Microservice Applications and Their Workloads on GitHub

Many cloud applications employ a microservice architecture to reap its various benefits, such as independent scalability and development. However, microservices' distributed nature comes with its own challenges, which the literature tries to combat with various approaches in domains, such as autoscaling or service placement. Researchers, especially performance engineers, need representative microservice applications as a basis to develop and evaluate their approaches. While the literature proposes several reference and benchmark applications, the set of commonly used applications is rather limited. Further, the representativeness of reference applications for industry practices has been challenged, raising questions about the applicability of existing approaches. To investigate possible alternatives to and inspirations for microservice testbeds, we conduct an exploratory search of microservice applications and their workloads by mining GitHub repositories. Our work provides two datasets containing 553 applications and 8 workload dataset repositories, respectively. In addition, we provide a first analysis of the collected data that future work can build upon.

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Publication Details

Published
2025-05-03
DOI
https://doi.org/10.1145/3680256.3721321
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
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Microservice Applications and Their Workloads on GitHub

Samuel Kounev, Martin Straesser, André Bauer, Ivo Rohwer et al.
Software System Performance and Reliability
article

Microservice Applications and Their Workloads on GitHub

Samuel Kounev, Martin Straesser, André Bauer, Ivo Rohwer, Yannik Lubas
article en

Abstract

Many cloud applications employ a microservice architecture to reap its various benefits, such as independent scalability and development. However, microservices' distributed nature comes with its own challenges, which the literature tries to combat with various approaches in domains, such as autoscaling or service placement. Researchers, especially performance engineers, need representative microservice applications as a basis to develop and evaluate their approaches. While the literature proposes several reference and benchmark applications, the set of commonly used applications is rather limited. Further, the representativeness of reference applications for industry practices has been challenged, raising questions about the applicability of existing approaches. To investigate possible alternatives to and inspirations for microservice testbeds, we conduct an exploratory search of microservice applications and their workloads by mining GitHub repositories. Our work provides two datasets containing 553 applications and 8 workload dataset repositories, respectively. In addition, we provide a first analysis of the collected data that future work can build upon.

Illinois Institute of Technology (US), University of Würzburg (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 91%
Software System Performance and Reliability
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