# AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2305.16092 · HTML: https://mlsanity.com/paper/2305.16092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 309 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 14 of 14)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systems](https://mlsanity.com/paper/2103.01782/) (2021)
- [Robust Failure Diagnosis of Microservice System through Multimodal Data](https://mlsanity.com/paper/2302.10512/) (2023)
- [Microservices: yesterday, today, and tomorrow](https://mlsanity.com/paper/1606.04036/) (2016)
- [Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data](https://mlsanity.com/paper/2302.05092/) (2023)
- [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://mlsanity.com/paper/2304.04661/) (2023)
- [Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions](https://mlsanity.com/paper/2106.12739/) (2021)
- [Machine Learning for Software Engineering: A Tertiary Study](https://mlsanity.com/paper/2211.09425/) (2022)
- [Distributed Redundant Placement for Microservice-based Applications at the Edge](https://mlsanity.com/paper/1911.03600/) (2019)
- [Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN](https://mlsanity.com/paper/2209.02551/) (2022)
- [A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architecture](https://mlsanity.com/paper/1901.04011/) (2019)
- [Detecting Latency Degradation Patterns in Service-based Systems](https://mlsanity.com/paper/2004.05851/) (2020)
- [Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming](https://mlsanity.com/paper/2012.06021/) (2020)

## Built on by (most cited first, 1 of 1)

- [Evaluating Large Language Models for Detecting Architectural Decision Violations](https://mlsanity.com/paper/2602.07609/) (2026)

## 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.