmarking and simulations. The TimestampTimer plug-in for JMeter already allows the former. LIMBO also offers a range of tools for easy load intensity modeling and analysis, including, but not limited to, a visual decomposition of load intensity time-series into seasonal and trend parts, a simplified load intensity model as part of a model creation wizard, and an automated model instance extractor. As part of our LIMBO tutorial, we explain these features in detail. We demonstrate common use cases for LIMBO and show how they are supported. We also focus on LIMBOs extensible architecture and discuss how to use LIMBO as part of another tool-chain.

@inproceedings{KiHeKo2014-SOSP-LIMBO, abstract = {{LIMBO is a tool for the creation of load profiles with variable intensity over time both from scratch and from existing data. Primarily, LIMBO's intended use is the description of load arrival behaviours in open workloads. Specifically, LIMBO can be employed for the creation of custom request or user arrival time-stamps or for the re-parameterization of existing traces. LIMBO bases on the Descartes Load Intensity Model (DLIM) for the formalized description of its load intensity profiles. The DLIM formalism can be understood as a structure for piece-wise defined and combined mathematical functions. We outline DLIM and its elements and demonstrate its integration within LIMBO. LIMBO is capable of generating request or user arrival time stamps from DLIM instances. In a next step, these generated time-stamps can be used for both open workload based benchmarking and simulations. The TimestampTimer plug-in for JMeter already allows the former. LIMBO also offers a range of tools for easy load intensity modeling and analysis, including, but not limited to, a visual decomposition of load intensity time-series into seasonal and trend parts, a simplified load intensity model as part of a model creation wizard, and an automated model instance extractor. As part of our LIMBO tutorial, we explain these features in detail. We demonstrate common use cases for LIMBO and show how they are supported. We also focus on LIMBOs extensible architecture and discuss how to use LIMBO as part of another tool-chain.}}, added-at = {2020-04-05T23:16:45.000+0200}, author = {von Kistowski, J{\'o}akim and Herbst, Nikolas Roman and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/2abea36147d9dd0cc2bde91934d8d03b0/nikolas.herbst}, booktitle = {Proceedings of the Symposium on Software Performance 2014}, interhash = {2107e30fa990c2bc36379691d950dfd2}, intrahash = {abea36147d9dd0cc2bde91934d8d03b0}, keywords = {LIMBO Award Metrics_and_benchmarking_methodologies Performance Automated_model_learning Resource_management Instrumentation_profiling_and_workload_characterization Meta-models t_poster myown Tool descartes}, month = {November}, note = {<b>Best Poster Award</b>}, pages = {131--140}, publisher = {University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology}, timestamp = {2020-10-05T16:29:16.000+0200}, title = {{Using and Extending LIMBO for the Descriptive Modeling of Arrival Behaviors}}, year = 2014 }