A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices

2025-08-21

AI Summary

This study addresses the lack of a systematic review on machine learning (ML) method selection and application in monolith-to-microservices migration. We conducted a rigorous systematic literature review (SLR) of 81 studies published between 2015 and 2024, adhering to the PRISMA guidelines. Our primary contribution is the first four-dimensional classification framework—encompassing migration phases, input data types, ML techniques, and evaluation practices—which reveals that stages such as monitoring and service identification have reached initial maturity, whereas critical phases like microservice packaging remain severely underexplored. We identify three core challenges: data scarcity, poor scalability of proposed approaches, and the absence of standardized benchmarks. The framework establishes a unified analytical paradigm for the field and provides concrete directions for future research and practical implementation of ML-driven migration strategies.

Technology Category

• Machine Learning: Scalability of ML Systems
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Machine learning and data science for the Web
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

Scalability and maintainability challenges in monolithic systems have led to the adoption of microservices, which divide systems into smaller, independent services. However, migrating existing monolithic systems to microservices is a complex and resource-intensive task, which can benefit from machine learning (ML) to automate some of its phases. Choosing the right ML approach for migration remains challenging for practitioners. Previous works studied separately the objectives, artifacts, techniques, tools, and benefits and challenges of migrating monolithic systems to microservices. No work has yet investigated systematically existing ML approaches for this migration to understand the evised{automated migration phases}, inputs used, ML techniques applied, evaluation processes followed, and challenges encountered. We present a systematic literature review (SLR) that aggregates, synthesises, and discusses the approaches and results of 81 primary studies (PSs) published between 2015 and 2024. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to report our findings and answer our research questions (RQs). We extract and analyse data from these PSs to answer our RQs. We synthesise the findings in the form of a classification that shows the usage of ML techniques in migrating monolithic systems to microservices. The findings reveal that some phases of the migration process, such as monitoring and service identification, are well-studied, while others, like packaging microservices, remain unexplored. Additionally, the findings highlight key challenges, including limited data availability, scalability and complexity constraints, insufficient tool support, and the absence of standardized benchmarking, emphasizing the need for more holistic solutions.

Problem

• Systematically reviewing ML approaches for monolithic to microservices migration
• Identifying automated migration phases, techniques, and evaluation processes used
• Addressing challenges like data scarcity and lack of standardized benchmarking

Innovation

• Systematic literature review of ML migration approaches
• Classification framework for ML techniques application
• Identified gaps in automated migration phases