Intern

Chair of Computer Science II - Software Engineering

Publications

Journal and Magazine Articles

Full Papers

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.
- [ BibTeX ]
- [ DOI ]
@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.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ Download ]
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 }

Workshop Papers

Vision and Position Papers

Poster Papers

Tutorial Papers

Talks

Techreports