Publications - Chair of Computer Science II

Journal and Magazine Articles

Telling Fortunes? Evaluation of Traffic Forecasting Models using Traffic and Context Features. Hadry, Marius; Bauer, André; Leppich, Robert; Lesch, Veronika; Kounev, Samuel; in Applied intelligence (2025). 55(755) Springer.
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@article{hadry2025telling, author = {Hadry, Marius and Bauer, André and Leppich, Robert and Lesch, Veronika and Kounev, Samuel}, journal = {Applied intelligence}, keywords = {t_journalmagazine}, month = {06}, number = 755, publisher = {Springer}, title = {Telling Fortunes? Evaluation of Traffic Forecasting Models using Traffic and Context Features}, volume = 55, year = 2025 }
Self-Aware Optimization of Adaptation Planning Strategies. Lesch, Veronika; Hadry, Marius; Krupitzer, Christian; Kounev, Samuel; in ACM Transactions on Autonomous and Adaptive Systems (2023). 18(3)
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In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

@article{lesch2022selfaware, abstract = {In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.}, author = {Lesch, Veronika and Hadry, Marius and Krupitzer, Christian and Kounev, Samuel}, journal = {ACM Transactions on Autonomous and Adaptive Systems}, keywords = {self-awareness}, month = {09}, number = 3, title = {Self-Aware Optimization of Adaptation Planning Strategies}, volume = 18, year = 2023 }
Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses. Lesch, Veronika; Müller, Patrick B.M.; Krämer, Moritz; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; in Applied intelligence (2023). 25.
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@article{lesch2023optimizing, author = {Lesch, Veronika and Müller, Patrick B.M. and Krämer, Moritz and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, journal = {Applied intelligence}, keywords = {t_journalmagazine}, month = {02}, pages = 25, title = {Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses}, year = 2023 }

Full Papers

Quantifying Data Leakage in Failure Prediction Tasks. Grillmeyer, Daniel; Hadry, Marius; Lesch, Veronika; Borst, Vanessa; Leppich, Robert; Bauer, Andr{é}; Kounev, Samuel; in Proceedings of the 16th ACM/SPEC International Conference on Performance Engineering (2025). 334–346. Association for Computing Machinery, Toronto ON, Canada.
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With the ever increasing importance of cloud computing and a strong focus on reliable data centers, a high amount of research has been done on failure prediction for hard disk drives. The collection of monitoring data, such as SMART statistics (Self-Monitoring, Analysis, and Reporting Technology) from operational HDDs, enables operators to obtain predictions about the expected remaining useful life. Numerous methods for HDD failure prediction have been published in recent years, and their evaluation has shown decent results. However, a naive splitting into training and test sets can lead to data leakage and, thus, over-optimistic results that cannot be achieved on the data of scientific interest. In this paper, we propose a novel data leakage measure for quantifying the amount of data leakage in training and test datasets. Further, we define four splitting techniques and evaluate our measure in terms of the performance optimism of classification models with respect to these different splitting strategies. Our results consistently show that splitting techniques prone to data leakage induce an overestimation of predictive performance. Overall, we were able to show the usefulness of the defined data leakage measure, as well as its connection with different splitting techniques and the performance optimism of prediction models.

@inproceedings{10.1145/3676151.3719368, abstract = {With the ever increasing importance of cloud computing and a strong focus on reliable data centers, a high amount of research has been done on failure prediction for hard disk drives. The collection of monitoring data, such as SMART statistics (Self-Monitoring, Analysis, and Reporting Technology) from operational HDDs, enables operators to obtain predictions about the expected remaining useful life. Numerous methods for HDD failure prediction have been published in recent years, and their evaluation has shown decent results. However, a naive splitting into training and test sets can lead to data leakage and, thus, over-optimistic results that cannot be achieved on the data of scientific interest. In this paper, we propose a novel data leakage measure for quantifying the amount of data leakage in training and test datasets. Further, we define four splitting techniques and evaluate our measure in terms of the performance optimism of classification models with respect to these different splitting strategies. Our results consistently show that splitting techniques prone to data leakage induce an overestimation of predictive performance. Overall, we were able to show the usefulness of the defined data leakage measure, as well as its connection with different splitting techniques and the performance optimism of prediction models.}, address = {New York, NY, USA}, author = {Grillmeyer, Daniel and Hadry, Marius and Lesch, Veronika and Borst, Vanessa and Leppich, Robert and Bauer, Andr\'{e} and Kounev, Samuel}, booktitle = {Proceedings of the 16th ACM/SPEC International Conference on Performance Engineering}, keywords = {descartes}, pages = {334–346}, publisher = {Association for Computing Machinery}, series = {ICPE '25}, title = {Quantifying Data Leakage in Failure Prediction Tasks}, year = 2025 }
Evaluating the Benefits of Model Retraining for Self-Aware Vehicle Traffic Forecasting. Hadry, Marius; Grillmeyer, Daniel; Becker, Martin; König, Maximilian; Lesch, Veronika; Kounev, Samuel; in IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) (2024). 41–50.
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@inproceedings{10771392, author = {Hadry, Marius and Grillmeyer, Daniel and Becker, Martin and König, Maximilian and Lesch, Veronika and Kounev, Samuel}, booktitle = {IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)}, keywords = {descartes}, pages = {41-50}, title = {Evaluating the Benefits of Model Retraining for Self-Aware Vehicle Traffic Forecasting}, year = 2024 }

Short Papers

Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis. Bauer, André; Dittus, Timo; Straesser, Martin; Kamatar, Alok; Baughman, Matt; Beierlieb, Lukas; Hadry, Marius; Grillmeyer, Daniel; Lubas, Yannik; Kounev, Samuel; Foster, Ian; Chard, Kyle; in Companion of the 15th ACM/SPEC International Conference on Performance Engineering (2024). 72–76. Association for Computing Machinery, New York, NY, USA.
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As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.

