André Bauer

Prof. Dr. rer. nat. André Bauer

Chair of Software Engineering (Informatik II)
Department of Computer Science
University of Würzburg

Am Hubland, 97074 Würzburg

Informatikgebäude, 1.OG, Room A107/8

Phone: +49 (931) 31 86027

Email: andre.bauer@uni-wuerzburg.de

Google Scholar profile

ResearchGate profile

ORCID: 0000-0002-5582-8812

List of all publications: André Bauer

Selected Publications:

Libra: A Benchmark for Time Series Forecasting Methods. Bauer, Andr{é}; Z{ü}fle, Marwin; Eismann, Simon; Grohmann, Johannes; Herbst, Nikolas; Kounev, Samuel; in Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) (2021).
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In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case. Therefore, the choice of the forecasting method remains a mandatory expert task. However, expert knowledge cannot be fully automated. To establish a level playing field for evaluating the performance of time series forecasting methods in a broad setting, we propose Libra, a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Based on this benchmark, we perform a comprehensive evaluation to compare different existing time series forecasting methods.

@inproceedings{bauer2021benchmark, abstract = {In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case. Therefore, the choice of the forecasting method remains a mandatory expert task. However, expert knowledge cannot be fully automated. To establish a level playing field for evaluating the performance of time series forecasting methods in a broad setting, we propose Libra, a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Based on this benchmark, we perform a comprehensive evaluation to compare different existing time series forecasting methods.}, address = {New York, NY, USA}, author = {Bauer, Andr{\'e} and Z{\"u}fle, Marwin and Eismann, Simon and Grohmann, Johannes and Herbst, Nikolas and Kounev, Samuel}, booktitle = {Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE)}, keywords = {benchmark}, month = {04}, organization = {ACM}, title = {Libra: A Benchmark for Time Series Forecasting Methods}, year = 2021 }

%0 Conference Paper %1 bauer2021benchmark %A Bauer, Andr{é} %A Z{ü}fle, Marwin %A Eismann, Simon %A Grohmann, Johannes %A Herbst, Nikolas %A Kounev, Samuel %B Proceedings of the 12th ACM/SPEC International Conference on Performance Engineering (ICPE) %C New York, NY, USA %D 2021 %R 10.1145/3427921.3450241 %T Libra: A Benchmark for Time Series Forecasting Methods %U https://dl.acm.org/doi/10.1145/3427921.3450241 %X In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case. Therefore, the choice of the forecasting method remains a mandatory expert task. However, expert knowledge cannot be fully automated. To establish a level playing field for evaluating the performance of time series forecasting methods in a broad setting, we propose Libra, a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Based on this benchmark, we perform a comprehensive evaluation to compare different existing time series forecasting methods.
{Time Series Forecasting for Self-Aware Systems}. Bauer, Andr{é}; Z{ü}fle, Marwin; Herbst, Nikolas; Zehe, Albin; Hotho, Andreas; Kounev, Samuel; in Proceedings of the IEEE (2020). 108(7) 1068–1093.
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@article{BaZuHeZeHoKo-PIEEE-Forecasting, author = {Bauer, Andr{\'e} and Z{\"u}fle, Marwin and Herbst, Nikolas and Zehe, Albin and Hotho, Andreas and Kounev, Samuel}, journal = {Proceedings of the IEEE}, keywords = {t_journalmagazine}, month = {07}, number = 7, pages = {1068 - 1093}, title = {{Time Series Forecasting for Self-Aware Systems}}, volume = 108, year = 2020 }

%0 Journal Article %1 BaZuHeZeHoKo-PIEEE-Forecasting %A Bauer, Andr{é} %A Z{ü}fle, Marwin %A Herbst, Nikolas %A Zehe, Albin %A Hotho, Andreas %A Kounev, Samuel %D 2020 %J Proceedings of the IEEE %N 7 %P 1068 - 1093 %R 10.1109/JPROC.2020.2983857 %T {Time Series Forecasting for Self-Aware Systems} %V 108
Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. Bauer, Andr{é}; Z{ü}fle, Marwin; Herbst, Nikolas; Kounev, Samuel; Curtef, Valentin; in Proceedings of the 36th International Conference on Data Engineering (ICDE) (2020).
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@inproceedings{BaZuHeKoCu-ICDE-Telescope, author = {Bauer, Andr{\'e} and Z{\"u}fle, Marwin and Herbst, Nikolas and Kounev, Samuel and Curtef, Valentin}, booktitle = {Proceedings of the 36th International Conference on Data Engineering (ICDE)}, keywords = {Prediction}, month = {04}, title = {Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field}, year = 2020 }

