Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis

Conference · 01 January 2024

DOI:https://doi.org/10.1145/3629527.3651843· OSTI ID:2475168

Bauer, André; Dittus, Timo; Straesser, Martin; Kamatar, Alok; Baughman, Matt; Beierlieb, Lukas; Hadry, Marius; Grillmeyer, Daniel; Lubas, Yannik; Kounev, Samuel; Foster, Ian

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.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: Deutsche Forschungsgemeinschaft (DFG); U.S. Department of Energy (Office not specified); National Science Foundation (NSF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 2475168
• Country of Publication: United States
• Language: English

Related Subjects

Clustering
Data Challenge
Microservices
Performance Analysis
Performance Deviation
Temporal Analysis