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Predicting Server Power Consumption from Standard Rating Results

, , , and . Proceedings of the 19th ACM/SPEC International Conference on Performance Engineering, page 301--312. New York, NY, USA, Association for Computing Machinery (ACM), (2019)Full Paper Acceptance Rate: 18.6\% (13/70).
DOI: 10.1145/3297663.3310298

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Common Errors and Assumptions in Energy Measurement and Management. Technical Talk at Symposium on Software Performance (SSP) 2015, (November 2015)Scenario-based Resilience Evaluation and Improvement of Microservice Architectures: An Experience Report., , , , , and . ECSA (Companion), volume 2978 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques., , , , , and . MASCOTS, page 309-322. IEEE Computer Society, (2019)TeaStore - A Micro-Service Application for Benchmarking, Modeling and Resource Management Research. Talk at Workshop on Hot Topics in Cloud Computing Performance 2018, (April 2018)Modeling Variations in Load Intensity Profiles. Karlsruhe Institute of Technology (KIT), Am Fasanengarten 5, 76131 Karlsruhe, Germany, Master Thesis, (March 2014)<b>Gesellschaft zur Förderung des Forschungstransfers (GFFT) Prize "Best Master Thesis"</b>.Measuring, Rating, and Predicting the Energy Efficiency of Servers. University of Würzburg, Germany, (March 2019)Measuring, Rating, and Predicting the Energy Efficiency of Servers (Messung, Bewertung und Vorhersage von Serverenergieeffizienz). Würzburg University, Germany, (2019)base-search.net (ftunivwuerz:oai:opus.bibliothek.uni-wuerzburg.de:17847).TeaStore: A Micro-Service Reference Application for Cloud Researchers., , , , , , and . UCC Companion, page 11-12. IEEE, (2018)TeaStore - A Micro-Service Reference Application for Performance Engineers., , , , , and . ICPE Companion, page 47-48. ACM, (2019)LIMBO - A Load Intensity Modeling Platform. Talk at SPEC RG Annual Meeting 2014, (March 2014)