%0 Conference Paper %1 KiKo2015-VALUETOOLS %A von Kistowski, Jóakim %A Kounev, Samuel %B Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2015) %D 2015 %K Power Interpolation Tool Performance t_short Automated_model_learning Power-energy_efficient_computing descartes %T Univariate Interpolation-based Modeling of Power and Performance %X Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6\%.

@inproceedings{KiKo2015-VALUETOOLS, abstract = {{Performance and power scale non-linearly with device utilization, making characterization and prediction of energy efficiency at a given load level a challenging issue. A common approach to address this problem is the creation of power or performance state tables for a pre-measured subset of all possible system states. Approaches to determine performance and power for a state not included in the measured subset use simple interpolation, such as nearest neighbor interpolation, or define state switching rules. This leads to a loss in accuracy, as unmeasured system states are not considered. In this paper, we compare different interpolation functions and automatically configure and select functions for a given domain or measurement set. We evaluate our approach by comparing interpolation of measurement data subsets against power and performance measurements on a commodity server. We show that for non-extrapolating models interpolation is significantly more accurate than regression, with our automatically configured interpolation function improving modeling accuracy up to 43.6\%.}}, added-at = {2020-04-05T23:17:40.000+0200}, author = {von Kistowski, J{\'o}akim and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/2ac041f8d703bcdfaab65378cd999a41f/se-group}, booktitle = {Proceedings of the 9th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2015)}, interhash = {a00aa585322647873d7d61f643d0b73e}, intrahash = {ac041f8d703bcdfaab65378cd999a41f}, keywords = {Power Interpolation Tool Performance t_short Automated_model_learning Power-energy_efficient_computing descartes}, month = {December}, timestamp = {2020-10-05T16:27:05.000+0200}, title = {{Univariate Interpolation-based Modeling of Power and Performance}}, year = 2015 }