A Systematic Literature Review of Machine Learning Approaches for Migrating Monolithic Systems to Microservices
Bibliographic record
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 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 bench-marking, emphasizing the need for more holistic solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".