%0 Conference Paper %1 10.1145/3629527.3651843 %A Bauer, André %A Dittus, Timo %A Straesser, Martin %A Kamatar, Alok %A Baughman, Matt %A Beierlieb, Lukas %A Hadry, Marius %A Grillmeyer, Daniel %A Lubas, Yannik %A Kounev, Samuel %A Foster, Ian %A Chard, Kyle %B Companion of the 15th ACM/SPEC International Conference on Performance Engineering %C New York, NY, USA %D 2024 %I Association for Computing Machinery %K descartes t_short myown %P 72–76 %T Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis %U https://doi.org/10.1145/3629527.3651843 %X 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.}, added-at = {2024-05-10T01:06:30.000+0200}, 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}, biburl = {https://www.bibsonomy.org/bibtex/263516521a294f511d349f030bdb4c07f/timo_dittus}, booktitle = {Companion of the 15th ACM/SPEC International Conference on Performance Engineering}, interhash = {005b4c9849f8dc8a6d217732633a2749}, intrahash = {63516521a294f511d349f030bdb4c07f}, keywords = {descartes t_short myown}, pages = {72–76}, publisher = {Association for Computing Machinery}, series = {ICPE '24 Companion}, timestamp = {2024-05-10T01:06:30.000+0200}, title = {Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis}, url = {https://doi.org/10.1145/3629527.3651843}, year = 2024 }