%0 Conference Paper %1 KiGrScKo2019-ICPE-PowerPrediction %A von Kistowski, Jóakim %A Grohmann, Johannes %A Schmitt, Norbert %A Kounev, Samuel %B Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2019 %I Association for Computing Machinery (ACM) %K Metrics_and_benchmarking_methodologies PRISMA Power Prediction SPEC Statistical_estimation_and_machine_learning descartes t_full myown %P 301--312 %R 10.1145/3297663.3310298 %T Predicting Server Power Consumption from Standard Rating Results %U https://doi.org/10.1145/3297663.3310298 %X Data center providers and server operators try to reduce the power consumption of their servers. Finding an energy efficient server for a specific target application is a first step in this regard. Estimating the power consumption of an application on an unavailable server is difficult, as nameplate power values are generally overestimations. Offline power models are able to predict the consumption accurately, but are usually intended for system design, requiring very specific and detailed knowledge about the system under consideration. In this paper, we introduce an offline power prediction method that uses the results of standard power rating tools. The method predicts the power consumption of a specific application for multiple load levels on a target server that is otherwise unavailable for testing. We evaluate our approach by predicting the power consumption of three applications on different physical servers. Our method is able to achieve an average prediction error of 9.49% for three workloads running on real-world, physical servers. %@ 9781450362399

@inproceedings{KiGrScKo2019-ICPE-PowerPrediction, abstract = {Data center providers and server operators try to reduce the power consumption of their servers. Finding an energy efficient server for a specific target application is a first step in this regard. Estimating the power consumption of an application on an unavailable server is difficult, as nameplate power values are generally overestimations. Offline power models are able to predict the consumption accurately, but are usually intended for system design, requiring very specific and detailed knowledge about the system under consideration. In this paper, we introduce an offline power prediction method that uses the results of standard power rating tools. The method predicts the power consumption of a specific application for multiple load levels on a target server that is otherwise unavailable for testing. We evaluate our approach by predicting the power consumption of three applications on different physical servers. Our method is able to achieve an average prediction error of 9.49% for three workloads running on real-world, physical servers.}, added-at = {2020-04-05T23:19:53.000+0200}, address = {New York, NY, USA}, author = {von Kistowski, J{\'o}akim and Grohmann, Johannes and Schmitt, Norbert and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/2a826f3539bbe4449819b6619b1273c7a/joh.grohmann}, booktitle = {Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering}, doi = {10.1145/3297663.3310298}, interhash = {a71907be8e299d24dceccf56ea75c2c0}, intrahash = {a826f3539bbe4449819b6619b1273c7a}, isbn = {9781450362399}, keywords = {Metrics_and_benchmarking_methodologies PRISMA Power Prediction SPEC Statistical_estimation_and_machine_learning descartes t_full myown}, note = {{Full Paper Acceptance Rate: 18.6\% (13/70)}}, pages = {301--312}, publisher = {Association for Computing Machinery (ACM)}, series = {ICPE '19}, timestamp = {2022-11-16T09:10:42.000+0100}, title = {{Predicting Server Power Consumption from Standard Rating Results}}, url = {https://doi.org/10.1145/3297663.3310298}, venue = {Mumbai, India}, year = 2019 }