AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study
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Abstract (arXiv)
The use of AI in microservices (MSs) is an emerging field as indicated by a substantial number of surveys. However these surveys focus on a specific problem using specific AI techniques, therefore not fully capturing the growth of research and the rise and disappearance of trends. In our systematic mapping study, we take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases. Our results include 16 research themes that connect to the intersection of particular QAs, AI domains and DevOps phases. Moreover by mapping identified future research challenges and relevant industry domains, we can show that many studies aim to deliver prototypes to be automated at a later stage, aiming at providing exploitable products in a number of key industry domains.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 309 references that are in the map. First posted on arXiv 25 May 2023.
Builds on
14 of 309 references.
References · 14
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2020FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservicescited by 41
- 2021MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systemscited by 36
- 2023Robust Failure Diagnosis of Microservice System through Multimodal Datacited by 23
- 2016Microservices: yesterday, today, and tomorrowcited by 22
- 2023Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Datacited by 19
- 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
- 2021Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directionscited by 7
- 2022Machine Learning for Software Engineering: A Tertiary Studycited by 7
- 2019Distributed Redundant Placement for Microservice-based Applications at the Edgecited by 4
- 2022Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNNcited by 3
- 2019A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architecturecited by 2
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Citing papers per year: 2026: 1.
1 paper.