%0 Conference Paper %1 BaZuHeKoCu-ICDE-Telescope %A Bauer, Andr{é} %A Z{ü}fle, Marwin %A Herbst, Nikolas %A Kounev, Samuel %A Curtef, Valentin %B Proceedings of the 36th International Conference on Data Engineering (ICDE) %D 2020 %T Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field
Methodological Principles for Reproducible Performance Evaluation in Cloud Computing. Papadopoulos, Alessandro Vittorio; Versluis, Laurens; Bauer, Andr{é}; Herbst, Nikolas; von Kistowski, J{ó}akim; Ali-Eldin, Ahmed; Abad, Cristina; Amaral, J. Nelson; Tuma, Petr; Iosup, Alexandru; in IEEE Transactions on Software Engineering (2019). 47(8) 1528–1543.
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@article{PaVeBaHeKiAEAbAmTuIo2019-TSE-MethodologicalPrinciples, author = {Papadopoulos, Alessandro Vittorio and Versluis, Laurens and Bauer, Andr{\'e} and Herbst, Nikolas and von Kistowski, J{\'o}akim and Ali-Eldin, Ahmed and Abad, Cristina and Amaral, J. Nelson and Tuma, Petr and Iosup, Alexandru}, journal = {IEEE Transactions on Software Engineering}, keywords = {SPEC}, month = {07}, number = 8, pages = {1528-1543}, title = {Methodological Principles for Reproducible Performance Evaluation in Cloud Computing}, volume = 47, year = 2019 }

%0 Journal Article %1 PaVeBaHeKiAEAbAmTuIo2019-TSE-MethodologicalPrinciples %A Papadopoulos, Alessandro Vittorio %A Versluis, Laurens %A Bauer, Andr{é} %A Herbst, Nikolas %A von Kistowski, J{ó}akim %A Ali-Eldin, Ahmed %A Abad, Cristina %A Amaral, J. Nelson %A Tuma, Petr %A Iosup, Alexandru %D 2019 %J IEEE Transactions on Software Engineering %N 8 %P 1528-1543 %R 10.1109/TSE.2019.2927908 %T Methodological Principles for Reproducible Performance Evaluation in Cloud Computing %V 47
{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}. Bauer, Andr{é}; Herbst, Nikolas; Spinner, Simon; Ali-Eldin, Ahmed; Kounev, Samuel; in IEEE Transactions on Parallel and Distributed Systems (2019). 30(4) 800–813.
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@article{BaHeSpAEKo2019-TPDS-ChameleonAutoscaler, author = {Bauer, Andr{\'e} and Herbst, Nikolas and Spinner, Simon and Ali-Eldin, Ahmed and Kounev, Samuel}, journal = {IEEE Transactions on Parallel and Distributed Systems}, keywords = {Self-adaptive-systems}, month = {04}, number = 4, pages = {800–813}, publisher = {IEEE}, title = {{Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field}}, volume = 30, year = 2019 }

%0 Journal Article %1 BaHeSpAEKo2019-TPDS-ChameleonAutoscaler %A Bauer, Andr{é} %A Herbst, Nikolas %A Spinner, Simon %A Ali-Eldin, Ahmed %A Kounev, Samuel %D 2019 %I IEEE %J IEEE Transactions on Parallel and Distributed Systems %N 4 %P 800--813 %T {Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field} %V 30
{TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research}. von Kistowski, J{ó}akim; Eismann, Simon; Schmitt, Norbert; Bauer, Andr{é}; Grohmann, Johannes; Kounev, Samuel; in Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (2018). 223–236.
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Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.

