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DOE OSTI · 2475168

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
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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.

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ClusteringData ChallengeMicroservicesPerformance AnalysisPerformance DeviationTemporal Analysis

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Bauer, André, Dittus, Timo, Straesser, Martin, Kamatar, Alok, Baughman, Matt, Beierlieb, Lukas, Hadry, Marius, Grillmeyer, Daniel, Lubas, Yannik, Kounev, Samuel, Foster, Ian. 2024-01-01. Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis. https://doi.org/10.1145/3629527.3651843

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