@inproceedings{10.1145/3629527.3651843, abstract = {As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.}, address = {New York, NY, USA}, author = {Bauer, André and Dittus, Timo and Straesser, Martin and Kamatar, Alok and Baughman, Matt and Beierlieb, Lukas and Hadry, Marius and Grillmeyer, Daniel and Lubas, Yannik and Kounev, Samuel and Foster, Ian and Chard, Kyle}, booktitle = {Companion of the 15th ACM/SPEC International Conference on Performance Engineering}, keywords = {descartes}, pages = {72–76}, publisher = {Association for Computing Machinery}, series = {ICPE '24 Companion}, title = {Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis}, year = 2024 }
Searching for the Ground Truth: Assessing the Similarity of Benchmarking Runs. Bauer, Andr{é}; Straesser, Martin; Leznik, Mark; Beierlieb, Lukas; Hadry, Marius; Hudson, Nathaniel; Chard, Kyle; Kounev, Samuel; Foster, Ian; in Companion of the 2023 ACM/SPEC International Conference on Performance Engineering (2023). 95–99. Association for Computing Machinery, New York, NY, USA.
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Stable and repeatable measurements are essential for comparing the performance of different systems or applications, and benchmarks are used to ensure accuracy and replication. However, if the corresponding measurements are not stable and repeatable, wrong conclusions can be drawn. To facilitate the task of determining whether the measurements are similar, we used a data set of 586 micro-benchmarks to (i) analyze the data set itself, (ii) examine our previous approach, and (iii) propose and evaluate a heuristic. To evaluate the different approaches, we perform a peer review to assess the dissimilarity of the benchmark runs. Our results show that this task is challenging even for humans and that our heuristic exhibits a sensitivity of 92%.

@inproceedings{10.1145/3578245.3584693, abstract = {Stable and repeatable measurements are essential for comparing the performance of different systems or applications, and benchmarks are used to ensure accuracy and replication. However, if the corresponding measurements are not stable and repeatable, wrong conclusions can be drawn. To facilitate the task of determining whether the measurements are similar, we used a data set of 586 micro-benchmarks to (i) analyze the data set itself, (ii) examine our previous approach, and (iii) propose and evaluate a heuristic. To evaluate the different approaches, we perform a peer review to assess the dissimilarity of the benchmark runs. Our results show that this task is challenging even for humans and that our heuristic exhibits a sensitivity of 92%.}, address = {New York, NY, USA}, author = {Bauer, Andr\'{e} and Straesser, Martin and Leznik, Mark and Beierlieb, Lukas and Hadry, Marius and Hudson, Nathaniel and Chard, Kyle and Kounev, Samuel and Foster, Ian}, booktitle = {Companion of the 2023 ACM/SPEC International Conference on Performance Engineering}, keywords = {distance_measures}, pages = {95–99}, publisher = {Association for Computing Machinery}, series = {ICPE '23 Companion}, title = {Searching for the Ground Truth: Assessing the Similarity of Benchmarking Runs}, year = 2023 }

Workshop Papers

Utility-based Vehicle Routing Integrating User Preferences. Lesch, Veronika; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; in Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021 (2021). IEEE.
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@inproceedings{lesch2021utility, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, booktitle = {Proceedings of 3rd International Workshop on Pervasive Computing for Vehicular Systems (PerVehicle), 2021}, keywords = {t_workshop}, month = {03}, publisher = {IEEE}, title = {Utility-based Vehicle Routing Integrating User Preferences}, year = 2021 }

Vision and Position Papers

FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles. Hadry, Marius; Lesch, Veronika; Kounev, Samuel; in Companion of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE 2022) (2022). ACM.
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@inproceedings{hadry2022towards, author = {Hadry, Marius and Lesch, Veronika and Kounev, Samuel}, booktitle = {Companion of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE 2022)}, keywords = {cyber-physical_systems}, month = {04}, publisher = {ACM}, title = {FADE: Towards Flexible and Adaptive Distance Estimation Considering Obstacles}, year = 2022 }

Poster Papers

Tutorial Papers

Talks

Techreports

A Case Study on Optimization of Platooning Coordination Lesch, Veronika; Hadry, Marius; Kounev, Samuel; Krupitzer, Christian; (2021). Universit{ä}t W{ü}rzburg and Universit{ä}t Hohenheim.
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@techreport{lesch2021studyplatooning, author = {Lesch, Veronika and Hadry, Marius and Kounev, Samuel and Krupitzer, Christian}, institution = {Universit{ä}t W{ü}rzburg and Universit{ä}t Hohenheim}, keywords = {t_techreport}, title = {A Case Study on Optimization of Platooning Coordination}, year = 2021 }

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