The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Monitoring tools for DevOps and microservices: A systematic grey literature review
Open accessAbstract
Microservice-based systems are usually developed according to agile practices like DevOps, which enables rapid and frequent releases to promptly react and adapt to changes. Monitoring is a key enabler for these systems, as they allow to continuously get feedback from the field and support timely and tailored decisions for a quality-driven evolution. In the realm of monitoring tools available for microservices in the DevOps-driven development practice, each with different features, assumptions, and performance, selecting a suitable tool is an as much difficult as impactful task. This article presents the results of a systematic study of the grey literature we performed to identify, classify and analyze the available monitoring tools for DevOps and microservices. We selected and examined a list of 71 monitoring tools, drawing a map of their characteristics, limitations, assumptions, and open challenges, meant to be useful to both researchers and practitioners working in this area. Results are publicly available and replicable.
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“These solutions are based on fine-grained alerting and require multi-faceted thresholds for Key Performance Indicators (KPIs) in different layers of the application [8].”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [8]
Citations
Cites
Verdecchia, R., Lago, P., Ebert, C., & de Vries, C. (2021) · IEEE Software · doi:10.1109/ms.2021.3102254 · Cited by 78
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Cited by
Sorted by citations
Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs
Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3652304 · Cited by 6
“These solutions are based on fine-grained alerting and require multi-faceted thresholds for Key Performance Indicators (KPIs) in different layers of the application [8].”
Metadata
- Authors
- Giamattei, L., Guerriero, A., Pietrantuono, R., Russo, S., Malavolta, I., Islam, T., Dînga, M., Koziolek, A., Singh, S., Armbruster, M., Gutierrez-Martinez, J. M., Caro-Alvaro, S., Rodriguez, D., Weber, S., Henss, J., Vogelin, E. F., & Panojo, F. S.
- Affiliations
- University of Naples Federico II (IT)
- Vrije Universiteit Amsterdam (NL)
- Karlsruhe Institute of Technology (DE)
- FZI Research Center for Information Technology (DE)
- Corresponding author
- Pietrantuono, R.
- ORCID
- Venue
- Journal of Systems and Software
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 208
- Pages
- 111906
- ISSN
- 0164-1212 (ISSN-L)
- OpenAlex
- W4388914159
- Cited by
- 55
- References
- 1
- Citations by year
- 2026: 19 · 2025: 26 · 2024: 10
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- www.sciencedirect.com
- Licence
- Locations
- Journal of Systems and Software (publishedVersion, cc-by-nc-nd)
- VU Research Portal (publishedVersion, other-oa)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by-nc-nd)
- e_Buah (submittedVersion, cc-by-nc-nd)
- KITopen
- Funders
- H2020 Marie Skłodowska-Curie Actions
- European Commission Marie Sklodowska-Curie Actions (871342)
- Horizon 2020 Framework Programme
- Horizon 2020
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Software › Advanced Software Engineering Methodologies
- Keywords
- monitoring tools, DevOps, microservices, software monitoring, tool selection, tool classification, agile software development, system evolution
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