J. von Kistowski, N. Herbst, and S. Kounev. Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014), page 1--4. New York, NY, USA, ACM, (March 2014)
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, we 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 (HLDLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts and noise parts. We demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy through comparison with a real life trace.
Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)
%0 Conference Paper
%1 KiHeKo2014-LT-DLIM
%A von Kistowski, Jóakim
%A Herbst, Nikolas Roman
%A Kounev, Samuel
%B Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)
%C New York, NY, USA
%D 2014
%I ACM
%K Automated_model_learning Elasticity Instrumentation_profiling_and_workload_characterization LIMBO Meta-models Metrics_and_benchmarking_methodologies Performance Resource_management descartes t_workshop myown
%P 1--4
%T Modeling Variations in Load Intensity over Time
%U http://doi.acm.org/10.1145/2577036.2577037
%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, we 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 (HLDLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts and noise parts. We demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy through comparison with a real life trace.
@inproceedings{KiHeKo2014-LT-DLIM,
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, we 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 (HLDLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts and noise parts. We demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy through comparison with a real life trace.}},
added-at = {2020-04-05T23:07:51.000+0200},
address = {New York, NY, USA},
author = {von Kistowski, J{\'o}akim and Herbst, Nikolas Roman and Kounev, Samuel},
biburl = {https://www.bibsonomy.org/bibtex/29c9b3200995cfb938f18c25ef9f7ebe9/samuel.kounev},
booktitle = {Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014)},
interhash = {f4ba563d389b708b46833d05ef6414b8},
intrahash = {9c9b3200995cfb938f18c25ef9f7ebe9},
keywords = {Automated_model_learning Elasticity Instrumentation_profiling_and_workload_characterization LIMBO Meta-models Metrics_and_benchmarking_methodologies Performance Resource_management descartes t_workshop myown},
month = {March},
pages = {1--4},
publisher = {ACM},
timestamp = {2021-08-19T04:00:35.000+0200},
title = {{Modeling Variations in Load Intensity over Time}},
url = {http://doi.acm.org/10.1145/2577036.2577037},
year = 2014
}