@inproceedings{KiEiScBaGrKo2018-MASCOTS-TeaStore, abstract = {Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.}, author = {von Kistowski, J{\'o}akim and Eismann, Simon and Schmitt, Norbert and Bauer, Andr{\'e} and Grohmann, Johannes and Kounev, Samuel}, booktitle = {Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems}, keywords = {Power}, month = {09}, note = {{Acceptance Rate: 29.5\% (23/78)}}, pages = {223–236}, publisher = {IEEE Computer Society}, series = {MASCOTS '18}, title = {{TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research}}, year = 2018 }

%0 Conference Paper %1 KiEiScBaGrKo2018-MASCOTS-TeaStore %A von Kistowski, J{ó}akim %A Eismann, Simon %A Schmitt, Norbert %A Bauer, Andr{é} %A Grohmann, Johannes %A Kounev, Samuel %B Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems %D 2018 %I IEEE Computer Society %P 223--236 %R 10.1109/MASCOTS.2018.00030 %T {TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research} %U https://doi.org/10.1109/MASCOTS.2018.00030 %X Modern distributed applications offer complex performance behavior and many degrees of freedom regarding deployment and configuration. Researchers employ various methods of analysis, modeling, and management that leverage these degrees of freedom to predict or improve non-functional properties of the software under consideration. In order to demonstrate and evaluate their applicability in the real world, methods resulting from such research areas require test and reference applications that offer a range of different behaviors, as well as the necessary degrees of freedom. Existing production software is often inaccessible for researchers or closed off to instrumentation. Existing testing and benchmarking frameworks, on the other hand, are either designed for specific testing scenarios, or they do not offer the necessary degrees of freedom. Further, most test applications are difficult to deploy and run, or are outdated. In this paper, we introduce the TeaStore, a state-of-the-art micro-service-based test and reference application. TeaStore offers services with different performance characteristics and many degrees of freedom regarding deployment and configuration to be used as a benchmarking framework for researchers. The TeaStore allows evaluating performance modeling and resource management techniques; it also offers instrumented variants to enable extensive run-time analysis. We demonstrate TeaStore's use in three contexts: performance modeling, cloud resource management, and energy efficiency analysis. Our experiments show that TeaStore can be used for evaluating novel approaches in these contexts and also motivates further research in the areas of performance modeling and resource management.

Teaching

Coordination of the exercises on the bachelor's lecture "Operating Systems" by Prof. Samuel Kounev, University of Würzburg
Coordination of the practical course on the bachelor's lecture " Softwarepraktikum" by Prof. Samuel Kounev, University of Würzburg
Supervision Bachelor and Master Theses

Functions and Memberships

SPEC Research Group, Predictive Data Analytics Group
Founder
SPEC Research Group, Cloud Working Group
Release Manager
SPEC Research Group
Newsletter Editor

Awards and Recognitions

Preis des Institus für Informatik für eine Promotion mit dem Prädikat summa cum laude from the Universtiy of Würzburg, July, 2021.
Gemeinsamer Promotionspreis der Unterfränkischen Gedenkjahrstiftung für Wissenschaft und der Universität Würzburg from the University of Würzburg for his doctoral dissertation titled “Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases” supervised by Samuel Kounev, May, 2021. [more]
Summa cum laude for his doctoral dissertation titled “Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases” supervised by Samuel Kounev, December, 2020. [more]
Winner of the Bavarian FameLab Contest with the presentation "Teils Cloud, teils Green, mit Aussicht auf Höchstwerte von 8 bis 14 Servern", March, 2020. [read more, see video]
Deutschland Stipendium from the University of Würzburg for outstanding academic achievement, December, 2014. [more]

Curriculum Vitae / Resume

since January 2021	Post-doctoral researcher and group leader at the Chair of Software Engineering (Informatik II) headed by Prof. Samuel Kounev, University of Würzburg
Oct 2016 - Dec 2020	Doctoral reseacher at the Chair of Software-Engineering (Informatik II) headed by Prof. Samuel Kounev, University of Würzburg
Jan 2016 - Sep 2016	Master of Computer Science at the University of Würzburg, Germany
Aug 2015 - Jan 2016	Master of Computer Science at Blekinge Institute of Technology, Karlskrona
Oct 2014 - Aug 2015	Master of Computer Science at the University of Würzburg, Germany
Oct 2012- Sep 2014	Bachelor of Computer Science at the University of Würzburg, Germany

Last Update: June 01, 2021