%0 Conference Paper %1 WiDlBrSpKoKr2015-EPEW-LibredePMWT %A Willnecker, Felix %A Dlugi, Markus %A Brunnert, Andreas %A Spinner, Simon %A Kounev, Samuel %A Krcmar, Helmut %B Computer Performance Engineering - Proceedings of the 12th European Workshop (EPEW 2015) %D 2015 %E Beltrán, Marta %E Knottenbelt, William %E Bradley, Jeremy %I Springer %K Automated_model_learning LibReDE Online_monitoring_and_forecasting Performance SPEC Statistical_estimation_and_machine_learning descartes t_workshop myown %P 115-129 %T Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques %V 9272 %X Resource demands are a core aspect of performance models. They describe how an operation utilizes a resource and therefore influence the systems performance metrics: response time, resource utilization and throughput. Such demands can be determined by two extraction classes: direct measurement or demand estimation. Selecting the best suited technique depends on available tools, acceptable measurement overhead and the level of granularity necessary for the performance model. This work compares two direct measurement techniques and an adaptive estimation technique based on multiple statistical approaches to evaluate strengths and weaknesses of each technique.We conduct a series of experiments using the SPECjEnterprise2010 industry benchmark and an automatic performance model generator for architecture-level performance models based on the Palladio Component Model. To compare the techniques we conduct two experiments with different levels of granularity on a standalone system, followed by one experiment using a distributed SPECjEnterprise2010 deployment combining both extraction classes for generating a full-stack performance model.

@inproceedings{WiDlBrSpKoKr2015-EPEW-LibredePMWT, abstract = {Resource demands are a core aspect of performance models. They describe how an operation utilizes a resource and therefore influence the systems performance metrics: response time, resource utilization and throughput. Such demands can be determined by two extraction classes: direct measurement or demand estimation. Selecting the best suited technique depends on available tools, acceptable measurement overhead and the level of granularity necessary for the performance model. This work compares two direct measurement techniques and an adaptive estimation technique based on multiple statistical approaches to evaluate strengths and weaknesses of each technique.We conduct a series of experiments using the SPECjEnterprise2010 industry benchmark and an automatic performance model generator for architecture-level performance models based on the Palladio Component Model. To compare the techniques we conduct two experiments with different levels of granularity on a standalone system, followed by one experiment using a distributed SPECjEnterprise2010 deployment combining both extraction classes for generating a full-stack performance model.}, added-at = {2020-04-05T23:11:07.000+0200}, author = {Willnecker, Felix and Dlugi, Markus and Brunnert, Andreas and Spinner, Simon and Kounev, Samuel and Krcmar, Helmut}, biburl = {https://www.bibsonomy.org/bibtex/2f5a7c65e8b4cbf3089bf1653e0732af9/samuel.kounev}, booktitle = {Computer Performance Engineering - Proceedings of the 12th European Workshop (EPEW 2015)}, editor = {Beltr{\'a}n, Marta and Knottenbelt, William and Bradley, Jeremy}, interhash = {d5f80955918594812027057d923ab02f}, intrahash = {f5a7c65e8b4cbf3089bf1653e0732af9}, keywords = {Automated_model_learning LibReDE Online_monitoring_and_forecasting Performance SPEC Statistical_estimation_and_machine_learning descartes t_workshop myown}, month = {{August}}, pages = {115-129}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2020-10-06T14:13:08.000+0200}, title = {{Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques}}, volume = 9272, year = 2015 }