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A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network

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Publié dans:Future Internet vol. 17, no. 7 (2025), p. 303-322
Auteur principal: Maharjan Rokin
Autres auteurs: Korn, Sooksatra, Cerny Tomas, Rajbhandari Yudeep, Shrestha Sakshi
Publié:
MDPI AG
Sujets:
Machine learning
Software
Static code analysis
Artificial intelligence
Graphs
Communication
Graph neural networks
Neural networks
Social networks
Decomposition
Architecture
Automation
Case studies
Accès en ligne:Citation/Abstract
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Résumé:Microservice is a popular architecture for developing cloud-native applications. However, decomposing a monolithic application into microservices remains a challenging task. This complexity arises from the need to account for factors such as component dependencies, cohesive clusters, and bounded contexts. To address this challenge, we present an automated approach to decomposing monolithic applications into microservices. Our approach uses static code analysis to generate a dependency graph of the monolithic application. Then, a variational autoencoder (VAE) is used to extract features from the components of a monolithic application. Finally, the C-means algorithm is used to cluster the components into possible microservices. We evaluate our approach using a third-party benchmark comprising both monolithic and microservice implementations. Additionally, we compare its performance against two existing decomposition techniques. The results demonstrate the potential of our method as a practical tool for guiding the transition from monolithic to microservice architectures.
ISSN:1999-5903
DOI:10.3390/fi17070303
Source:ABI/INFORM Global